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252a29e5e6* Reapply span attributes after creation to survive non-forwarding samplers Tracer.start_span builds the span from sampling_result.attributes, not the attributes kwarg — a custom Sampler that returns SamplingResult(RECORD_AND_SAMPLE) without forwarding attributes silently drops everything FastMCP passed at creation time. Reapply the same attributes immediately after span creation (guarded by is_recording()) so on_start hooks and samplers still see them, while the finished span is guaranteed to carry FastMCP's telemetry regardless of sampler behavior. * Restore only missing span attributes, not a blanket reapply Reapplying all attributes after span creation overwrote values a sampler deliberately set (e.g. a redacted mcp.method.name) and inflated dropped-attribute counts when the SDK's attribute limit was hit. Compare against the span's existing attributes and restore only the keys a non-forwarding sampler actually dropped, via a shared restore_missing_attributes() helper in fastmcp.telemetry. * Gate attribute restore on all-or-nothing, not per-key Restoring only missing keys reinserted attributes the SDK's bounded attribute map had already evicted under a low OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT, evicting a different retained key and inflating dropped_attributes beyond what the sampler actually dropped. Gate on none of our attributes being present (plus dropped_attributes == 0) instead — the regression this exists to fix is a sampler dropping everything, and eviction under a limit always leaves some. Renamed restore_missing_attributes to restore_dropped_attributes to match. * Gate attribute restore on empty span, not per-key presence A sampler that intentionally supplies only its own attributes (e.g. to strip component names or resource URIs for privacy/cardinality control) left none of FastMCP's keys on the span, so the previous all-or-nothing gate treated it identically to a bare non-forwarding sampler and restored everything, defeating the filter. Key off the span having no attributes at all instead — a bare sampler leaves it empty, a filtering sampler doesn't.
863 lines
32 KiB
Python
863 lines
32 KiB
Python
"""Tracing coverage for sampling create_message and tool-execution spans.
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Regression focus: the `sampling create_message` span is created with
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`record_exception=False, set_status_on_exception=False` and records the
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exception manually in its `except` block. A failed sampling call must
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therefore produce exactly ONE exception event, not two.
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"""
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from __future__ import annotations
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import pytest
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from mcp_types import TextContent
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from opentelemetry.context import Context as OTelContext
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from opentelemetry.sdk.trace import (
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ReadableSpan,
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Span,
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SpanLimits,
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SpanProcessor,
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TracerProvider,
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)
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from opentelemetry.sdk.trace.export import SimpleSpanProcessor
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from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
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from opentelemetry.sdk.trace.sampling import Decision, Sampler, SamplingResult
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from opentelemetry.trace import StatusCode
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from opentelemetry.util import types as otel_types
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from fastmcp import Client, Context, FastMCP
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from fastmcp.client.sampling import RequestContext, SamplingMessage, SamplingParams
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class OnStartRecorder(SpanProcessor):
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def __init__(self) -> None:
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self.attributes: dict[str, dict[str, object]] = {}
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def on_start(self, span: Span, parent_context: OTelContext | None = None) -> None:
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self.attributes[span.name] = dict(span.attributes or {})
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class NonForwardingSampler(Sampler):
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"""Samples every span but never forwards the attributes it was handed.
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See `tests/telemetry/test_span_attributes.py` for the full explanation:
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OTel's `Tracer.start_span` builds the finished span from
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`sampling_result.attributes`, not from the `attributes` kwarg passed to
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`start_as_current_span`, so a custom sampler like this one reproduces the
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regression where a non-forwarding sampler silently drops FastMCP's
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attributes.
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"""
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def should_sample(
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self,
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parent_context: OTelContext | None,
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trace_id: int,
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name: str,
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kind: object = None,
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attributes: object = None,
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links: object = None,
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trace_state: object = None,
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) -> SamplingResult:
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return SamplingResult(Decision.RECORD_AND_SAMPLE)
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def get_description(self) -> str:
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return "NonForwardingSampler"
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class RedactingSampler(Sampler):
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"""Forwards the attributes it receives, but replaces `mcp.method.name`.
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See `tests/telemetry/test_span_attributes.py` for the full explanation:
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a sampler can legitimately keep an attribute FastMCP set while replacing
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its value (e.g. redacting the method name for privacy), and that decision
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must survive the restore step.
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"""
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def should_sample(
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self,
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parent_context: OTelContext | None,
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trace_id: int,
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name: str,
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kind: object = None,
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attributes: otel_types.Attributes = None,
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links: object = None,
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trace_state: object = None,
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) -> SamplingResult:
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forwarded = dict(attributes or {})
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forwarded["mcp.method.name"] = "REDACTED"
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return SamplingResult(Decision.RECORD_AND_SAMPLE, attributes=forwarded)
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def get_description(self) -> str:
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return "RedactingSampler"
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class AttributeAddingSampler(Sampler):
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"""Forwards the attributes it receives unchanged and adds its own."""
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def should_sample(
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self,
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parent_context: OTelContext | None,
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trace_id: int,
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name: str,
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kind: object = None,
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attributes: otel_types.Attributes = None,
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links: object = None,
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trace_state: object = None,
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) -> SamplingResult:
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forwarded = dict(attributes or {})
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forwarded["sampling.policy"] = "always_on"
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return SamplingResult(Decision.RECORD_AND_SAMPLE, attributes=forwarded)
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def get_description(self) -> str:
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return "AttributeAddingSampler"
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class FilteringSampler(Sampler):
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"""Discards every attribute it receives and substitutes its own.
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See `tests/telemetry/test_span_attributes.py` for the full explanation:
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a sampler can legitimately supply only its own attributes (e.g. to strip
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component names or resource URIs for privacy or cardinality control),
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and the restore step must not reintroduce FastMCP's attributes in that
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case — doing so would defeat the filter.
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"""
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def should_sample(
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self,
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parent_context: OTelContext | None,
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trace_id: int,
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name: str,
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kind: object = None,
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attributes: otel_types.Attributes = None,
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links: object = None,
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trace_state: object = None,
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) -> SamplingResult:
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return SamplingResult(
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Decision.RECORD_AND_SAMPLE, attributes={"sampling.policy": "filtered"}
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)
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def get_description(self) -> str:
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return "FilteringSampler"
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@pytest.fixture
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def on_start_recorder(
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monkeypatch: pytest.MonkeyPatch,
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trace_exporter: InMemorySpanExporter,
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) -> OnStartRecorder:
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recorder = OnStartRecorder()
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provider = TracerProvider()
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provider.add_span_processor(recorder)
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provider.add_span_processor(SimpleSpanProcessor(trace_exporter))
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tracer = provider.get_tracer("test")
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monkeypatch.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
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return recorder
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def _spans_named(exporter: InMemorySpanExporter, name: str):
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return [s for s in exporter.get_finished_spans() if s.name == name]
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def _exception_events(span):
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return [e for e in span.events if e.name == "exception"]
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class TestSamplingCreateMessageSpan:
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async def test_success_creates_span_with_attributes(
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self,
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trace_exporter: InMemorySpanExporter,
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on_start_recorder: OnStartRecorder,
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):
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def sampling_handler(
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messages: list[SamplingMessage],
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params: SamplingParams,
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ctx: RequestContext,
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) -> str:
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return "sampled-text"
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mcp = FastMCP("sampling-server")
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@mcp.tool
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async def ask(question: str, context: Context) -> str:
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result = await context.sample(messages=question)
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return result.text or ""
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async with Client(mcp, sampling_handler=sampling_handler) as client:
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await client.call_tool("ask", {"question": "hi"})
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spans = _spans_named(trace_exporter, "sampling create_message")
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assert len(spans) == 1
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span = spans[0]
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assert span.attributes is not None
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assert span.attributes["mcp.method.name"] == "sampling/createMessage"
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assert span.attributes["fastmcp.server.name"] == "sampling-server"
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assert on_start_recorder.attributes["sampling create_message"] == {
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"mcp.method.name": "sampling/createMessage",
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"fastmcp.server.name": "sampling-server",
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}
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# Success path must not record any exception.
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assert _exception_events(span) == []
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assert span.status.status_code != StatusCode.ERROR
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async def test_failure_records_exception_exactly_once(
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self, trace_exporter: InMemorySpanExporter
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):
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"""Regression: span created with record_exception=False so the manual
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record_exception in the except block fires exactly once (no duplicate
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exception events from OTel auto-recording on `with` exit)."""
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def sampling_handler(
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messages: list[SamplingMessage],
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params: SamplingParams,
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ctx: RequestContext,
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) -> str:
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raise RuntimeError("sampling boom")
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mcp = FastMCP("sampling-server")
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@mcp.tool
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async def ask(question: str, context: Context) -> str:
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result = await context.sample(messages=question)
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return result.text or ""
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with pytest.raises(Exception):
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async with Client(mcp, sampling_handler=sampling_handler) as client:
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await client.call_tool("ask", {"question": "hi"})
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spans = _spans_named(trace_exporter, "sampling create_message")
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assert len(spans) == 1
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span = spans[0]
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assert span.status.status_code == StatusCode.ERROR
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assert span.attributes is not None
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assert "error.type" in span.attributes
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# The whole point of the fix: exactly one exception event.
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assert len(_exception_events(span)) == 1
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class TestSamplingToolSpan:
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async def test_tool_error_span_records_exception_once(
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self,
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trace_exporter: InMemorySpanExporter,
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on_start_recorder: OnStartRecorder,
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):
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from mcp_types import CreateMessageResultWithTools, ToolUseContent
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call_count = 0
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def boom_tool() -> str:
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raise ValueError("tool exploded")
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def sampling_handler(
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messages: list[SamplingMessage],
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params: SamplingParams,
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ctx: RequestContext,
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) -> CreateMessageResultWithTools:
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nonlocal call_count
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call_count += 1
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if call_count == 1:
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return CreateMessageResultWithTools(
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role="assistant",
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content=[
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ToolUseContent(
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type="tool_use",
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id="call_1",
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name="boom_tool",
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input={},
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)
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],
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model="test-model",
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stop_reason="toolUse",
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)
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return CreateMessageResultWithTools(
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role="assistant",
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content=[TextContent(type="text", text="done")],
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model="test-model",
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stop_reason="endTurn",
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)
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mcp = FastMCP(sampling_handler=sampling_handler)
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@mcp.tool
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async def driver(context: Context) -> str:
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result = await context.sample(messages="go", tools=[boom_tool])
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return result.text or ""
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async with Client(mcp) as client:
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await client.call_tool("driver", {})
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spans = _spans_named(trace_exporter, "sampling tool boom_tool")
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assert len(spans) == 1
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span = spans[0]
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assert span.status.status_code == StatusCode.ERROR
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assert span.attributes is not None
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assert span.attributes["gen_ai.tool.name"] == "boom_tool"
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assert on_start_recorder.attributes["sampling tool boom_tool"] == {
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"gen_ai.tool.name": "boom_tool",
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"fastmcp.tool.use_id": "call_1",
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}
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assert "error.type" in span.attributes
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# Tool spans catch-and-convert (no re-raise), so OTel auto-recording
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# never fires; the manual record_exception must fire exactly once.
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assert len(_exception_events(span)) == 1
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class TestAttributesSurviveANonForwardingSampler:
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"""Regression: `sampling create_message` and `sampling tool ...` spans
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must keep FastMCP's attributes even when the configured Sampler doesn't
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forward the `attributes` it was handed to its `SamplingResult`.
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"""
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@pytest.fixture
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def non_forwarding_recorder(
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self,
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monkeypatch: pytest.MonkeyPatch,
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trace_exporter: InMemorySpanExporter,
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) -> OnStartRecorder:
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recorder = OnStartRecorder()
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provider = TracerProvider(sampler=NonForwardingSampler())
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provider.add_span_processor(recorder)
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provider.add_span_processor(SimpleSpanProcessor(trace_exporter))
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tracer = provider.get_tracer("test")
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monkeypatch.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
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return recorder
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async def test_create_message_span_keeps_attributes(
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self,
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trace_exporter: InMemorySpanExporter,
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non_forwarding_recorder: OnStartRecorder,
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):
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def sampling_handler(
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messages: list[SamplingMessage],
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params: SamplingParams,
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ctx: RequestContext,
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) -> str:
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return "sampled-text"
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mcp = FastMCP("sampling-server")
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@mcp.tool
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async def ask(question: str, context: Context) -> str:
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result = await context.sample(messages=question)
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return result.text or ""
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async with Client(mcp, sampling_handler=sampling_handler) as client:
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await client.call_tool("ask", {"question": "hi"})
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spans = _spans_named(trace_exporter, "sampling create_message")
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assert len(spans) == 1
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span = spans[0]
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assert span.attributes is not None
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assert span.attributes["mcp.method.name"] == "sampling/createMessage"
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assert span.attributes["fastmcp.server.name"] == "sampling-server"
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# The sampler never forwards attributes, so on_start legitimately sees
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# none — this documents that limitation rather than asserting around it.
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assert non_forwarding_recorder.attributes["sampling create_message"] == {}
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async def test_sampling_tool_span_keeps_attributes(
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self,
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trace_exporter: InMemorySpanExporter,
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non_forwarding_recorder: OnStartRecorder,
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):
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from mcp_types import CreateMessageResultWithTools, ToolUseContent
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def echo_tool(text: str) -> str:
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return text
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call_count = 0
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def sampling_handler(
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messages: list[SamplingMessage],
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params: SamplingParams,
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ctx: RequestContext,
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) -> CreateMessageResultWithTools:
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nonlocal call_count
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call_count += 1
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if call_count == 1:
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return CreateMessageResultWithTools(
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role="assistant",
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content=[
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ToolUseContent(
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type="tool_use",
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id="call_1",
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name="echo_tool",
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input={"text": "hi"},
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)
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],
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model="test-model",
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stop_reason="toolUse",
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)
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return CreateMessageResultWithTools(
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role="assistant",
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content=[TextContent(type="text", text="done")],
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model="test-model",
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stop_reason="endTurn",
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)
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mcp = FastMCP(sampling_handler=sampling_handler)
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@mcp.tool
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async def driver(context: Context) -> str:
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result = await context.sample(messages="go", tools=[echo_tool])
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return result.text or ""
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async with Client(mcp) as client:
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await client.call_tool("driver", {})
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spans = _spans_named(trace_exporter, "sampling tool echo_tool")
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assert len(spans) == 1
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span = spans[0]
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assert span.attributes is not None
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assert span.attributes["gen_ai.tool.name"] == "echo_tool"
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assert span.attributes["fastmcp.tool.use_id"] == "call_1"
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# The sampler never forwards attributes, so on_start legitimately sees
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# none — this documents that limitation rather than asserting around it.
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assert non_forwarding_recorder.attributes["sampling tool echo_tool"] == {}
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class TestAttributeRestoreRespectsSampler:
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"""Restoring missing attributes must not clobber attributes a sampler
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deliberately kept and modified, must coexist with attributes a sampler
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adds of its own, and must not fire at all when a sampler supplies only
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its own attributes and drops FastMCP's entirely."""
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@pytest.fixture
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def redacting_tracer(
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self, monkeypatch: pytest.MonkeyPatch, trace_exporter: InMemorySpanExporter
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) -> None:
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provider = TracerProvider(sampler=RedactingSampler())
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provider.add_span_processor(SimpleSpanProcessor(trace_exporter))
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tracer = provider.get_tracer("test")
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monkeypatch.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
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@pytest.fixture
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def attribute_adding_tracer(
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self, monkeypatch: pytest.MonkeyPatch, trace_exporter: InMemorySpanExporter
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) -> None:
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provider = TracerProvider(sampler=AttributeAddingSampler())
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provider.add_span_processor(SimpleSpanProcessor(trace_exporter))
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tracer = provider.get_tracer("test")
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monkeypatch.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
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@pytest.fixture
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def filtering_tracer(
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self, monkeypatch: pytest.MonkeyPatch, trace_exporter: InMemorySpanExporter
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) -> None:
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provider = TracerProvider(sampler=FilteringSampler())
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provider.add_span_processor(SimpleSpanProcessor(trace_exporter))
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tracer = provider.get_tracer("test")
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monkeypatch.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
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async def test_create_message_span_keeps_redacted_value(
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self,
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trace_exporter: InMemorySpanExporter,
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redacting_tracer: None,
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):
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def sampling_handler(
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messages: list[SamplingMessage],
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params: SamplingParams,
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ctx: RequestContext,
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) -> str:
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return "sampled-text"
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mcp = FastMCP("sampling-server")
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@mcp.tool
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async def ask(question: str, context: Context) -> str:
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result = await context.sample(messages=question)
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return result.text or ""
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async with Client(mcp, sampling_handler=sampling_handler) as client:
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await client.call_tool("ask", {"question": "hi"})
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spans = _spans_named(trace_exporter, "sampling create_message")
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assert len(spans) == 1
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span = spans[0]
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assert span.attributes is not None
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# The redaction survives — it must not be overwritten by a restore.
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assert span.attributes["mcp.method.name"] == "REDACTED"
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# Attributes the sampler didn't touch are still present.
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assert span.attributes["fastmcp.server.name"] == "sampling-server"
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|
|
async def test_create_message_span_keeps_sampler_added_attribute(
|
|
self,
|
|
trace_exporter: InMemorySpanExporter,
|
|
attribute_adding_tracer: None,
|
|
):
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> str:
|
|
return "sampled-text"
|
|
|
|
mcp = FastMCP("sampling-server")
|
|
|
|
@mcp.tool
|
|
async def ask(question: str, context: Context) -> str:
|
|
result = await context.sample(messages=question)
|
|
return result.text or ""
|
|
|
|
async with Client(mcp, sampling_handler=sampling_handler) as client:
|
|
await client.call_tool("ask", {"question": "hi"})
|
|
|
|
spans = _spans_named(trace_exporter, "sampling create_message")
|
|
assert len(spans) == 1
|
|
span = spans[0]
|
|
assert span.attributes is not None
|
|
assert span.attributes["sampling.policy"] == "always_on"
|
|
assert span.attributes["mcp.method.name"] == "sampling/createMessage"
|
|
assert span.attributes["fastmcp.server.name"] == "sampling-server"
|
|
|
|
async def test_tool_span_keeps_redacted_value(
|
|
self,
|
|
trace_exporter: InMemorySpanExporter,
|
|
redacting_tracer: None,
|
|
):
|
|
from mcp_types import CreateMessageResultWithTools, ToolUseContent
|
|
|
|
def echo_tool(text: str) -> str:
|
|
return text
|
|
|
|
call_count = 0
|
|
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> CreateMessageResultWithTools:
|
|
nonlocal call_count
|
|
call_count += 1
|
|
if call_count == 1:
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[
|
|
ToolUseContent(
|
|
type="tool_use",
|
|
id="call_1",
|
|
name="echo_tool",
|
|
input={"text": "hi"},
|
|
)
|
|
],
|
|
model="test-model",
|
|
stop_reason="toolUse",
|
|
)
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[TextContent(type="text", text="done")],
|
|
model="test-model",
|
|
stop_reason="endTurn",
|
|
)
|
|
|
|
mcp = FastMCP(sampling_handler=sampling_handler)
|
|
|
|
@mcp.tool
|
|
async def driver(context: Context) -> str:
|
|
result = await context.sample(messages="go", tools=[echo_tool])
|
|
return result.text or ""
|
|
|
|
async with Client(mcp) as client:
|
|
await client.call_tool("driver", {})
|
|
|
|
spans = _spans_named(trace_exporter, "sampling tool echo_tool")
|
|
assert len(spans) == 1
|
|
span = spans[0]
|
|
assert span.attributes is not None
|
|
# RedactingSampler only touches mcp.method.name, which this span
|
|
# doesn't set — its attributes are forwarded unchanged, confirming
|
|
# the restore step doesn't disturb them either.
|
|
assert span.attributes["gen_ai.tool.name"] == "echo_tool"
|
|
assert span.attributes["fastmcp.tool.use_id"] == "call_1"
|
|
|
|
async def test_tool_span_keeps_sampler_added_attribute(
|
|
self,
|
|
trace_exporter: InMemorySpanExporter,
|
|
attribute_adding_tracer: None,
|
|
):
|
|
from mcp_types import CreateMessageResultWithTools, ToolUseContent
|
|
|
|
def echo_tool(text: str) -> str:
|
|
return text
|
|
|
|
call_count = 0
|
|
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> CreateMessageResultWithTools:
|
|
nonlocal call_count
|
|
call_count += 1
|
|
if call_count == 1:
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[
|
|
ToolUseContent(
|
|
type="tool_use",
|
|
id="call_1",
|
|
name="echo_tool",
|
|
input={"text": "hi"},
|
|
)
|
|
],
|
|
model="test-model",
|
|
stop_reason="toolUse",
|
|
)
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[TextContent(type="text", text="done")],
|
|
model="test-model",
|
|
stop_reason="endTurn",
|
|
)
|
|
|
|
mcp = FastMCP(sampling_handler=sampling_handler)
|
|
|
|
@mcp.tool
|
|
async def driver(context: Context) -> str:
|
|
result = await context.sample(messages="go", tools=[echo_tool])
|
|
return result.text or ""
|
|
|
|
async with Client(mcp) as client:
|
|
await client.call_tool("driver", {})
|
|
|
|
spans = _spans_named(trace_exporter, "sampling tool echo_tool")
|
|
assert len(spans) == 1
|
|
span = spans[0]
|
|
assert span.attributes is not None
|
|
assert span.attributes["sampling.policy"] == "always_on"
|
|
assert span.attributes["gen_ai.tool.name"] == "echo_tool"
|
|
assert span.attributes["fastmcp.tool.use_id"] == "call_1"
|
|
|
|
async def test_create_message_span_filtered_attributes_are_not_restored(
|
|
self,
|
|
trace_exporter: InMemorySpanExporter,
|
|
filtering_tracer: None,
|
|
):
|
|
"""Regression: a sampler that intentionally supplies only its own
|
|
attributes must not have FastMCP's attributes restored on top."""
|
|
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> str:
|
|
return "sampled-text"
|
|
|
|
mcp = FastMCP("sampling-server")
|
|
|
|
@mcp.tool
|
|
async def ask(question: str, context: Context) -> str:
|
|
result = await context.sample(messages=question)
|
|
return result.text or ""
|
|
|
|
async with Client(mcp, sampling_handler=sampling_handler) as client:
|
|
await client.call_tool("ask", {"question": "hi"})
|
|
|
|
spans = _spans_named(trace_exporter, "sampling create_message")
|
|
assert len(spans) == 1
|
|
span = spans[0]
|
|
assert dict(span.attributes or {}) == {"sampling.policy": "filtered"}
|
|
|
|
async def test_tool_span_filtered_attributes_are_not_restored(
|
|
self,
|
|
trace_exporter: InMemorySpanExporter,
|
|
filtering_tracer: None,
|
|
):
|
|
"""Regression: a sampler that intentionally supplies only its own
|
|
attributes must not have FastMCP's attributes restored on top."""
|
|
from mcp_types import CreateMessageResultWithTools, ToolUseContent
|
|
|
|
def echo_tool(text: str) -> str:
|
|
return text
|
|
|
|
call_count = 0
|
|
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> CreateMessageResultWithTools:
|
|
nonlocal call_count
|
|
call_count += 1
|
|
if call_count == 1:
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[
|
|
ToolUseContent(
|
|
type="tool_use",
|
|
id="call_1",
|
|
name="echo_tool",
|
|
input={"text": "hi"},
|
|
)
|
|
],
|
|
model="test-model",
|
|
stop_reason="toolUse",
|
|
)
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[TextContent(type="text", text="done")],
|
|
model="test-model",
|
|
stop_reason="endTurn",
|
|
)
|
|
|
|
mcp = FastMCP(sampling_handler=sampling_handler)
|
|
|
|
@mcp.tool
|
|
async def driver(context: Context) -> str:
|
|
result = await context.sample(messages="go", tools=[echo_tool])
|
|
return result.text or ""
|
|
|
|
async with Client(mcp) as client:
|
|
await client.call_tool("driver", {})
|
|
|
|
spans = _spans_named(trace_exporter, "sampling tool echo_tool")
|
|
assert len(spans) == 1
|
|
span = spans[0]
|
|
assert dict(span.attributes or {}) == {"sampling.policy": "filtered"}
|
|
|
|
|
|
class TestRestoreDoesNotChurnAttributeLimitEvictions:
|
|
"""Regression: under a low `OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT`, the restore
|
|
step in `fastmcp.server.sampling.run` must not reinsert an attribute the
|
|
SDK's bounded attribute map already evicted.
|
|
|
|
See `tests/telemetry/test_span_attributes.py::
|
|
test_restore_does_not_churn_sdk_attribute_limit_evictions` for the full
|
|
explanation: an evicted key looks identical to a sampler-omitted one from
|
|
inside the restore helper, so reinserting it churns which attributes the
|
|
SDK ultimately retains and inflates `dropped_attributes` beyond what the
|
|
limit alone already cost. This proves the same discriminator holds
|
|
end-to-end through the real sampling call path, not just at the helper
|
|
level.
|
|
"""
|
|
|
|
@staticmethod
|
|
async def _run_create_message(
|
|
span_limits: SpanLimits, *, stub_restore: bool
|
|
) -> ReadableSpan:
|
|
with pytest.MonkeyPatch.context() as mp:
|
|
exporter = InMemorySpanExporter()
|
|
provider = TracerProvider(span_limits=span_limits)
|
|
provider.add_span_processor(SimpleSpanProcessor(exporter))
|
|
tracer = provider.get_tracer("test")
|
|
mp.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
|
|
if stub_restore:
|
|
mp.setattr(
|
|
"fastmcp.server.sampling.run.restore_dropped_attributes",
|
|
lambda span, attrs: None,
|
|
)
|
|
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> str:
|
|
return "sampled-text"
|
|
|
|
mcp = FastMCP("sampling-server")
|
|
|
|
@mcp.tool
|
|
async def ask(question: str, context: Context) -> str:
|
|
result = await context.sample(messages=question)
|
|
return result.text or ""
|
|
|
|
async with Client(mcp, sampling_handler=sampling_handler) as client:
|
|
await client.call_tool("ask", {"question": "hi"})
|
|
|
|
spans = _spans_named(exporter, "sampling create_message")
|
|
assert len(spans) == 1
|
|
return spans[0]
|
|
|
|
@staticmethod
|
|
async def _run_tool_span(
|
|
span_limits: SpanLimits, *, stub_restore: bool
|
|
) -> ReadableSpan:
|
|
from mcp_types import CreateMessageResultWithTools, ToolUseContent
|
|
|
|
with pytest.MonkeyPatch.context() as mp:
|
|
exporter = InMemorySpanExporter()
|
|
provider = TracerProvider(span_limits=span_limits)
|
|
provider.add_span_processor(SimpleSpanProcessor(exporter))
|
|
tracer = provider.get_tracer("test")
|
|
mp.setattr("fastmcp.server.sampling.run.get_tracer", lambda: tracer)
|
|
if stub_restore:
|
|
mp.setattr(
|
|
"fastmcp.server.sampling.run.restore_dropped_attributes",
|
|
lambda span, attrs: None,
|
|
)
|
|
|
|
def echo_tool(text: str) -> str:
|
|
return text
|
|
|
|
call_count = 0
|
|
|
|
def sampling_handler(
|
|
messages: list[SamplingMessage],
|
|
params: SamplingParams,
|
|
ctx: RequestContext,
|
|
) -> CreateMessageResultWithTools:
|
|
nonlocal call_count
|
|
call_count += 1
|
|
if call_count == 1:
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[
|
|
ToolUseContent(
|
|
type="tool_use",
|
|
id="call_1",
|
|
name="echo_tool",
|
|
input={"text": "hi"},
|
|
)
|
|
],
|
|
model="test-model",
|
|
stop_reason="toolUse",
|
|
)
|
|
return CreateMessageResultWithTools(
|
|
role="assistant",
|
|
content=[TextContent(type="text", text="done")],
|
|
model="test-model",
|
|
stop_reason="endTurn",
|
|
)
|
|
|
|
mcp = FastMCP(sampling_handler=sampling_handler)
|
|
|
|
@mcp.tool
|
|
async def driver(context: Context) -> str:
|
|
result = await context.sample(messages="go", tools=[echo_tool])
|
|
return result.text or ""
|
|
|
|
async with Client(mcp) as client:
|
|
await client.call_tool("driver", {})
|
|
|
|
spans = _spans_named(exporter, "sampling tool echo_tool")
|
|
assert len(spans) == 1
|
|
return spans[0]
|
|
|
|
async def test_create_message_span(self, monkeypatch: pytest.MonkeyPatch):
|
|
# Two attributes on this span (`mcp.method.name`, `fastmcp.server.name`)
|
|
# — a limit of 1 guarantees eviction.
|
|
monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "1")
|
|
span_limits = SpanLimits()
|
|
assert span_limits.max_span_attributes == 1
|
|
|
|
baseline = await self._run_create_message(span_limits, stub_restore=True)
|
|
# The whole test is moot if the limit didn't actually bind.
|
|
assert baseline.dropped_attributes > 0
|
|
|
|
with_restore = await self._run_create_message(span_limits, stub_restore=False)
|
|
|
|
assert dict(with_restore.attributes or {}) == dict(baseline.attributes or {})
|
|
assert with_restore.dropped_attributes == baseline.dropped_attributes
|
|
|
|
async def test_tool_span(self, monkeypatch: pytest.MonkeyPatch):
|
|
# Two attributes on this span (`gen_ai.tool.name`, `fastmcp.tool.use_id`)
|
|
# — a limit of 1 guarantees eviction.
|
|
monkeypatch.setenv("OTEL_SPAN_ATTRIBUTE_COUNT_LIMIT", "1")
|
|
span_limits = SpanLimits()
|
|
assert span_limits.max_span_attributes == 1
|
|
|
|
baseline = await self._run_tool_span(span_limits, stub_restore=True)
|
|
# The whole test is moot if the limit didn't actually bind.
|
|
assert baseline.dropped_attributes > 0
|
|
|
|
with_restore = await self._run_tool_span(span_limits, stub_restore=False)
|
|
|
|
assert dict(with_restore.attributes or {}) == dict(baseline.attributes or {})
|
|
assert with_restore.dropped_attributes == baseline.dropped_attributes
|