fastmcp/tests/downstream

downstream smoke

Checks that FastMCP still works the way its consumers use it. The workflow builds FastMCP's wheels once, installs them where each consumer runs, and drives the consumer against servers started from the checkout.

job	what it runs	FastMCP install
pydantic-ai test suite	pydantic-ai's own MCP tests at a pinned tag	wheels overlaid on pydantic-ai's locked environment, as their FastMCP 4 job does
langchain test suite	langchain.mcp's own unit and integration tests at a pinned tag	wheels overlaid on langchain's locked test environment
pydantic-ai smoke	smoke_pydantic_ai.py	client-only fastmcp-slim[client], as pydantic-ai-slim[mcp] installs it
langchain.mcp smoke	smoke_langchain_mcp.py, including a ClientGroup of a legacy HTTP server and a modern stdio server	fastmcp, as langchain[mcp] installs it
langchain-mcp-adapters smoke	smoke_langchain_mcp_adapters.py	none: the adapters pin mcp<2, so they reach FastMCP over the wire
redis credential providers smoke	smoke_redis_credentials.py: TasksExtension tasks through rotating credentials from AWS's ElastiCache IAM provider (botocore) and redis-entraid's streaming Entra ID provider, against a local redis-server	fastmcp, fastmcp-slim, and fastmcp-tasks wheels

Each smoke runs its checks over stdio, streamable HTTP, SSE, and a FastMCP proxy in front of a stdio server, all with bearer auth where the transport allows it. The checks cover shapes past releases got wrong: template literals such as | and non-ASCII, comma-joined and exploded list query params, elicitation in both protocol eras, schema counts written as floats, and a timed-out call followed by another on the same connection.

Each smoke prints one line per check, writes a table to the job summary, and reports warnings raised in the consumer's process. A check listed in _harness.py's PROXY_GAPS or LEGACY_PROXY_GAPS is a gap the proxy already had in 4.0.5: it shows as ⚠ without failing the run, and fails the run if it starts passing so the entry gets removed.

Pushes and PRs test the versions pinned in the workflow. The nightly run, or a manual run with latest, tests each consumer's latest release.

running locally

uv sync
uv build --wheel --out-dir dist . fastmcp_slim fastmcp_tasks
slim="fastmcp-slim @ file://$PWD/$(ls dist/fastmcp_slim-*.whl)"
full="fastmcp @ file://$PWD/$(ls dist/fastmcp-4*.whl)"
tasks="fastmcp-tasks @ file://$PWD/$(ls dist/fastmcp_tasks-*.whl)"

uv run --isolated --no-project --no-config \
    --with 'pydantic-ai-slim[mcp]' --with "${slim/fastmcp-slim/fastmcp-slim[client]}" \
    python tests/downstream/smoke_pydantic_ai.py

uv run --isolated --no-project --no-config \
    --with 'langchain[mcp]' --with "$full" --with "$slim" \
    python tests/downstream/smoke_langchain_mcp.py

uv run --isolated --no-project --no-config \
    --with langchain-mcp-adapters --with langchain \
    python tests/downstream/smoke_langchain_mcp_adapters.py

# needs redis-server on PATH
uv run --isolated --no-project --no-config \
    --with "$full" --with "$slim" --with "$tasks" \
    --with botocore --with cachetools --with redis-entraid \
    python tests/downstream/smoke_redis_credentials.py