---
title: Resources as Tools
sidebarTitle: Resources as Tools
description: Expose resources to tool-only clients
icon: toolbox
tag: NEW
---

import { VersionBadge } from '/snippets/version-badge.mdx'

<VersionBadge version="3.0.0" />

Some MCP clients only support tools. They cannot list or read resources directly because they lack resource protocol support. The `ResourcesAsTools` transform bridges this gap by generating tools that provide access to your server's resources.

When you add `ResourcesAsTools` to a server, it creates two tools that clients can call instead of using the resource protocol:

- **`list_resources`** returns JSON describing all available resources and templates
- **`read_resource`** reads a specific resource by URI

This means any client that can call tools can now access resources, even if the client has no native resource support.

## Basic Usage

Pass your server to `ResourcesAsTools` when adding the transform. The transform queries that server for resources whenever the generated tools are called.

```python
from fastmcp import FastMCP
from fastmcp.server.transforms import ResourcesAsTools

mcp = FastMCP("My Server")

@mcp.resource("config://app")
def app_config() -> str:
    """Application configuration."""
    return '{"app_name": "My App", "version": "1.0.0"}'

@mcp.resource("user://{user_id}/profile")
def user_profile(user_id: str) -> str:
    """Get a user's profile by ID."""
    return f'{{"user_id": "{user_id}", "name": "User {user_id}"}}'

# Add the transform - creates list_resources and read_resource tools
mcp.add_transform(ResourcesAsTools(mcp))
```

Clients now see three tools: whatever tools you defined directly, plus `list_resources` and `read_resource`.

Both generated tools are annotated with `readOnlyHint=True`, since they only read data. Clients that respect tool annotations (like Cursor) can use this to auto-confirm these tool calls without prompting the user.

## Static Resources vs Templates

Resources come in two forms, and the `list_resources` tool distinguishes between them in its JSON output.

Static resources have fixed URIs. They represent concrete data that exists at a known location. In the listing output, static resources include a `uri` field containing the exact URI to request.

Resource templates have parameterized URIs with placeholders like `{user_id}`. They represent patterns for accessing dynamic data. In the listing output, templates include a `uri_template` field showing the pattern with its placeholders.

When a client calls `list_resources`, it receives JSON like this:

```json
[
  {
    "uri": "config://app",
    "name": "app_config",
    "description": "Application configuration.",
    "mime_type": "text/plain"
  },
  {
    "uri_template": "user://{user_id}/profile",
    "name": "user_profile",
    "description": "Get a user's profile by ID."
  }
]
```

The client can distinguish resource types by checking which field is present: `uri` for static resources, `uri_template` for templates.

## Reading Resources

The `read_resource` tool accepts a single `uri` argument. For static resources, pass the exact URI. For templates, fill in the placeholders with actual values.

```python
# Reading a static resource
result = await client.call_tool("read_resource", {"uri": "config://app"})
print(result.data)  # '{"app_name": "My App", "version": "1.0.0"}'

# Reading a templated resource - fill in {user_id} with an actual ID
result = await client.call_tool("read_resource", {"uri": "user://42/profile"})
print(result.data)  # '{"user_id": "42", "name": "User 42"}'
```

The transform handles template matching automatically. When you request `user://42/profile`, it matches against the `user://{user_id}/profile` template, extracts `user_id=42`, and calls your resource function with that parameter.

## Binary Content

Resources that return binary data (like images or files) are automatically base64-encoded when read through the `read_resource` tool. This ensures binary content can be transmitted as a string in the tool response.

```python
@mcp.resource("data://binary", mime_type="application/octet-stream")
def binary_data() -> bytes:
    return b"\x00\x01\x02\x03"

# Client receives base64-encoded string
result = await client.call_tool("read_resource", {"uri": "data://binary"})
decoded = base64.b64decode(result.data)  # b'\x00\x01\x02\x03'
```

