Rohan Mehta
GitHub
commit
362a9dc078
6 changed files with 69 additions and 103 deletions
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docs/mcp.md
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51
docs/mcp.md
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# Model context protocol
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The [Model context protocol](https://modelcontextprotocol.io/introduction) (aka MCP) is a way to provide tools and context to the LLM. From the MCP docs:
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> MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
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The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents.
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## MCP servers
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Currently, the MCP spec defines two kinds of servers, based on the transport mechanism they use:
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1. **stdio** servers run as a subprocess of your application. You can think of them as running "locally".
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2. **HTTP over SSE** servers run remotely. You connect to them via a URL.
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You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse] classes to connect to these servers.
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For example, this is how you'd use the [official MCP filesystem server](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem).
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```python
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async with MCPServerStdio(
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params={
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"command": "npx",
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"args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir],
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}
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) as server:
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tools = await server.list_tools()
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```
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## Using MCP servers
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MCP servers can be added to Agents. The Agents SDK will call `list_tools()` on the MCP servers each time the Agent is run. This makes the LLM aware of the MCP server's tools. When the LLM calls a tool from an MCP server, the SDK calls `call_tool()` on that server.
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```python
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agent=Agent(
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name="Assistant",
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instructions="Use the tools to achieve the task",
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mcp_servers=[mcp_server_1, mcp_server_2]
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)
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```
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## Caching
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Every time an Agent runs, it calls `list_tools()` on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass `cache_tools_list=True` to both [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse]. You should only do this if you're certain the tool list will not change.
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If you want to invalidate the cache, you can call `invalidate_tools_cache()` on the servers.
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## End-to-end example
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View complete working examples at [examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp).
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docs/ref/mcp/server.md
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docs/ref/mcp/server.md
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# `MCP Servers`
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::: agents.mcp.server
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docs/ref/mcp/util.md
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docs/ref/mcp/util.md
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# `MCP Util`
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::: agents.mcp.util
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@ -28,6 +28,7 @@ nav:
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- results.md
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- streaming.md
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- tools.md
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- mcp.md
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- handoffs.md
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- tracing.md
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- context.md
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@ -60,6 +61,8 @@ nav:
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- ref/models/interface.md
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- ref/models/openai_chatcompletions.md
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- ref/models/openai_responses.md
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- ref/mcp/server.md
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- ref/mcp/util.md
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- Tracing:
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- ref/tracing/index.md
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- ref/tracing/create.md
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@ -107,6 +110,8 @@ plugins:
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show_signature_annotations: true
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# Makes the font sizes nicer
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heading_level: 3
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# Show inherited members
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inherited_members: true
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extra:
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# Remove material generation message in footer
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@ -1,94 +0,0 @@
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import functools
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import json
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from typing import Any
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from mcp.types import Tool as MCPTool
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from .. import _debug
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from ..exceptions import AgentsException, ModelBehaviorError, UserError
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from ..logger import logger
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from ..run_context import RunContextWrapper
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from ..tool import FunctionTool, Tool
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from .server import MCPServer
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class MCPUtil:
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"""Set of utilities for interop between MCP and Agents SDK tools."""
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@classmethod
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async def get_all_function_tools(cls, servers: list[MCPServer]) -> list[Tool]:
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"""Get all function tools from a list of MCP servers."""
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tools = []
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tool_names: set[str] = set()
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for server in servers:
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server_tools = await cls.get_function_tools(server)
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server_tool_names = {tool.name for tool in server_tools}
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if len(server_tool_names & tool_names) > 0:
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raise UserError(
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f"Duplicate tool names found across MCP servers: "
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f"{server_tool_names & tool_names}"
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)
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tool_names.update(server_tool_names)
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tools.extend(server_tools)
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return tools
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@classmethod
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async def get_function_tools(cls, server: MCPServer) -> list[Tool]:
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"""Get all function tools from a single MCP server."""
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tools = await server.list_tools()
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return [cls.to_function_tool(tool, server) for tool in tools]
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@classmethod
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def to_function_tool(cls, tool: MCPTool, server: MCPServer) -> FunctionTool:
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"""Convert an MCP tool to an Agents SDK function tool."""
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invoke_func = functools.partial(cls.invoke_mcp_tool, server, tool)
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return FunctionTool(
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name=tool.name,
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description=tool.description or "",
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params_json_schema=tool.inputSchema,
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on_invoke_tool=invoke_func,
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strict_json_schema=False,
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)
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@classmethod
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async def invoke_mcp_tool(
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cls, server: MCPServer, tool: MCPTool, context: RunContextWrapper[Any], input_json: str
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) -> str:
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"""Invoke an MCP tool and return the result as a string."""
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try:
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json_data: dict[str, Any] = json.loads(input_json) if input_json else {}
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except Exception as e:
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if _debug.DONT_LOG_TOOL_DATA:
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logger.debug(f"Invalid JSON input for tool {tool.name}")
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else:
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logger.debug(f"Invalid JSON input for tool {tool.name}: {input_json}")
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raise ModelBehaviorError(
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f"Invalid JSON input for tool {tool.name}: {input_json}"
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) from e
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if _debug.DONT_LOG_TOOL_DATA:
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logger.debug(f"Invoking MCP tool {tool.name}")
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else:
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logger.debug(f"Invoking MCP tool {tool.name} with input {input_json}")
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try:
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result = await server.call_tool(tool.name, json_data)
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except Exception as e:
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logger.error(f"Error invoking MCP tool {tool.name}: {e}")
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raise AgentsException(f"Error invoking MCP tool {tool.name}: {e}") from e
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if _debug.DONT_LOG_TOOL_DATA:
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logger.debug(f"MCP tool {tool.name} completed.")
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else:
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logger.debug(f"MCP tool {tool.name} returned {result}")
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# The MCP tool result is a list of content items, whereas OpenAI tool outputs are a single
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# string. We'll try to convert.
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if len(result.content) == 1:
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return result.content[0].model_dump_json()
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elif len(result.content) > 1:
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return json.dumps([item.model_dump() for item in result.content])
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else:
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logger.error(f"Errored MCP tool result: {result}")
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return "Error running tool."
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@ -175,10 +175,10 @@ class MCPServerStdio(_MCPServerWithClientSession):
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"""Create a new MCP server based on the stdio transport.
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Args:
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params: The params that configure the server. This includes:
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- The command (e.g. `python` or `node`) that starts the server.
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- The args to pass to the server command (e.g. `foo.py` or `server.js`).
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- The environment variables to set for the server.
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params: The params that configure the server. This includes the command to run to
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start the server, the args to pass to the command, the environment variables to
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set for the server, the working directory to use when spawning the process, and
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the text encoding used when sending/receiving messages to the server.
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cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be
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cached and only fetched from the server once. If `False`, the tools list will be
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fetched from the server on each call to `list_tools()`. The cache can be
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@ -235,11 +235,9 @@ class MCPServerSse(_MCPServerWithClientSession):
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"""Create a new MCP server based on the HTTP with SSE transport.
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Args:
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params: The params that configure the server. This includes:
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- The URL of the server.
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- The headers to send to the server.
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- The timeout for the HTTP request.
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- The timeout for the SSE connection.
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params: The params that configure the server. This includes the URL of the server,
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the headers to send to the server, the timeout for the HTTP request, and the
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timeout for the SSE connection.
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cache_tools_list: Whether to cache the tools list. If `True`, the tools list will be
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cached and only fetched from the server once. If `False`, the tools list will be
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