60 lines
2.7 KiB
Markdown
60 lines
2.7 KiB
Markdown
# Model context protocol (MCP)
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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 examples
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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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## Tracing
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[Tracing](./tracing.md) automatically captures MCP operations, including:
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1. Calls to the MCP server to list tools
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2. MCP-related info on function calls
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