a96108e279
## Summary - mention MCPServerStreamableHttp in MCP server docs - document CodeInterpreterTool, HostedMCPTool, ImageGenerationTool and LocalShellTool - update Japanese translations
61 lines
3 KiB
Markdown
61 lines
3 KiB
Markdown
# Model context protocol (MCP)
|
|
|
|
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:
|
|
|
|
> 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.
|
|
|
|
The Agents SDK has support for MCP. This enables you to use a wide range of MCP servers to provide tools to your Agents.
|
|
|
|
## MCP servers
|
|
|
|
Currently, the MCP spec defines two kinds of servers, based on the transport mechanism they use:
|
|
|
|
1. **stdio** servers run as a subprocess of your application. You can think of them as running "locally".
|
|
2. **HTTP over SSE** servers run remotely. You connect to them via a URL.
|
|
3. **Streamable HTTP** servers run remotely using the Streamable HTTP transport defined in the MCP spec.
|
|
|
|
You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio], [`MCPServerSse`][agents.mcp.server.MCPServerSse], and [`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp] classes to connect to these servers.
|
|
|
|
For example, this is how you'd use the [official MCP filesystem server](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem).
|
|
|
|
```python
|
|
async with MCPServerStdio(
|
|
params={
|
|
"command": "npx",
|
|
"args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir],
|
|
}
|
|
) as server:
|
|
tools = await server.list_tools()
|
|
```
|
|
|
|
## Using MCP servers
|
|
|
|
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.
|
|
|
|
```python
|
|
|
|
agent=Agent(
|
|
name="Assistant",
|
|
instructions="Use the tools to achieve the task",
|
|
mcp_servers=[mcp_server_1, mcp_server_2]
|
|
)
|
|
```
|
|
|
|
## Caching
|
|
|
|
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 [`MCPServerStdio`][agents.mcp.server.MCPServerStdio], [`MCPServerSse`][agents.mcp.server.MCPServerSse], and [`MCPServerStreamableHttp`][agents.mcp.server.MCPServerStreamableHttp]. You should only do this if you're certain the tool list will not change.
|
|
|
|
If you want to invalidate the cache, you can call `invalidate_tools_cache()` on the servers.
|
|
|
|
## End-to-end examples
|
|
|
|
View complete working examples at [examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp).
|
|
|
|
## Tracing
|
|
|
|
[Tracing](./tracing.md) automatically captures MCP operations, including:
|
|
|
|
1. Calls to the MCP server to list tools
|
|
2. MCP-related info on function calls
|
|
|
|
|