46 lines
2 KiB
Markdown
46 lines
2 KiB
Markdown
# An LLM-Powered Chatbot MCP Client written in Python
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An interactive chatbot MCP client: it launches an MCP server over stdio, lists its tools, and hands them to Claude, which can call them while answering your questions.
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This example accompanies the [Build an MCP client](https://modelcontextprotocol.io/docs/develop/build-client) tutorial.
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## Prerequisites
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- Python 3.10+
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- [uv](https://docs.astral.sh/uv/)
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- An [Anthropic API key](https://console.anthropic.com/) (optional — see below)
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## Setup
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Put your API key in a `.env` file (or export `ANTHROPIC_API_KEY` in your shell):
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```bash
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cp .env.example .env
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# then edit .env and add your key
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```
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## Run the client
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Pass the path to a server script. `.py` servers are launched with `uv`, `.js` servers with `node`:
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```bash
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uv run client.py ../weather-server-python/weather.py
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```
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or, against the TypeScript server (build it first — see its README):
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```bash
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uv run client.py ../weather-server-typescript/build/index.js
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```
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Type a question (for example, "What's the weather in Sacramento?") and Claude answers using the server's tools. Type `quit` to exit.
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Without an `ANTHROPIC_API_KEY`, the client still connects, prints the server's tools, and exits — useful for verifying the MCP wiring without credentials.
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## Structured content
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`call_tool` validates every result against the tool's declared output schema, so the spec's client-side SHOULD needs no code here.
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The two channels go to different readers: `content` is forwarded to the model, while `structured_content` is used as data — when a tool returns an array, the client counts its items rather than re-reading the prose. See [Structured Content](https://modelcontextprotocol.io/specification/2026-07-28/server/tools#structured-content).
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`Client(transport, mode="auto")` probes `server/discover` and falls back to the `2025-11-25` handshake; `client.protocol_version` reports which era you got. See [Protocol versions](https://py.sdk.modelcontextprotocol.io/v2/protocol-versions/).
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