43 lines
1.9 KiB
Markdown
43 lines
1.9 KiB
Markdown
# An LLM-Powered Chatbot MCP Client written in Go
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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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- Go 1.25+
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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 — the `.env` file is optional):
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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 command that starts the server — a binary or an interpreter plus its arguments:
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```bash
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go run main.go ../weather-server-go/weather
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```
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(Build the server first with `go build -o weather` in its directory.) Any server command works, for example the Python server:
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```bash
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go run main.go uv --directory ../weather-server-python run weather.py
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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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The SDK does not validate tool output, so this client compiles each declared `outputSchema` at connect time and checks results against it — the spec's client-side SHOULD. It uses [`jsonschema-go`](https://pkg.go.dev/github.com/google/jsonschema-go/jsonschema), already an SDK dependency.
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The two channels go to different readers: `Content` is forwarded to the model, while `StructuredContent` is used as data — the client counts the items it returns. See [Structured Content](https://modelcontextprotocol.io/specification/2026-07-28/server/tools#structured-content).
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