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.
This example accompanies the Build an MCP client tutorial.
- Rust 1.88+ and Cargo
- An Anthropic API key (optional — see below)
Export your API key, or put it in a .env file in this directory (the .env file is optional):
export ANTHROPIC_API_KEY=your-api-key-herePass the command that starts the server — a binary or a script — after --:
cargo run -- ../weather-server-rust/target/release/weather(Build the server first with cargo build --release in its directory.)
Type a question (for example, "What's the weather in Sacramento?") and Claude answers using the server's tools. Type quit to exit.
Without an ANTHROPIC_API_KEY, the client still connects, prints the server's tools, and exits — useful for verifying the MCP wiring without credentials.
There is no official Anthropic SDK for Rust, so this client calls the Messages API over HTTP with reqwest. That keeps the request shape visible, and it means an assistant turn can be fed back exactly as it arrived. Claude requires that: the blocks it sends must return unchanged on the next request, a thinking block's signature included, and a wrapper type that models only text and tool calls would drop the rest.
ANTHROPIC_BASE_URL overrides the endpoint, as it does in the official SDKs for the other languages.
rmcp's serve only performs the legacy 2025-11-25 initialize handshake, so this client uses serve_with_lifecycle with ClientLifecycleMode::Auto instead: one server/discover probe for 2026-07-28, falling back to initialize. Matching the Python and TypeScript clients' auto mode, it also makes this era's features reachable — an array-rooted outputSchema, for one, which get_alerts uses. The negotiated revision is printed on connect.
rmcp 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 the jsonschema crate, which rmcp's own documentation recommends.
The two channels go to different readers: content is forwarded to the model, while structured_content is used as data — the client counts the items it returns. See Structured Content.