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🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.
Modern AI-powered coding assistants (like Cursor, Cline, Roo Code, etc.) excel at understanding code structure and syntax but often struggle with the specifics of rapidly evolving libraries and frameworks, especially in ecosystems like Rust where crates are updated frequently. Their training data cutoff means they may lack knowledge of the latest APIs, leading to incorrect or outdated code suggestions.
This MCP server addresses this challenge by providing a focused, up-to-date
knowledge source for a specific Rust crate. By running an instance of this
server for a crate (e.g., serde, tokio, reqwest), you give your LLM coding
assistant a tool (query_rust_docs) it can use before writing code related to
that crate.
When instructed to use this tool, the LLM can ask specific questions about the crate's API or usage and receive answers derived directly from the current documentation. This significantly improves the accuracy and relevance of the generated code, reducing the need for manual correction and speeding up development.
Multiple instances of this server can be run concurrently, allowing the LLM assistant to access documentation for several different crates during a coding session.
This server fetches the documentation for a specified Rust crate, generates embeddings for the content, and provides an MCP tool to answer questions about the crate based on the documentation context.
text-embedding-3-small model to find the
most relevant documentation sections for a given question.gpt-4o-mini-2024-07-18 model to
generate concise answers based only on the retrieved documentation context.~/.local/share/rustdocs-mcp-server/ or similar)
based on crate, version, and requested features to speed up subsequent
launches.OPENAI_API_KEY
environment variable. (The server also requires network access to download
crate dependencies and interact with the OpenAI API).The recommended way to install is to download the pre-compiled binary for your operating system from the GitHub Releases page.
.zip for Windows, .tar.gz for
Linux/macOS) for your system.rustdocs_mcp_server (or rustdocs_mcp_server.exe) binary.PATH environment
variable (e.g., /usr/local/bin, ~/bin).If you prefer to build from source, you will need the Rust Toolchain installed.
git clone https://github.com/Govcraft/rust-docs-mcp-server.git
cd rust-docs-mcp-server
cargo build --release
Important Note for New Crates:
When using the server with a crate for the first time (or with a new version/feature set), it needs to download the documentation and generate embeddings. This process can take some time, especially for crates with extensive documentation, and requires an active internet connection and OpenAI API key.
It is recommended to run the server once directly from your command line for any new crate configuration before adding it to your AI coding assistant (like Roo Code, Cursor, etc.). This allows the initial embedding generation and caching to complete. Once you see the server startup messages indicating it's ready (e.g., "MCP Server listening on stdio"), you can shut it down (Ctrl+C). Subsequent launches, including those initiated by your coding assistant, will use the cached data and start much faster.
The server is launched from the command line and requires the Package ID
Specification for the target crate. This specification follows the format used
by Cargo (e.g., crate_name, crate_name@version_req). For the full
specification details, see man cargo-pkgid or the
Cargo documentation.
Optionally, you can specify required crate features using the -F or
--features flag, followed by a comma-separated list of features. This is
necessary for crates that require specific features to be enabled for
cargo doc to succeed (e.g., crates requiring a runtime feature like
async-stripe).
# Set the API key (replace with your actual key)
export OPENAI_API_KEY="sk-..."
# Example: Run server for the latest 1.x version of serde
rustdocs_mcp_server "serde@^1.0"
# Example: Run server for a specific version of reqwest
rustdocs_mcp_server "[email protected]"
# Example: Run server for the latest version of tokio
rustdocs_mcp_server tokio
# Example: Run server for async-stripe, enabling a required runtime feature
rustdocs_mcp_server "[email protected]" -F runtime-tokio-hyper-rustls
# Example: Run server for another crate with multiple features
rustdocs_mcp_server "[email protected]" --features feat1,feat2
On the first run for a specific crate version and feature set, the server will:
cargo doc (with specified features).async-stripe with over 5000
documentation pages cost only $0.18 USD for embedding generation during
testing).Subsequent runs for the same crate version and feature set will load the data from the cache, making startup much faster.
The server communicates using the Model Context Protocol over standard input/output (stdio). It exposes the following:
Tool: query_rust_docs
{
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The specific question about the crate's API or usage."
}
},
"required": ["question"]
}
From <crate_name> docs:.{
"jsonrpc": "2.0",
"method": "callTool",
"params": {
"tool_name": "query_rust_docs",
"arguments": {
"question": "How do I make a simple GET request with reqwest?"
}
},
"id": 1
}
Resource: crate://<crate_name>
crate://<crate_name> (e.g., crate://serde, crate://reqwest)Logging: The server sends informational logs (startup messages, query
processing steps) back to the MCP client via logging/message notifications.
You can configure MCP clients like Roo Code to run multiple instances of this
server, each targeting a different crate. Here's an example snippet for Roo
Code's mcp_settings.json file, configuring servers for reqwest and
async-stripe (note the added features argument for async-stripe):
{
"mcpServers": {
"rust-docs-reqwest": {
"command": "/path/to/your/rustdocs_mcp_server",
"args": [
"[email protected]"
],
"env": {
"OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE"
},
"disabled": false,
"alwaysAllow": []
},
"rust-docs-async-stripe": {
"command": "rustdocs_mcp_server",
"args": [
"[email protected]",
"-F",
" runtime-tokio-hyper-rustls"
],
"env": {
"OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE"
},
"disabled": false,
"alwaysAllow": []
}
}
}
Note:
/path/to/your/rustdocs_mcp_server with the actual path to the
compiled binary on your system if it isn't in your PATH.YOUR_OPENAI_API_KEY_HERE with your actual OpenAI API key.rust-docs-reqwest, rust-docs-async-stripe) are arbitrary names
you choose to identify the server instances within Roo Code.For Claude Desktop users, you can configure the server in the MCP settings.
Here's an example configuring servers for serde and async-stripe:
{
"mcpServers": {
"rust-docs-serde": {
"command": "/path/to/your/rustdocs_mcp_server",
"args": [
"serde@^1.0"
]
},
"rust-docs-async-stripe-rt": {
"command": "rustdocs_mcp_server",
"args": [
"[email protected]",
"-F",
"runtime-tokio-hyper-rustls"
]
}
}
}
Note:
rustdocs_mcp_server is in your system's PATH or provide the full path
(e.g., /path/to/your/rustdocs_mcp_server).rust-docs-serde, rust-docs-async-stripe-rt) are arbitrary names
you choose to identify the server instances.OPENAI_API_KEY environment variable where Claude Desktop
can access it (this might be system-wide or via how you launch Claude
Desktop). Claude Desktop's MCP configuration might not directly support
setting environment variables per-server like Roo Code.-F argument for crates like async-stripe
that require specific features.~/.local/share/rustdocs-mcp-server/<crate_name>/<sanitized_version_req>/<features_hash>/embeddings.bin.
The sanitized_version_req is derived from the version requirement, and
features_hash is a hash representing the specific combination of features
requested at startup. This ensures different feature sets are cached
separately.bincode serialization.clap.Cargo.toml.cargo doc using the cargo library API to generate HTML
documentation in the temporary directory.target/doc by
searching for the subdirectory containing index.html.scraper crate to parse each HTML file and extract text content
from the main content area (<section id="main-content">).async-openai crate and tiktoken-rs to generate embeddings for
each extracted document chunk using the text-embedding-3-small model.bincode.RustDocsServer with the
loaded/generated documents and embeddings.rmcp over stdio.query_rust_docs tool):
gpt-4o-mini-2024-07-18 model via the OpenAI API.This project is licensed under the MIT License.
Copyright (c) 2025 Govcraft
Govcraft is a one-person shop—no corporate backing, no investors, just me building useful tools. If this project helps you, sponsoring keeps the work going.
🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.
Outils
Pas encore de liste d'outils. Les serveurs distants en obtiennent une après le premier contrôle d'état.
FAQ
Copiez sur cette page la configuration stdio pour Cursor, Claude Desktop, Claude Code ou VS Code. Elle utilise l'installation de l'éditeur, pas un proxy PlugMCP.
Aucune variable d'environnement requise n'est indiquée sur cette fiche.
Rust Docs s'exécute en local via stdio.
Dernière vérification : 2026-10-26. 🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.
