Rust Docs Mcp

Rust is the language of AI

An MCP (Model Context Protocol) server that provides comprehensive access to Rust crate documentation, source code analysis, dependency trees, and module structure visualization. Built for agents to gain quality insights into Rust projects and build with confidence.

Warning

This is an entirely vibe coded project and was created as an experiment to see if LLMs can build a fully functional tool without human oversight.

The Problem: Agents Building in the Dark

The rise of AI agents has revolutionized software development, with new tools and libraries emerging at an unprecedented pace. However, this rapid evolution creates a critical challenge: agents cannot reliably build with tools they weren't trained on.

When an agent tries to use a new Rust crate:

• 🚫 Training data is outdated — The model hasn't seen recent crates or API changes
• 🚫 Documentation scraping is inefficient — Web scraping GitHub or docs.rs is slow and unreliable
• 🚫 Examples aren't enough — Copy-pasting README examples provides surface-level understanding
• 🚫 Internal structure is opaque — Agents can't explore how modules, traits, and types interconnect

This leads to frustrated developers watching their agents fail repeatedly, guessing at APIs, and producing broken code.

The Solution: Deep Crate Intelligence

This MCP server gives agents the tools they need to truly understand Rust crates:

• 🔍 Explore internal structure — Navigate module hierarchies and type relationships
• 📖 Access complete documentation — Full rustdoc with signatures, fields, and methods
• 🔗 Trace dependencies — Understand what a crate depends on and why
• 💾 Work offline — Cache crates locally for instant, reliable access
• 🎯 Query precisely — Search by pattern, kind, or path to find exactly what's needed

With these capabilities, agents can build confidently with any Rust crate — even ones released yesterday.

Agent Capabilities

• Multi-source caching — crates.io, GitHub repositories, local filesystem paths
• Workspace support — Individual member analysis and caching for cargo workspaces
• Documentation search — Pattern matching with kind/path filtering and preview modes
• Item inspection — Detailed signatures, fields, methods, and documentation strings
• Source code access — Line-level precision with parameterized surrounding context
• Dependency analysis — Direct and transitive dependency trees with metadata
• Module structure — Hierarchical tree generation via cargo-modules integration
• Offline operation — Full functionality after initial crate caching
• Token management — Response truncation and preview modes for LLM compatibility

MCP Tools

Cache Management