"Information about Rust" matching MCP servers:

A MCP server to allow the LLM in Cursor to access Rust Analyzer, Crate Docs and Cargo Commands.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Enables asking natural-language questions about any GitHub repository and receiving answers grounded in the actual code, with read-only enforcement and automatic repository fetching.

truth is a deterministic fact-checker for the claims an AI coding agent makes about its own work.

High-performance local MCP server and CLI written in Rust that scrubs 90%+ framework noise from AI debug logs with Tree-sitter AST validation and test rollback.

Semantic git queries via MCP. Beyond git log — answer who/what/why about any line, file, or branch with blame, co-change, PR linkage.

MCP server that provides npm package information for AI agents during TypeScript development.

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

Enables AI agents to search and retrieve details about Odoo code elements (models, fields, views, etc.) with exact file locations using MCP tools.

Local code-graph engine for AI coding workflows: static analysis of Rust, Cangjie, ArkTS, TypeScript/JavaScript, C/C++, Python, and Shell projects into symbols, imports, call graphs, and quality gates, with pre-commit change review and impact analysis. Read-only by design — never executes build scripts or uploads code.

Provides coding agents with a compact map of a TypeScript/TSX codebase, enabling direct answers about symbol locations, imports, dependencies, and references while reducing token usage.

Record Python/Rust/TypeScript program runs to local SQLite; query call trees, exceptions, and value flow over MCP — refuses rather than guesses. Not the unrelated Telegram sensorium-mcp.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Deterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.

Bridges Claude Code to Language Server Protocol (LSP) servers to enable semantic code intelligence features like navigation, refactoring, and real-time diagnostics. It supports multiple languages including TypeScript, Python, and Rust with multi-root workspace capabilities.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Enables inspection and analysis of project structures to detect languages, frameworks, entry points, and dependencies across multiple programming languages including Node.js, Python, PHP, Go, Java, and Rust.

Enables AI agents to perform step-through debugging of Python, JavaScript/Node.js, and Rust programs using the Debug Adapter Protocol, with support for breakpoints, variable inspection, and stack traces.

Enables coding agents to establish exact-release library identity, search provenance-tracked API, docs, example, source, and release evidence, compare releases, and test proposed invocations in isolated capsules for Rust and Python dependency environments.

Extracts minimal, relevant code context from multiple programming languages while analyzing diffs and optimizing imports to reduce token usage for AI assistants. Supports TypeScript/JavaScript, Python, Go, and Rust with token-aware caching.