"Information about the programming language Rust" matching MCP servers:

TypeScript-based MCP server designed to enhance code editing experiences by providing features such as hover information, code completion, and diagnostics.

Enables bounded, source-write-free Rust development workflows: inspect Cargo workspaces offline, search and read source, collect compiler diagnostics, select and run tests or binaries/examples, resolve exact-version docs, and produce verified navigation or change/refactor packages without writing the workspace.

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

Enables AI coding agents to interact with TypeScript projects through compiler-level code intelligence, providing tools for navigation, type information, diagnostics, refactoring, and semantic search.

Enables Codex to read, write, and design windows for E-language (易语言) programs using official IDE APIs, allowing natural language control of development workflows.

Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.

Exposes VSCode's Language Server Protocol features through MCP, enabling AI assistants to perform language-aware operations like symbol navigation, reference tracking, safe renaming, type information retrieval, and hover documentation across codebases.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Provides LLMs with comprehensive knowledge about the V programming language, including documentation access, code examples, standard library reference, and syntax explanations to help users learn and work with V.

Enables intelligent image analysis using GLM-4.5V model, specializing in extracting and analyzing code from screenshots with support for file paths and clipboard input.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

A high-performance CLI tool that provides semantic code search, advanced architectural analysis, and codebase indexing with vector embeddings across multiple programming languages. Enables AI assistants to understand and navigate large codebases through graph-based relationships and intelligent code pattern detection.

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.

Enables LLM-powered agents to generate, execute, and analyze Alloy modeling language code for software modeling and formal verification. Provides tools to run Alloy models, access documentation and examples, and generate models from system descriptions.

Enables AI IDEs and assistants to perform AST analysis, map dependency graphs and call chains, run semantic code search, and optionally sync cloud-backed memory — all locally so source code never leaves the machine.

Enables agents to audit and safeguard repositories by detecting dependency pinning issues, license compliance problems, hardcoded secrets, and dead code through MCP tools.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

Enables coding agents to audit a Next.js codebase read-only, checking routes, Prisma models, environment variables, and locale consistency via MCP tools.

MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.

Bridges MCP clients to Language Server Protocol servers like rust-analyzer, pyright, and gopls, exposing type information, references, diagnostics, and refactoring as 31 MCP tools. This lets AI coding agents reason about code with IDE-level understanding across single- or multi-language workspaces.

Exposes a set of CLI tools (test generation, documentation generation, linting, test running, code search) to AI assistants via MCP, allowing them to perform these tasks through natural language.

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.

Audits a module against its test suite and reports the cases the tests are structurally unable to see, then proves each gap with a real failing test rather than a warning. Covers Python, TypeScript, JavaScript, Java, Rust and Go.

Call graph MCP server for Rust, Python, TypeScript, TSX, and JavaScript. Every caller and callee carries a resolution field showing how the reference was matched to a definition, plus multi-hop impact analysis for refactoring. Local-only, single binary, no network egress. Benchmarked against grep, ripgrep, and ctags on a 120-item hand-verified golden set, including the cases where it loses.

Verified, dated facts for AI coding agents: what breaks between npm package versions (diffed from TypeScript types), per-version vulnerabilities, the newest version compatible with a given Node/Python/Rust/PHP, end-of-life dates of ~480 runtimes and frameworks, and AI model prices. Covers npm, PyPI, crates.io and Packagist.

Contract-first programming language with a local MCP server for checking types, effects, capabilities, and contracts in agent-written modules. Its review tool compares before and after source sets and returns receipts for new authority, weaker promise tiers, and new Guarded runtime obligations.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

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.