"Comment perdre du poids (How to lose weight)" matching MCP servers:

A runtime-free MCP server that converts source code into AST🌲, regardless of language.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

Enables automated migration of Vue projects to React with intelligent analysis, task management, and learning from user modifications.

Enables AI assistants to analyze project folders for dependencies and frameworks and fetch current documentation or datasheets from the web.

Enables migration of test automation projects from WebDriverIO to Playwright using AST-based transformations. Provides tools for analyzing tests, converting syntax, refactoring to Page Object Model, and generating migration reports.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Enables AI coding agents to reduce context window bloat by filtering noisy Git diffs, summarizing structural AST changes, simulating merges, and masking high-entropy secrets before they reach the model.

Enables MCP clients such as Claude Desktop or Cursor to inspect a coffee-shop sales database schema, validate SQL, and run guarded read-only queries. It supports natural-language questions by exposing tools for schema description, SQL checking, and safe query execution.

Enables AI coding agents to extract Figma design data and convert it into Flutter widgets and screens. Supports theme setup, component analysis, asset exports, and provides Flutter implementation guidance from Figma designs.

Translate Python between English and your language, with round-trip verification. Pack-based and reviewed, not model-guessed: translate files both ways, look up how terms render, check identifier names, and report how legible a codebase is in a language, all locally with uvx legesher-mcp.

Deterministic code review MCP server that provides tools for file selection, rule matching, comment positioning, and reflection, ensuring stable review quality without LLM calls.

Integrates Jira and Sourcegraph, enabling LLMs to retrieve and comment on Jira issues and perform code searches using Sourcegraph.

Analyze and compare public GitHub repository line counts (SLOC) from any MCP client. Provides per-language files, code, comment, and blank totals via the OctoCounts API, with compare_repos for side-by-side diffs of two repos or refs.

Enables interaction with SAP ABAP systems via ADT APIs, allowing management of ABAP objects, transport requests, and code analysis through MCP clients.

Enables scanning instruction files such as AGENTS.md, CLAUDE.md, Cursor rules, and SKILL.md to report token weight, near-duplicate rules, and opposing always/never conflicts entirely offline.

Enables agents and terminals to reverse engineer native binaries, JavaScript/Electron apps, .NET assemblies, and websites, returning findings with supporting evidence and stated limitations. Analysis runs locally, so users can investigate features without source code, explain how they work, and build comparable implementations.

Provides documentation generation and analysis tools for AI agents, including JSDoc generation, README analysis, changelog generation, API documentation, and code comment analysis.

Enables coding agents to query a codebase's git-history coupling graph to find blast radius, missing coupled files, explain why files change together via PRs/issues, and report how much of the graph was inspected.

MCP server that performs mutation testing and diff scoping to score how trustworthy tests are for detecting regressions in changed code.

Connects coding agents to local analysis tools that inspect native binaries, JavaScript/Electron apps, .NET assemblies, and websites, returning findings with supporting evidence and stated limitations. This lets an agent explain how an existing feature works without source code and then build an equivalent implementation for the user's own project.

Helps AI read GitHub repository structure and important files. Want to quickly understand what a repo is about? Prompt it with "read https://github.com/adhikasp/mcp-git-ingest and determine how the code technically works".

Enables AI agents (and a matching CLI) to inspect native binaries, JavaScript/Electron apps, .NET assemblies, Go/Rust/WASM artifacts, EVM bytecode, SQLite databases, recorded crashes, and websites without source code, explaining how features work and returning the evidence and limitations behind each conclusion. Analysis runs locally, using optional Hopper, Ghidra, or IDA providers for native targets.

An MCP server that provides code quality checks for AI coding assistants, including file size limits, ESLint, architecture compliance, anti-pattern scanning, and comment compliance.

Tells you how you and your Claude agent actually work together. Reads CLAUDE.md, hooks, skills, memory + scheduled tasks and writes you a letter. Local-only, no API keys, no data leaves your machine.

Enables AI-powered pull request review and analysis for Bitbucket Cloud, plus tools to list, create, diff, comment on, and analyze PRs. Supports stdio or HTTP transports with OAuth/bearer auth and pluggable LLM providers.

Enables agents to generate a JSON sitemap of how a path through a TypeScript/JavaScript codebase runs, capturing the summary, areas, every step with its file, lines, description, calls and data, and the links between services. The resulting document can be opened in a viewer that draws the same map as a single page, and rebuilt in CI to keep system documentation current.

Enables Claude Desktop to analyze repositories, fetch and create issues, inspect pull request diffs, and comment on pull requests through the GitHub API using natural-language commands.

Enables AI coding assistants to scan an iOS, Android, Flutter, React Native or Expo project for App Store and Google Play rejection risks, returning deterministic findings that cite the guideline section, link to the official Apple or Google page and explain how to fix each problem. Nothing leaves the machine and no AI is used in the checks.

A Model Context Protocol server that connects AI coding agents to Gerrit code review, enabling change search, diff review, and comment publishing without leaving the session.