"Guides and Tools for Converting Figma Designs to Code" matching MCP servers:

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

Enables Cursor to access Figma files through the Model Context Protocol, enhancing its ability to accurately interpret and utilize design data for code generation.

Local MCP server that transforms Figma documentation and business rules into a semantically searchable index, exposed as a tool for Claude Code to query via natural language.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables semantic search over codebases using natural language queries, returning relevant code snippets with source locations. Integrates with Claude Code for automatic codebase exploration.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Provides access to OpenAI's advanced models (including o3) with integrated web search, Python code interpreter, and combined analysis capabilities. Enables users to perform web searches, execute code in sandboxed environments, and combine search with analysis through natural language.

An MCP server designed to assist with generating, converting, and translating Milvus SDK code by retrieving relevant documentation and snippets. It supports PyMilvus code generation, ORM-to-client conversion, and cross-language translation between Python, Java, Go, and other supported languages.

A multi-function Streamable HTTP MCP tool aggregation server that provides web search via Brave, Exa, and SearXNG with multi-key rotation and cross-provider fallback, and supports extensible tool families (URL fetch, code search, RAG) through a pluggable architecture.

Enables semantic code search across indexed codebases using natural language queries, with support for CLI and MCP interfaces.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Enables AI assistants to perform hybrid semantic and lexical code search across multiple repositories, retrieve symbol definitions and call hierarchies, and manage repository relations through MCP tools.

Enables clinical question answering via OpenEvidence with citation verification, including tools to ask questions, retrieve results, and follow up, plus an integrated skill to check citations against primary sources.