"How to find people on LinkedIn" matching MCP servers:

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.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables AI agents to find and query real-time GitHub coding bounties with built-in scam filtering, supporting listing, matching, and detailed bounty retrieval.

Enables semantic search over local markdown specs, RFCs, and docs using Ollama and LanceDB, allowing AI agents to find relevant information by meaning rather than exact keywords.

Provides semantic search and access to Contraption Company blog posts and essays. Uses AI-powered embeddings to find relevant content and supports real-time updates via webhooks.

Read-only semantic retrieval for agents that need to find the right Obsidian note without write access.

Provides code repository indexing and semantic search capabilities, allowing natural language queries to find relevant code snippets with automatic incremental indexing and multi-language support.

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.

Find, run, and manage your local AI models, then talk to your files, your code, and the sites you crawl. Every answer cites the source, it all runs on your own machine, and it's one program with nothing else to set up.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Enables semantic search across multiple knowledge datasets using FAISS vector embeddings, allowing natural language queries to find relevant documents with fast retrieval.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

A Model Context Protocol server that runs on Cloudflare Workers with OAuth login, allowing clients like Claude Desktop to connect to it for tool-augmented AI interactions.

A Model Context Protocol server that provides AI assistants with access to Glean's enterprise knowledge features, including content search, people directory, and AI chat capabilities.

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

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables AI assistants and developer tools to securely access and interact with an organization's enterprise knowledge, documents, and people through natural language while respecting existing access permissions.

Enables creation and management of AI-powered web entity collections (companies, people, research papers) with automated search, data enrichment, monitoring, and verification through Exa's Websets API.