"Setting up Minecraft (MCP) servers with Python and Visual Studio Code" matching MCP servers:

A workspace of six MCP servers—grounded-cv (hybrid RAG with citations), repo-intel (GitHub repo intelligence), web-research (safe web search/extraction), ats-jobs (unified ATS job boards), outcome-ledger (durable task ledger), and coding-workflows (code review & planning)—purpose-built for agentic hiring evaluations.

Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Integrates with the Datalog Studio REST API to explore projects, tables, and assets within a workspace. It enables users to understand data schemas and upload plain text content directly for AI processing.

A Python MCP server providing AI agents with tools for local knowledge base search, business context retrieval, and text summarization prompt generation.

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.

Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

Self-hosted hybrid code search MCP server with text, symbol, and semantic search layers. Runs locally, no third-party MCP servers, LSP, or SaaS.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.

Enables notes management, weather and news search, document ingestion and RAG-based semantic search using GroundX, with OpenAI GPT integration for summarization and completions.

Provides a retrieval/MCP knowledge server for the Unreal Engine Python API, enabling AI agents to retrieve real signatures and documentation before writing code.

A full-feature MCP server that serves as a product-development copilot, providing RAG search, SQL queries, and inline chart visualizations from seeded product data, with per-user identity and its own OAuth login.

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.

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

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.

An MCP (Model Context Protocol) server that turns the book Deep Learning with Python (François Chollet) into a searchable knowledge base, so Claude can act as a deep-learning/ML expert grounded in the book's content.