Tools: Why I Spent 4 Months Building a "Map" for the Model Context Protocol (MCP)

The Beginning: From Data Science to AI Agents ## The Middle: Curating the Chaos ## The Result: The mcp-registry ## Why I’m doing this ## Check it out! ## Zayan-Mohamed / mcp-registry ## A curated discovery engine for Model Context Protocol (MCP) servers: installation guides, security profiles and configuration snippets. ## Essential MCP Server Registry ## Master List ## Knowledge Retrieval ## Vector Stores ## Data Stores Imagine this: It’s 2 AM. You just found a cool new tool that promises to let your AI read your local database, search the web, and even manage your calendar. You’re excited. But then, the "Config Nightmare" begins. You’re staring at a claude_desktop_config.json file. The documentation is scattered. Is the argument an array or a string? Does this server need an API key? And most importantly—is it safe to give this server access to my entire file system? As a Data Science undergraduate at SLIIT, I’ve spent the last few months obsessed with the Model Context Protocol (MCP). But the more I explored, the more I realized we were missing a "Map." So, I decided to build one: mcp-registry. A few months ago, while working on data preprocessing and ensemble learning assignments, I started experimenting with how AI could help me automate my workflow. I wanted my AI to do more than just write code; I wanted it to interact with my local environment—running security scans with tools like SecScan or managing my commits with GitWizard. MCP was the missing link. It turned my AI from a "chatter" into a "doer." But every time I wanted to try a new server, I had to hunt down the repo, guess the environment variables, and pray it didn't break my config. Over the last few months, my private "note-to-self" list started growing. I started treating this list like a Data Science project—categorizing servers by their capabilities, auditing their security profiles, and writing down standardized configuration snippets. I wanted a place where any developer could just copy, paste, and play. I finally decided to open-source this list. It’s not just a "link list." It’s a discovery engine. I believe MCP is the future of how we work with AI. But for that future to happen, the tools need to be accessible. Whether you’re a student like me or a senior engineer, you shouldn't have to spend hours on a JSON file just to get a weather tool or a database connector working. I’m still adding to it every day. If you’ve built a server or found one that changed your workflow, let’s add it to the map! A curated discovery engine for Model Context Protocol (MCP) servers: installation guides, security profiles and configuration snippets. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - "This one is great for Google Search." - "This one is essential for Postgres." - "Watch out, this one needs full admin access." - 📂 Categorized Servers: From Dev Tools to specialized Data Science and ML connectors. - 🛡️ Security Profiles: I’ve audited each one so you know exactly what permissions (Network/File System) you’re granting. - 📋 Copy-Paste Configs: Ready-to-use JSON blocks for Claude Desktop, Cursor, and Zed. - Repo Link: Zayan-Mohamed / mcp-registry A curated discovery engine for Model Context Protocol (MCP) servers: installation guides, security profiles and configuration snippets. Essential MCP Server Registry A curated discovery engine for Model Context Protocol (MCP) servers: installation guides, security profiles and configuration snippets. INSTRUCTIONS TEMPLATE DOCS Master List Knowledge Retrieval Overview — Summary and recommendations for retrieval tools. Claude Desktop — Local-first connector for semantic search. Cursor — Streaming retrieval & developer UI. Vector Stores Overview — Summary and guidance for vector databases. Pinecone — Managed vector DB. Qdrant — Open-source vector database for production. Milvus — Scalable vector store for large datasets. Weaviate — Schema-driven vector search engine. Chroma — Lightweight embedded vector DB. FAISS — Local similarity search library. Redis Stack — In-memory vector similarity with Redis Stack. Data Stores Overview — Guidance for choosing a backing store. DuckDB (Local) — Local analytical DB for fast SQL queries. Postgres — Relational DB for metadata and schemas. CockroachDB — Distributed SQL for fault tolerance and geo. Supabase — Hosted Postgres +… View on GitHub - INSTRUCTIONS - Overview — Summary and recommendations for retrieval tools. - Claude Desktop — Local-first connector for semantic search. - Cursor — Streaming retrieval & developer UI. - Overview — Summary and guidance for vector databases. - Pinecone — Managed vector DB. - Qdrant — Open-source vector database for production. - Milvus — Scalable vector store for large datasets. - Weaviate — Schema-driven vector search engine. - Chroma — Lightweight embedded vector DB. - FAISS — Local similarity search library. - Redis Stack — In-memory vector similarity with Redis Stack. - Overview — Guidance for choosing a backing store. - DuckDB (Local) — Local analytical DB for fast SQL queries. - Postgres — Relational DB for metadata and schemas. - CockroachDB — Distributed SQL for fault tolerance and geo. - Supabase — Hosted Postgres +… - INSTRUCTIONS - Overview — Summary and recommendations for retrieval tools. - Claude Desktop — Local-first connector for semantic search. - Cursor — Streaming retrieval & developer UI. - Overview — Summary and guidance for vector databases. - Pinecone — Managed vector DB. - Qdrant — Open-source vector database for production. - Milvus — Scalable vector store for large datasets. - Weaviate — Schema-driven vector search engine. - Chroma — Lightweight embedded vector DB. - FAISS — Local similarity search library. - Redis Stack — In-memory vector similarity with Redis Stack. - Overview — Guidance for choosing a backing store. - DuckDB (Local) — Local analytical DB for fast SQL queries. - Postgres — Relational DB for metadata and schemas. - CockroachDB — Distributed SQL for fault tolerance and geo. - Supabase — Hosted Postgres +…