"How to Use the WeChat Developer Assistant" matching MCP servers:

Enables agents to search SEC filings, earnings transcripts, and EU regulations with ready-to-cite evidence, including exact passages and source links.

MCP server that enables AI agents to fetch and summarize WeChat public articles, supporting export to HTML, Markdown, Obsidian, Notion, and OneNote.

Provides structured web search capabilities optimized for technical and software development content via providers like OpenRouter. It enables AI agents to perform research and retrieve relevant technical data in a consistent, programmatic JSON format.

Pinecone Assistant MCP server

Provides MCP tools for searching, retrieving, and answering questions about indexed Nextcloud developer documentation from multiple sources.

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.

MCP server for querying your local Google Takeout activity (Search/YouTube history) via SQLite/FTS5. Provides tools for keyword search, activity stats, and database info, enabling AI assistants to cite your personal history.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

A read-only MCP server that enables AI clients to search a knowledge base and retrieve complete source documents via narrow search and fetch tools.

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.

Enables interaction with OpenAI's Chat Completion and Assistants APIs, supporting assistant management, file operations, and direct queries to GPT models through standardized MCP tools.

Enables querying, listing, and summarizing personal knowledge base documents using RAG with hybrid search and LLM.

An enterprise MCP server that transforms IFS Cloud documentation, local code, and Oracle error knowledge into AI-powered development intelligence, offering 60+ tools for code generation, review, error diagnosis, and deployment.

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.

Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.

Enables AI-powered interaction with Metabase instances and PostgreSQL databases through natural language. Creates models, SQL queries, metrics, and dashboards using both Metabase API and direct database connections.

Provides medical evidence tools including PubMed search, citation verification, clinical trial lookup, drug information, drug interaction checking, PICO extraction, and medical entity recognition via MCP.

Enables local document Q&A by reading .txt or .pdf files and answering questions in Persian using locally running models.

It lets MCP clients such as Claude Desktop ask natural-language questions about the Model Context Protocol and get grounded answers with reranking, semantic caching, guardrails, PII masking, and hallucination detection.

Hybrid RAG pipeline that indexes documents and exposes them via an MCP server, enabling natural language queries to retrieve relevant context chunks for LLMs.