"Polymarket - A Decentralized Information Market" matching MCP servers:

Enables AI agents to conduct local, auditable opportunity discovery by collecting public discussions, tracing claims to exact quotes and source provenance, testing claims with counterevidence, and exporting deterministic evidence packs.

A Model Context Protocol server that exposes over 200+ APIs from API.market as MCP resources, allowing large language models to discover and interact with various APIs through natural language commands.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

Enables AI agents to automate and navigate Naver SmartStore administration by providing screen and selector maps, Talk CS handling, three-step safety approvals, official help-document RAG, and search ad keyword tools.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables AI coding agents to retain and recall project context across sessions through local Markdown storage and hybrid keyword/semantic retrieval, including reusable knowledge cards. It runs offline as a daemon and integrates with MCP-compatible IDEs to continue long-running work without re-explaining decisions or architecture.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

MCP server that provides stock market research tools including real-time quotes, news search, semantic context retrieval, watchlist management, and saving research notes/reports.

Provides semantic search and complete document retrieval by ID through OpenAI's Vector Store API, letting deep research agents access company-specific knowledge and tools.

A Model Context Protocol server that enables AI assistants like Claude to read from, append to, and format text in Google Documents programmatically.

Provides LLM-free search tools across Confluence, JIRA, code, and project knowledge for any MCP client.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Enables AI clients to search a personal corpus, read relevant source passages, and answer questions with traceable evidence from imported sessions, notes, and articles.

A round-robin API key pool for Tavily with automatic health failover and usage tracking, enabling seamless multi-key usage with MCP clients.

A stdio MCP server that provides a single tool to query Gemini with Google Search grounding for current web information, returning synthesized answers with source links.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

A Model Context Protocol server that integrates ChainGPT capabilities into AI agents, allowing users to access crypto news, prices, and market trends.

An MCP server that retrieves and provides AI model information from OpenAI, Anthropic, and Google through a unified interface.

A Model Context Protocol implementation that enables large language models to call external tools (like weather forecasts and GitHub information) through a structured protocol, with visualization of the model's reasoning process.

A Model Context Protocol server that enables interaction with Chop's Setup system, providing an interface to retrieve setup information via a REST API.

A local MCP server that integrates with Claude Desktop, enabling RAG capabilities to provide Claude with up-to-date private information from custom LlamaCloud indices.

Enables AI coding assistants to search and retrieve information from a locally ingested knowledge base using hybrid search, grounded in user-curated documentation.

A Model Context Protocol server that enables AI assistants like Claude to programmatically access financial data from Financial Modeling Prep API, including company profiles, financial statements, metrics, SEC filings, and market data.