"US Government Open Data API Aggregator (FEC, SEC, Congress, Lobbying, USAspending)" matching MCP servers:

MCP server for SEC EDGAR filing intelligence, providing tools for fetching, chunking/embedding, retrieval, and evaluation of filings.

Stores what AI agents learn — thoughts, decisions, session events and repo facts — in PostgreSQL with pgvector and serves it back through versioned MCP tools with hybrid vector-plus-full-text retrieval and budgeted context packs. Enables multiple agents to share one scoped, role-isolated long-term memory, with deliberate promotion of distilled candidates into durable memories.

A self-hosted second brain MCP server that enables capturing thoughts with deduplication and semantic search using local embeddings and PostgreSQL with pgvector, all running on your own hardware.

Open-source MCP server for mem0 that enables memory storage and retrieval using local LLMs, self-hosted, and Docker-free.

A remote MCP server enabling semantic memory across multiple AI platforms like Perplexity Comet and Claude Desktop, with vector embeddings for intelligent retrieval.

Enables unified access to SQL, semantic vector search, and columnar analytics through a single MCP interface.

Provides a unified MCP interface that proxies requests to multiple backend servers including memory/knowledge graph and vector database services. Enables seamless access to distributed MCP tools through a single endpoint with automatic routing, health monitoring, and retry logic.

Enables managing knowledge bases in Open WebUI and retrieving relevant fragments from Qdrant-stored knowledge through an MCP client.

Indexes local files (PDF, TXT, CSV, Markdown) with embeddings for semantic search. Provides both CLI and MCP server interfaces so Claude Desktop can search and read your local documents.

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

Enables semantic search over OpenAPI endpoint catalogs using FAISS and LangGraph, returning grounded technical recommendations for natural-language API queries.

Enables stateless similarity search over pre-computed vector corpora using NMI and cosine fusion with entropy-calibrated weighting. Provides tools for ranking, scoring, outlier detection, and alpha calibration for AI agents.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

Enables Claude to scrape, crawl, extract data, and sync to vector databases using scrapedatshi's RAG pipeline, with support for multiple embedding and vector DB providers.

Enables MCP clients to ingest invoice PDFs into a hybrid-chunked FAISS index, semantically search them, and extract typed invoice data with deterministic arithmetic validation. It also provides listed-company financials and current web search results so agents can research vendors.

Enables answering Formula 1 FAQ questions via vector search and web search using Bright Data.

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

Lets AI assistants connect to Apache Solr deployments to enumerate collections and schemas, run full-text and filtered queries, apply faceting and sorting, and issue SQL or vector-semantic searches without building custom API glue. Communicates over stdio or SSE and requires minimal environment configuration instead of app-specific credentials or mandatory ZooKeeper wiring.

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

Enables making REST API calls to Teradata cloud services including Elastic Compute, Vector Store, OMS, and QueryGrid. Supports custom authentication, Socks5 proxy, and multipart file uploads.

Local-first memory for Claude Code and any MCP client: hybrid vector + keyword search and a bi-temporal knowledge graph in one SQLite file. Local embeddings, no API key, $0/token.

A Model Context Protocol server for Chroma, enabling AI models to create collections and retrieve data using vector search, full text search, and metadata filtering.

An MCP server that streams K-line (candlestick) data from decentralized exchanges (DEXs) to power your AI agents and workflows.

An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.

Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.

Enables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).

Enables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.

The official Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis.

Connects AI clients to MindsDB via the MySQL protocol to execute SQL queries, manage databases, and perform semantic searches within knowledge bases. It enables automated workflows through job scheduling and provides seamless integration with external data sources.