"A tool or MCP to access and aggregate data from various APIs like Facebook API or Google Ads API" matching MCP servers:

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

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

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

A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.

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.

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.

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.

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.

An MCP server for semantic search and retrieval of indexed Slack messages stored in Qdrant using Cohere reranking via AWS Bedrock. It enables users to search through Slack history, retrieve full message threads, and access channel or user statistics through natural language.

Enables AI assistants to semantically search your entire local conversation history from Claude Desktop, ChatGPT and Claude Code, and to retrieve, browse, ingest and report on those conversations. All embeddings run locally, so no cloud, API keys, or data leave your machine.

A multi-functional computing platform server designed to integrate with large language models like Qwen, providing file access, database connections, API integration, and vector database functionality.

A fully-local AI memory system that saves notes, code, and terminal output to a vector database via Ollama and LanceDB, enabling context retrieval from terminal, API calls, or inside Claude Code.

Enables AI agents to access enterprise data from Unity Catalog (vector search, functions, Genie spaces) and perform developer actions in Databricks like managing notebooks and running jobs.

Enables storing and retrieving text in a local vector store using Qdrant, running fully locally with no external database or API key.

Auto-converts FastAPI endpoints into MCP tools for semantic search, data ingestion (text, code, conversations), and knowledge graph operations. Provides dual access through MCP protocol and direct API calls with automatic tool generation from FastAPI routes.

An MCP server that enables searching and retrieving ACL NLP conference papers from a Qdrant vector database using semantic search and structured filters like year, venue, and field of study.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Provides persistent memory for AI coding agents via MCP, enabling teams to share and recall facts across sessions. Automatically captures, classifies, and curates knowledge from supported transcript sources.

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

A local-first personal RAG memory system that turns AI conversation history into a searchable, retrievable knowledge base via MCP, enabling LLMs to semantically search past conversations.