"RAG (Retrieval-Augmented Generation) MCP Integration for ChatGPT" matching MCP servers:

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

Enables execution of Snowflake stored procedures through the MCP server by providing a framework to define, manage, and call procedures from specific schemas or individual procedures.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

A privacy-preserving local RAG system integrated with MCP, enabling natural language queries over ingested documents and a SQLite database through vector search and local database tools.

Connects to a PostgreSQL database (pgvector) to perform semantic retrieval via the search_documents tool, returning raw document snippets for LLM synthesis.

Enables authenticated organization members to submit, query, and manage schema-validated structured records directly from ChatGPT, with membership requests and approval workflows gating access. All operations are tenant-isolated, permission-checked, and audited, so records are only reachable by approved members of the owning organization.

Read-only MCP server that exposes ZAP's RAG search modes (plain, hybrid, hybrid_rerank) for agents to perform semantic code retrieval.

Enables RAG (Retrieval-Augmented Generation) with tools for vector search, document access, and OpenAI integration, plus MySQL storage and file system operations via MCP.

Enables AI assistants to search NHTSA recall documents and query governed dbt analytics via validated, read-only text-to-SQL, returning citable evidence and data freshness without granting database or file access.

Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.

A private MCP server that converts natural-language questions into embeddings and performs semantic retrieval over document chunks stored in BigQuery, returning structured passages with source and page metadata. It serves as the read-only retrieval layer for a document-grounded chatbot.

Enables read-only document retrieval and SQLite exploration through tools over stdio, enforcing a safety gate before SQL execution.

A comprehensive Model Context Protocol (MCP) server providing advanced access to the UniProt protein database.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

A safety-gated Python MCP server for Metabase that lets MCP clients and agents inspect Metabase, run governed queries, and create or maintain dashboards, cards/questions, collections, snippets, permissions, and other Metabase assets through the Metabase REST API.

Enables integration with financial transaction data through REST APIs, PostgreSQL databases, and document storage systems. Demonstrates agentic AI capabilities by connecting to Alpha Vantage API and managing financial data through natural language interactions.

Enables exploring Microsoft Fabric / Power BI workspaces and semantic models, and executing ad-hoc DAX queries.

A Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

MCP server enabling AI agents to programmatically connect to and control Apache Superset instances, allowing users to manage dashboards, charts, databases, datasets, and run SQL queries through natural language interactions.

Enables integration with OpenMetadata by wrapping its REST API for standardized interaction through the Model Context Protocol.