"Developing an API using NestJS, Prisma, and PostgreSQL" matching MCP servers:

Enables secure, AI-driven PostgreSQL database administration, observability, and querying with support for extensions like PostGIS and pgvector, connection pooling, and advanced tool filtering.

A production-grade Model Context Protocol server for PostgreSQL. Lets AI agents safely inspect, query, operate, and tune a Postgres database — over 100 tools spanning catalog introspection, query intelligence, natural-language SQL, structural diffs, hybrid search, graph queries, data movement, live ops, and more.

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

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user 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.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Provides fast static embedding, similarity, and search capabilities via MCP tools and an OpenAI-compatible HTTP API using a tiny 16M-parameter English embedding model.

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.

An MCP server for storing and retrieving memories using Qdrant vector search, acting as a semantic memory layer.

An MCP server for neuroanatomically-inspired memory consolidation using Neo4j and semantic search, enabling episodic memory encoding, semantic retrieval, and graph operations.

A nested MCP system that demonstrates server composition by using an orchestrator to manage an internal vector store for semantic search. It enables complex, multi-hop retrieval and reasoning over a knowledge base through an agentic reasoning loop.

Enables AI agents to securely query PostgreSQL with pgvector, DynamoDB, and MongoDB Atlas with Vector Search through a read-only, allowlisted MCP interface.

Persistent memory MCP server for Claude Code — self-hosted, n8n + PostgreSQL + pgvector. Team memory for AI agents with multi-user roles, multi-project namespacing, and hybrid vector + keyword search. No cloud required.

Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.

Provides persistent, semantic memory storage for LLMs across sessions using vector embeddings and FAISS search. Features bio-inspired memory consolidation, intelligent forgetting, and semantic retrieval without API costs.

Semantic memory server for AI agent teams. Stores, searches, and retrieves knowledge across sessions using pluggable vector backends with local ONNX embeddings, exposed as an MCP server.

An MCP server that transforms a local codebase into a queryable, citation-grounded knowledge base using hybrid FAISS and BM25 retrieval, with optional reranking and swappable LLM providers.

Official Kimss MCP server — call your Kimss agents, models, files, and vector stores from Cursor and other MCP clients using your API key.

A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.