"Using a terminal like PowerShell or Git Bash" matching MCP servers:

A composable semantic memory layer that provides cross-project recall and session context using Qdrant and OpenAI embeddings. It enables users to securely store, search, and manage persistent memories with built-in secret scrubbing for privacy.

A persistent, PostgreSQL-backed repository management system and semantic memory MCP server that stores the entire Git DAG natively in PostgreSQL with temporally-decayed semantic vector embeddings for AI IDEs.

A semantic memory store for AI agents, enabling persistent memory across sessions via MCP tools with semantic search, scoped access, and 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.

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.

A MCP server for document ingestion and semantic search using ChromaDB and sentence transformers, enabling users to query natural language over ingested documents.

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.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

Python MCP server for vector search using Qdrant vector database and Ollama embeddings, with advanced query techniques like query expansion, HyDE, and reranking.

Enables semantic code search across local projects and Git repositories using AI embeddings with ChromaDB. Supports both OpenAI and local Ollama models for private, enterprise-ready code analysis and similar code discovery.

Hardware-accelerated codebase mapping that indexes Git repositories into Postgres/pgvector and serves code search, relationships, and static analysis results via a stdio MCP server.

Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.

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

A high-performance MCP server for semantic search and codebase indexing using the Qdrant vector database. It features optimized embedding pipelines, AST-aware chunking, and git metadata enrichment for fast, privacy-focused local or remote search.

MCP Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

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.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

Provides semantic search capabilities using Qdrant vector database with multiple embedding providers, including hybrid search, code indexing, and git history search. Adds optional time-based recency scoring to search results.

A local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.

Enables semantic search over a Qdrant knowledge base through MCP, with optional domain filtering and stdio or Streamable HTTP transport.