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"Understanding Prompts or Prompt Engineering" matching MCP servers:

  • Memory Engineering MCP

    romiluz13
    A
    license
    -
    quality
    D
    maintenance
    Provides persistent memory and semantic code understanding for AI assistants using MongoDB Atlas Vector Search. Enables intelligent code search, memory management, and pattern detection across codebases with complete semantic context preservation.
    12
    13
    MIT

  • Prompt Learning MCP Server

    curiositech
    F
    license
    -
    quality
    D
    maintenance
    Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.
    4
  • Tribal by tribal-memory

    Tribalofficial

    tribal-memory
    F
    license
    A
    quality
    A
    maintenance
    Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
    10
    9

  • job-search-mcp

    jsundquist
    F
    license
    A
    quality
    B
    maintenance
    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.
    3

  • slack-indexed

    KanvaBhatia-Alaan
    F
    license
    A
    quality
    D
    maintenance
    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.
    5
  • io.github.labacacia/banyan-memory by labacacia

    io.github.labacacia/banyanofficial

    labacacia
    A
    license
    -
    quality
    A
    maintenance
    An offline-first, governed memory and knowledge server for AI agents that provides Remember, Search, Update, and Forget operations with hybrid retrieval, semantic embeddings, and NID-based authentication. It can be used as an MCP server via stdio or Streamable HTTP, enabling agents to persist and query memories and wiki knowledge.
    Apache 2.0

  • Tea Rags MCP

    artk0de
    A
    license
    -
    quality
    A
    maintenance
    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.
    685
    11
    MIT

  • AgenticRAG MCP Server

    aibozo
    A
    license
    -
    quality
    D
    maintenance
    An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.
    2
    MIT

  • LangChain Anthropic MCP Server

    spencer-life
    A
    license
    -
    quality
    -
    maintenance
    Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.

  • mcp-rag-mini

    Pinger1456
    A
    license
    -
    quality
    B
    maintenance
    A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.
    MIT

  • rag-mcp-server

    weitingyen168
    A
    license
    -
    quality
    C
    maintenance
    RAG document search MCP server that allows AI assistants to search a local document set and retrieve grounded passages via keyword (SQLite FTS5) or semantic (Chroma) backends.
    MIT