Skip to main content
Glama
61,048 servers. Last updated

Matching MCP tools:

Matching MCP Connectors:

"Screenshot or Screen Capture" matching MCP servers:

  • F
    license
    A
    quality
    B
    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.
    Last updated
    10
    8
  • A
    license
    A
    quality
    A
    maintenance
    Local-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.
    Last updated
    23
    12
    Apache 2.0
  • 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.
    Last updated
    5
  • A
    license
    A
    quality
    A
    maintenance
    An MCP server that provides persistent semantic memory for LLMs by building a concept graph with vector search. It enables storing, linking, and retrieving concepts across conversations using Turso vector search and 256-dimensional embeddings.
    Last updated
    22
    6
    PolyForm Noncommercial 1.0.0
  • A
    license
    B
    quality
    A
    maintenance
    XMemo is a secure, user-owned memory substrate and context engine for AI agents, CLIs, IDEs, and LLM workspaces. Exposed over Streamable HTTP MCP, it empowers agents with cross-session memory, task continuity, and personalized context. Key Features: * Personalized Context: Stores and recalls developer preferences, project guidelines, and coding patterns via semantic vector search. * Agent Daily Me
    Last updated
    20
    480
    12
    MIT
  • 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.
    Last updated
    369
    10
    MIT
  • A
    license
    -
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
    Last updated
    6
    135
    Apache 2.0
  • A
    license
    -
    quality
    D
    maintenance
    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.
    Last updated
    12
    79
    MIT
  • A
    license
    -
    quality
    C
    maintenance
    Enables seamless integration with Weaviate vector databases, providing tools for semantic, keyword, and hybrid search across local or cloud instances. It supports schema management, collection retrieval, and multi-tenancy configurations through the Model Context Protocol.
    Last updated
    5
    MIT
  • A
    license
    -
    quality
    -
    maintenance
    Provides AI coding agents with persistent, long-term memory through local semantic search and SQLite storage. It enables agents to save and retrieve architectural decisions or project context across different conversation sessions without requiring cloud services.
    Last updated
  • A
    license
    -
    quality
    D
    maintenance
    Provides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.
    Last updated
    1
    MIT