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"A tool for analyzing Indian stock data for investment decisions using LLMs and news insights" matching MCP servers:

  • llms-txt-mcp

    tenequm
    A
    license
    -
    quality
    C
    maintenance
    Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.
    Last updated
    3
    MIT

  • stock-rag-mcp

    hemanthkumarkp
    F
    license
    -
    quality
    B
    maintenance
    Enables RAG-based querying of local stock company data using a local LLM and vector database, providing tools to ask questions, search raw chunks, and list documents.
    Last updated

  • MCP Server for Milvus

    zilliztech
    A
    license
    -
    quality
    D
    maintenance
    An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.
    Last updated
    237
    Apache 2.0

  • EyeLevel RAG MCP Server

    wannabidr
    F
    license
    -
    quality
    -
    maintenance
    A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.
    Last updated

  • privateGPT MCP Server

    Fujitsu-AI
    A
    license
    -
    quality
    D
    maintenance
    A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.
    Last updated
    6
    MIT

  • Munin AI Memory

    3d-era
    F
    license
    -
    quality
    A
    maintenance
    Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic
    Last updated
    3

  • mcp-lancedb

    adiom-data
    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

  • 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.
    Last updated
    5

  • Memory-MCP

    wb200
    A
    license
    A
    quality
    D
    maintenance
    Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.
    Last updated
    8
    MIT

  • MemAI

    Filipe-Soares-de-Almeida
    A
    license
    A
    quality
    B
    maintenance
    A long-term memory MCP server for AI agents that stores memories (facts, decisions, etc.) in a single SQLite database with hybrid search and full edit history, ensuring consistency across sessions.
    Last updated
    24
    MIT
  • @manasdb/mcp-server by manasdb

    @manasdb/mcp-serverofficial

    manasdb
    A
    license
    -
    quality
    D
    maintenance
    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.
    Last updated
    6
    MIT