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"Parsing and Unparsing CSV Files for AI Context Use" matching MCP servers:

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    A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.
    7
    MIT
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    A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.
    15
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    MIT
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    Enables Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.
    16
    1
    MIT
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    Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.
    765
    Apache 2.0
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    Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.
    1
    MIT
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    An MCP server that enables semantic search over local files or GitHub repositories by indexing content into a serverless vector database, allowing AI assistants to understand meaning rather than just keywords.
    24
    MIT
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    Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.
    10
    MIT
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    Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.
    6
    3
    MIT
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    A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.
    2
    MIT
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    A CloudFlare Workers-based MCP server that provides semantic memory and journal capabilities with vector search. Enables users to store, search, and retrieve memories and journal entries using AI-powered semantic similarity without any local setup required.
  • F
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    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
    3
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    Enables storing, querying, and managing decision traces with semantic search using Voyage AI embeddings and ChromaDB. Supports outcome tracking and category filtering for software development decisions.
  • F
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    A local MCP server for RAG memory, semantic search, and context optimization using Ollama and SQLite. It serves as a central hub that manages document embeddings, text compression, and proxies calls to other sub-MCP servers.