{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_pjms9yqnbs58","handle":"iflow-mcp-zackriya-solutions-mcp-markdown-rag","url":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag","links":{"self":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/record.json","html":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag","markdown":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag","status":"https://wellknown.network/api/v1/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/status","claim":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-zackriya-solutions-mcp-markdown-rag/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-zackriya-solutions-mcp-markdown-rag","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_zackriya-solutions-mcp-markdown-rag","summary":"Markdown based RAG system","description":"<div align=\"center\">\n  <img src=\"docs/banner.png\" alt=\"MCP-Markdown-RAG\" width=\"800\" style=\"border-radius:10px;\"/>\n  <h1>MCP-Markdown-RAG</h1>\n  <p>\n  <img alt=\"GitHub forks\" src=\"https://img.shields.io/github/forks/Zackriya-Solutions/MCP-Markdown-RAG\"/>\n  <img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/Zackriya-Solutions/MCP-Markdown-RAG\">\n  <img alt=\"GitHub last commit\" src=\"https://img.shields.io/github/last-commit/Zackriya-Solutions/MCP-Markdown-RAG\">\n</p>\n<p>\n  <a href=\"LICENSE\">\n    <img src=\"https://img.shields.io/badge/License-Apache%202.0-green\" alt=\"License\" />\n  </a>\n  <img src=\"https://img.shields.io/badge/MCP-Server-blue\"/>\n</p>\n</div>\n\nA **Model Context Protocol (MCP)** server that provides a **local-first RAG engine** for your markdown documents. This server uses a file-based Milvus vector database to index your notes, enabling Large Language Models (LLMs) to perform semantic search and retrieve relevant content from your local files.\n\n> [!NOTE]\n> This project is in active development. The API and implementation are subject to change. We are exploring future enhancements, including a potential port to an Obsidian plugin for seamless vault integration.\n\n## 🎯 Key Features\n\n✅ **Local-First & Private**: All your data is processed and stored locally. Nothing is sent to a third-party service for indexing.\n\n✅ **Semantic Search for Markdown**: Go beyond simple keyword search. Find document sections based on conceptual meaning.\n\n✅ **MCP Compatible**: Integrates with any MCP-supported host application like Claude Desktop, Windsurf, or Cursor.\n\n✅ **Simple Tooling**: Provides two straightforward tools (`index_documents` and `search`) for managing and querying your knowledge base.\n\n## ⚙️ How It Works\n\nThe server operates in two main phases, exposing its functionality through MCP tools.\n\n1.  **Indexing**:\n\n    - The `index_documents` tool is called with a path to your markdown files.\n    - The server reads the documents, splits them into log…","publisher":null,"homepage":null,"repository":null,"version":"0.2.0","license":null,"protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_zackriya-solutions-mcp-markdown-rag","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","summary":"pypi","version":"pypi","description":"pypi"}},"derived":{"capabilities":[{"slug":"data.vector-search","name":"Vector Search","confidence":1,"provenance":"derived"},{"slug":"productivity.notes","name":"Notes & Docs","confidence":0.791,"provenance":"derived"}],"categories":["data","productivity"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_zackriya-solutions-mcp-markdown-rag","registry":"pypi","observedAt":"2026-11-09T11:26:07.402Z","publishedAt":"2026-02-10T06:19:11.871901Z","latestVersion":"0.2.0"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_zackriya-solutions-mcp-markdown-rag","url":"https://pypi.org/project/iflow-mcp_zackriya-solutions-mcp-markdown-rag/","firstSeenAt":"2026-09-09T21:25:20.881Z","fetchedAt":"2026-11-09T11:24:38.754Z","normalizedAt":"2026-11-09T11:24:38.754Z"}]},"firstSeenAt":"2026-09-09T21:25:20.881Z","updatedAt":"2026-11-09T11:26:07.402Z"}