io.github.ndjordjevic/pinrag

pinrag

MCP RAG: index PDFs, repos, YouTube, Discord, text; optional YouTube vision; query with citations.

2 11 tools GitHub PyPI

No known CVEs

MIT license

Maintained

Last commit 108d ago

Works with most clients

Transport: stdio, sse, http

11 tools

Grade F

MCPpedia last refreshed this data 7h ago

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Step 1

Install in your client

Config is the same across clients — only the file and path differ.

Supported in Claude Desktopstdio, sse, http · Node 18+

Paste into ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "pinrag": {
      "env": {
        "OPENROUTER_API_KEY": "your-openrouter-api-key-here",
        "PINRAG_PERSIST_DIR": "/absolute/path/to/your/pinrag-data"
      },
      "args": [
        "--refresh",
        "pinrag"
      ],
      "command": "uvx"
    }
  }
}

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What io.github.ndjordjevic/pinrag does

PinRAG is for when you want to learn about something and your materials are scattered—PDFs and ebooks, GitHub repos, YouTube videos, Discord discussions, and plain notes. You index those materials into one shared RAG index, then ask questions from Cursor, VS Code (GitHub Copilot), or any MCP-capable assistant and get answers with citations pointing back to pages, timestamps, files, or threads.

Test This Server

Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.

uvx 'pinrag' 2>&1 | head -1 && echo "✓ Server started successfully"

After testing, let us know if it worked:

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Scored, not listed

Why this score

Five weighted categories — click any category to see the underlying evidence.

Score breakdown

48/100across 5 weighted dimensions

How we score →

0255075100

−52

Security

Maintenance

Efficiency

Documentation

Compatibility

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Security

OSV.dev

No known CVEs.

Checked pinrag against OSV.dev.

Inventory

Tools (11)

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Community

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How easy was setup?Did it work reliably?How was the documentation?

Frequently Asked Questions

Is io.github.ndjordjevic/pinrag safe to use?: io.github.ndjordjevic/pinrag has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.
How do I install io.github.ndjordjevic/pinrag?: io.github.ndjordjevic/pinrag supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.
What can io.github.ndjordjevic/pinrag do?: io.github.ndjordjevic/pinrag provides 11 tools: servers, paths, anthropic, openrouter, PINRAG_PERSIST_DIR and 6 more. See the full tools list on the server page for descriptions and parameters.
What AI clients work with io.github.ndjordjevic/pinrag?: io.github.ndjordjevic/pinrag is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.
Is io.github.ndjordjevic/pinrag actively maintained?: io.github.ndjordjevic/pinrag is less actively maintained — last commit was 108 days ago. It has 2 GitHub stars.

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Community

Discussion

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Has anyone used this with Cursor?How do you handle auth?Any alternatives?

{ "mcpServers": { "pinrag": { "env": { "OPENROUTER_API_KEY": "your-openrouter-api-key-here", "PINRAG_PERSIST_DIR": "/absolute/path/to/your/pinrag-data" }, "args": [ "--refresh", "pinrag" ], "command": "uvx" } } }

Overview

PinRAG is for when you want to learn about something and your materials are scattered—PDFs and ebooks, GitHub repos, YouTube videos, Discord discussions, and plain notes. You index those materials into one shared RAG index, then ask questions from Cursor, VS Code (GitHub Copilot), or any MCP-capable assistant and get answers with citations pointing back to pages, timestamps, files, or threads.

Under the hood it is Retrieval-Augmented Generation built with LangChain and exposed as an MCP (Model Context Protocol) server: add documents from the editor, query with natural language, list or remove what you indexed. Supported inputs include PDFs, local text files and directories, Discord exports, YouTube (transcript from URL, playlist, or ID), and GitHub repo URLs. For YouTube you can optionally add vision so on-screen code, diagrams, and UI text are merged with the transcript in the same chunks—see YouTube vision enrichment.

Features

Multi-format indexing — PDF (.pdf), local files or directories, plain text (.txt), Discord export (.txt), YouTube (video or playlist URL, or video ID), GitHub repo (URL), web documentation sites (URL)

Optional YouTube vision — Off by default. When enabled, runs a vision model (OpenAI, Anthropic, or OpenRouter native video) and merges structured on-screen context with the transcript so RAG chunks carry searchable code names, labels, and diagrams—not speech alone. OpenRouter mode avoids local ffmpeg/video download; openai/anthropic use scene keyframes and require pinrag[vision] + ffmpeg (see YouTube vision enrichment)

RAG with citations — Answers cite source context: PDF page, YouTube timestamp, document name for plain text and Discord, chunk index for GitHub repos, source URL for web documentation

Document tags — Tag documents at index time (e.g. AMIGA, PI_PICO) for filtered search

Metadata filtering — query_tool supports document_id, tag, document_type, PDF page_min/page_max, and response_style (thorough or concise)

MCP tools — add_document_tool, query_tool, list_documents_tool, remove_document_tool, set_document_tag_tool, list_collections_tool; optional collection on tools overrides PINRAG_COLLECTION_NAME for that call

MCP resources — pinrag://documents (indexed documents) and pinrag://server-config (env vars and config); click in Cursor’s MCP panel to view

MCP prompt — use_pinrag (parameter: request) for querying, indexing, listing, or removing documents

Configurable LLM — OpenRouter (default, free openrouter/free router), OpenAI, Anthropic, or Cerebras Inference (OpenAI-compatible API); set via PINRAG_LLM_PROVIDER and PINRAG_LLM_MODEL in MCP env or your shell

Local embeddings — Nomic (PINRAG_EMBEDDING_MODEL, default nomic-embed-text-v1.5); no API key; first run downloads model weights (~270 MB, cached)

Retrieval & chunking options — Structu

Frequently Asked Questions

Is io.github.ndjordjevic/pinrag safe to use?

io.github.ndjordjevic/pinrag has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments. Licensed under MIT.

How do I install io.github.ndjordjevic/pinrag?

io.github.ndjordjevic/pinrag supports copy-paste install configs on its MCPpedia page for Claude Desktop, Cursor, and Claude Code. Scroll to the Quick Install section and select your client.

What can io.github.ndjordjevic/pinrag do?

io.github.ndjordjevic/pinrag provides 11 tools: servers, paths, anthropic, openrouter, PINRAG_PERSIST_DIR and 6 more. See the full tools list on the server page for descriptions and parameters.

What AI clients work with io.github.ndjordjevic/pinrag?

io.github.ndjordjevic/pinrag is compatible with claude-desktop, cursor, claude-code. It uses stdio and sse and http transport.

Is io.github.ndjordjevic/pinrag actively maintained?

io.github.ndjordjevic/pinrag is less actively maintained — last commit was 108 days ago. It has 2 GitHub stars.

Overview

Features

RAG with citations — Answers cite source context: PDF page, YouTube timestamp, document name for plain text and Discord, chunk index for GitHub repos, source URL for web documentation

Document tags — Tag documents at index time (e.g. AMIGA, PI_PICO) for filtered search

Metadata filtering — query_tool supports document_id, tag, document_type, PDF page_min/page_max, and response_style (thorough or concise)

MCP resources — pinrag://documents (indexed documents) and pinrag://server-config (env vars and config); click in Cursor’s MCP panel to view

MCP prompt — use_pinrag (parameter: request) for querying, indexing, listing, or removing documents

Local embeddings — Nomic (PINRAG_EMBEDDING_MODEL, default nomic-embed-text-v1.5); no API key; first run downloads model weights (~270 MB, cached)

Retrieval & chunking options — Structu