Ragie
Ingest documents and retrieve context from Ragie so your agent-backed app gets grounded answers without hand-rolling vector pipelines.
Overview
com.mcparmory/ragie is a MCP server for the Build phase that lets your agent ingest, manage, and retrieve Ragie documents for RAG-powered applications over stdio.
What is this MCP server?
- Ingest and manage documents in Ragie for RAG workflows from the agent
- Retrieve document-grounded context for AI application features
- stdio MCP mcparmory-ragie 1.0.2 via uvx or ghcr.io/mcparmory/ragie:1.0.2
- Reduces bespoke chunking and retrieval scripting during agent builds
- Server version 1.0.2 per registry metadata
- 2 packages: PyPI and OCI Docker with stdio
- Repository source github.com/mcparmory/registry
Community signal: 25 GitHub stars.
What problem does it solve?
Standing up document ingestion and retrieval for your agent app means repetitive API glue instead of focusing on product behavior.
Who is it for?
Indie builders using Ragie who want Claude Code or Cursor to manage corpora and retrieval while implementing agent features.
Skip if: Teams that want a fully local vector database with no Ragie subscription or cloud document store.
What do I get? / Deliverables
Your agent can drive Ragie document pipelines so grounded responses ship faster with less custom retrieval code.
- Document ingest and retrieval operations callable from the agent
- RAG integration aligned with your application code
- stdio MCP server configuration for Ragie
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Journey fit
RAG ingestion and retrieval are implemented while you are building agent features and knowledge-backed products, not during early market research alone. Agent-tooling is where you attach memory, retrieval, and document pipelines to the assistants and workflows you ship in code.
How it compares
Ragie RAG MCP connector, not a prompt-engineering or chunking methodology skill.
Common Questions / FAQ
Who is com.mcparmory/ragie for?
Builders creating RAG-backed agents with Ragie who want document ingest and retrieve operations exposed to their MCP coding agent.
When should I use com.mcparmory/ragie?
Use it during Build agent-tooling when you add knowledge bases, sync sources, or debug retrieval for an AI feature.
How do I add com.mcparmory/ragie to my agent?
Register stdio MCP using uvx mcparmory-ragie 1.0.2 or Docker ghcr.io/mcparmory/ragie:1.0.2 and provide your Ragie API key in environment configuration.