LLM Orchestration

MCP Docs RAG

RAG MCP server for documents using Gemini

Status
UnmaintainedNo commits in 544 days.
Last checked
Checked · Oct 10, 2026
License
Open source
Pricing
Free tier · from $0

What it is

A Retrieval-Augmented Generation (RAG) framework that leverages the Gemini model to provide document-based retrieval and generation capabilities, suitable for developers looking to integrate advanced AI-driven content retrieval into their applications.

Provides a flexible, self-hosted RAG solution leveraging the Gemini model, offering developers full control over their AI-driven content retrieval and generation processes.

When it fits — and when it doesn’t

A good fit for

  • Teams looking to integrate advanced document retrieval and generation capabilities into their web or server-side applications using the Gemini model.

  • Developers who need a self-hosted solution for RAG without relying on cloud services.

Look elsewhere if

  • Projects requiring real-time streaming capabilities as this tool is designed for batch processing

  • Teams with limited technical expertise in setting up and maintaining self-hosted solutions

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Performance issues with large-scale deployments

    Known performance bottlenecks when handling large document sets, leading to increased latency

What it does well

  • Integration with Gemini model for document retrieval and generation

  • Self-hosted deployment flexibility

  • Modular architecture for customization

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up MCP Docs RAG

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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MCP Docs RAG — tracked on AI Navigator

Markdown

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