# Gemini File Search

> Gemini File Search — node2flow-gemini-file-search-rag. Use this tool when you need to efficiently search and manage large collections of documents, or generate text based on existing documents using Retrieval-Augmented Generation (RAG). It solves problems such as document retrieval, information extraction, and text generation by providing a robust interface for uploading, querying, and managing documents with custom metadata. The tool accepts document uploads, metadata, and query inputs, and outputs relevant search results and generated text.

Canonical page: https://skillsregistry.net/skills/node2flow-gemini-file-search-rag  
JSON: https://api.skillsregistry.net/v1/skills/node2flow-gemini-file-search-rag

## Description

MCP server for Google's Gemini File Search and RAG (Retrieval-Augmented Generation).

  ## Features
  - Create and manage File Search stores
  - Upload documents (text, PDF, base64) with custom metadata and chunking config
  - Import existing Gemini files into stores
  - Track upload/operation status
  - Query documents using RAG with model selection and metadata filtering

  ## 12 Tools

  **Store Management**: create, list, get, delete stores
  **Upload & Import**: upload content directly or import existing files
  **Operations**: check status of store/upload operations
  **Documents**: list, get, delete documents in stores
  **RAG Query**: query documents with Gemini models (supports metadata filters)

  ## Configuration
  - GEMINI_API_KEY — Get one at https://aistudio.google.com/apikey

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/node2flow/gemini-file-search-rag)
- **Repository:** <https://github.com/node2flow-th/gemini-files-search-rag-mcp-community>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "node2flow-gemini-file-search-rag"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/node2flow-gemini-file-search-rag` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/node2flow-gemini-file-search-rag/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt