# McpDiffusion

> McpDiffusion — inseefrlab-mcpdiffusion. Use this tool when you need to access and analyze INSEE public data with large language models (LLMs). McpDiffusion solves data integration problems by exposing public data to LLMs, taking in data queries and outputting relevant information. It is particularly useful in contexts where French public data needs to be incorporated into AI models, with inputs via API calls and outputs in a format compatible with LLM processing.

Canonical page: https://skillsregistry.net/skills/inseefrlab-mcpdiffusion  
JSON: https://api.skillsregistry.net/v1/skills/inseefrlab-mcpdiffusion

## Description

MCP server exposing INSEE public data to LLMs written in Python

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** Apache-2.0
- **Updated:** 2026-09-24

## Source

- **Source listing:** [GitHub](https://github.com/InseeFrLab/McpDiffusion)

## 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": "inseefrlab-mcpdiffusion"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/inseefrlab-mcpdiffusion` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/inseefrlab-mcpdiffusion/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