# flet-mcp-server

> Use this tool when you need to dynamically access and search Flet documentation, discover ecosystem packages, or retrieve package details, as it provides an auto-updating Model Context Protocol (MCP) server with features like GitHub tree sync, intelligent caching, and AI-optimized tool definitions. It accepts queries and doc paths as inputs and returns relevant documentation, package lists, and details as outputs. Use it in contexts where you require fast and accurate information about Flet UI controls, documentation, and ecosystem packages.

Canonical page: https://skillsregistry.net/skills/nwokikeonyeka-flet-mcp-server  
JSON: https://api.skillsregistry.net/v1/skills/nwokikeonyeka-flet-mcp-server

## Description

# Flet MCP Server

[![PyPI Version](https://img.shields.io/pypi/v/flet-mcp-server)](https://pypi.org/project/flet-mcp-server/)
[![PyPI Downloads](https://static.pepy.tech/badge/flet-mcp-server)](https://pepy.tech/project/flet-mcp-server)
![Python Versions](https://img.shields.io/pypi/pyversions/flet-mcp-server)
![License](https://img.shields.io/github/license/Nwokike/flet-mcp-server)

An auto-updating Model Context Protocol (MCP) server that dynamically fetches, caches, and serves the official Flet documentation and ecosystem packages directly from GitHub and PyPI.

## Features

*   **GitHub Tree Sync**: Maps documentation in real-time.
*   **Intelligent Caching**: Uses `diskcache` for fast responses.
*   **Ecosystem Discovery**: Finds and verifies official and community Flet packages.
*   **AI-Optimized**: Tool definitions designed for LLM understanding.

## Tools Included

### 1. `list_flet_controls`
List all available Flet UI controls.

### 2. `search_flet_docs(query)`
Search the documentation index.

### 3. `get_flet_doc(doc_path)`
Get raw Markdown for a specific doc.

### 4. `list_official_packages()`
List official Flet extension packages.

### 5. `search_flet_ecosystem(query)`
Search for verified community Flet components.

### 6. `get_package_details(package_name)`
Fetch version and installation info from PyPI.

## Client Configuration Examples

### 🌌 Antigravity / Cascade
Add this to your `mcp_config.json`:

```json
{
  "mcpServers": {
    "flet-mcp-server": {
      "command": "uvx",
      "args": ["flet-mcp-server"]
    }
  }
}
```

### 🤖 Claude Desktop
Add this to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "flet-mcp-server": {
      "command": "uvx",
      "args": ["flet-mcp-server"]
    }
  }
}
```

### 💻 Cursor / Windsurf
In your IDE's MCP settings, add a new server:
- **Name**: Flet MCP
- **Type**: Command
- **Command**: `uvx flet-mcp-server`

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** version-control
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/nwokikeonyeka/flet-mcp-server)
- **Repository:** <http://github.com/nwokike/flet-mcp-server/>

## 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": "nwokikeonyeka-flet-mcp-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/nwokikeonyeka-flet-mcp-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/nwokikeonyeka-flet-mcp-server/pull`

---
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