Fusion MCP Server
# Fusion MCP Server
A Model Context Protocol (MCP) server that provides enhanced AI interactions through fusion algorithms and data transformation capabilities.
## Features
- **Data Analysis**: Analyze data using statistical, machine learning, or hybrid methods
- **Data Transformation**: Transform data between different formats using fusion techniques
- **MCP Compatible**: Fully compatible with Model Context Protocol specification
## Installation
```bash
npm install
```
## Usage
Start the server:
```bash
npm start
```
For development with auto-reload:
```bash
npm run dev
```
## Available Tools
### fusion_analyze
Analyze data using various fusion algorithms.
**Parameters:**
- `data` (required): Data to analyze
- `method` (optional): Analysis method - "statistical", "ml", or "hybrid"
### fusion_transform
Transform data using fusion techniques.
**Parameters:**
- `input` (required): Input data to transform
- `target_format` (required): Target format for transformation
## Development
Run linting:
```bash
npm run lint
```
Format code:
```bash
npm run format
```
Run tests:
```bash
npm test
```
## License
MIT
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'fusion_analyze' is for analysis, while 'fusion_transform' is for transformation. There is no overlap or ambiguity between these operations, making it easy for an agent to select the correct tool based on the task.
Both tools follow a consistent 'fusion_' prefix with a verb_noun pattern ('analyze' and 'transform'), using snake_case throughout. This predictable naming scheme enhances readability and reduces confusion for agents.
With only 2 tools, the server feels thin and under-scoped for a domain like 'fusion' which typically involves complex data operations. A more complete surface would likely include additional tools for tasks like data ingestion, validation, or visualization, making this count borderline inadequate.
The tool set is severely incomplete for a data fusion domain. While analysis and transformation are core functions, there are obvious gaps such as tools for data loading, cleaning, merging, or output generation. This limited coverage may lead to agent failures when handling end-to-end workflows.