Image Processor MCP Server
# image-processor MCP Server
A Model Context Protocol server
This is a TypeScript-based MCP server that implements a simple notes system. It demonstrates core MCP concepts by providing:
- Resources representing text notes with URIs and metadata
- Tools for creating new notes
- Prompts for generating summaries of notes
## Features
### Resources
- List and access notes via `note://` URIs
- Each note has a title, content and metadata
- Plain text mime type for simple content access
### Tools
- `create_note` - Create new text notes
- Takes title and content as required parameters
- Stores note in server state
### Prompts
- `summarize_notes` - Generate a summary of all stored notes
- Includes all note contents as embedded resources
- Returns structured prompt for LLM summarization
## Development
Install dependencies:
```bash
npm install
```
Build the server:
```bash
npm run build
```
For development with auto-rebuild:
```bash
npm run watch
```
## Installation
To use with Claude Desktop, add the server config:
On MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`
```json
{
"mcpServers": {
"image-processor": {
"command": "/path/to/image-processor/build/index.js"
}
}
}
```
### Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is available as a package script:
```bash
npm run inspector
```
The Inspector will provide a URL to access debugging tools in your browser.
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one generates code from images, and the other generates descriptions from images. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the desired output type.
Both tools follow a consistent verb_noun pattern with 'process_image_to_' as a prefix, followed by the specific output type ('code' or 'description'). This naming scheme is predictable and enhances readability across the tool set.
With only two tools, the server feels thin for an 'Image Processor' domain, which might imply broader capabilities like image editing, filtering, or analysis. The limited scope suggests potential gaps in functionality that could hinder agent workflows.
The tool set is severely incomplete for an image processing domain. It lacks basic operations such as image resizing, format conversion, filtering, or analysis tools, focusing only on generation tasks. This creates significant gaps that will likely cause agent failures when broader image processing needs arise.