N (`group-text-by-json`) This tool takes a JSON template with placeholders and generates a prompt for an AI to group text according to the template's structure. ```json { "template": "{ \"type\": \"\", \"text\": \"\" }" } ``` The tool analyzes the template, extracts placeholder keys, and returns a prompt that guides the AI to extract information in a key-value format. ### 2. Text to JSON (`text-to-json`) This tool takes the grouped text output from the previous step and converts it into a structured JSON object based on the original template. ```json { "template": "{ \"type\": \"\", \"text\": \"\" }", "text": "type: pen\ntext: This is a blue pen" } ``` It extracts key-value pairs from the text and structures them according to the template. ## Example Workflow 1. **Define a JSON template with placeholders:** ```json { "item": { "name": "", "price": "", "description": "" } } ``` 2. **Use `group-text-by-json` to create a prompt for AI:** - The tool identifies placeholder keys: name, price, description - Generates a prompt instructing the AI to group information by these keys 3. **Send the prompt to an AI model and receive grouped text:** ``` name: Blue Pen price: $2.99 description: A smooth-writing ballpoint pen with blue ink ``` 4. **Use `text-to-json` to convert the grouped text to JSON:** - Result: ```json { "item": { "name": "Blue Pen", "price": "$2.99", "description": "A smooth-writing ballpoint pen with blue ink" } } ``` ## Template Format Templates can include placeholders anywhere within a valid JSON structure: - Use angle brackets to define placeholders: ``, ``, ``, etc. - The template must be a valid JSON string - Placeholders can be at any level of nesting - Supports complex nested structures Example template with nested placeholders: ```json { "product": { "details": { "name": "", "category": "" }, "pricing": { "amount": "", "currency": "USD" } }, "metadata": { "timestamp": "2023-09-01T12:00:00Z" } } ``` ## Implementation Details The server works by: 1. Analyzing JSON templates to extract placeholder keys 2. Generating prompts that guide AI models to extract information by these keys 3. Parsing AI-generated text to extract key-value pairs 4. Reconstructing JSON objects based on the original template structure ## Development ### Prerequisites - Node.js v18 or higher - npm or yarn ### Build and Run ```bash # Install dependencies npm install # Build the project npm run build # Run the server npm start # Development with hot reloading npm run dev:watch ``` ### Custom Hot Reloading This project includes a custom hot reloading setup that combines: - **nodemon**: Watches for file changes in the src directory and rebuilds TypeScript files - **browser-sync**: Automatically refreshes the browser when build files change - **Concurrent execution**: Runs both services simultaneously with output synchronization The setup is configured in: - `nodemon.json`: Controls TypeScript watching and rebuilding - `package.json`: Uses concurrently to run nodemon and browser-sync together To use the custom hot reloading feature: ```bash npm run dev:watch ``` This creates a development environment where: 1. TypeScript files are automatically rebuilt when changed 2. The MCP server restarts with the updated code 3. Connected browsers refresh to show the latest changes ### Using with MCP Inspector You can use the MCP Inspector for debugging: ```bash npm run dev ``` This runs the server with the MCP Inspector for visual debugging of requests and responses.2a:T5df,## What is Custom Context MCP Server? The Custom Context MCP Server is a Model Context Protocol (MCP) server designed to provide tools for structuring and extracting data from text according to JSON templates. ## How to use Custom Context MCP Server? To use the server, install the necessary dependencies with `npm install`, then

Custom Context MCP Server

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