me":"twitter:image","content":"https://mcp.so/logo.png"}]],"error":null,"digest":"$undefined"} e:{"metadata":"$14:metadata","error":null,"digest":"$undefined"} 28:I[60719,["7626","static/chunks/f6ab4fea-4425dae3da7674a3.js","8548","static/chunks/b27dc69b-e5acb70cd9d66ddf.js","5232","static/chunks/5232-db5923cda78a4960.js","6465","static/chunks/6465-6b79f1dc957ae3f4.js","9388","static/chunks/9388-d1a745fd53a10850.js","4902","static/chunks/4902-40271c136eb736f1.js","1508","static/chunks/1508-abbc58115cbb4fd0.js","6125","static/chunks/6125-6f7cbc86df4f2503.js","7771","static/chunks/7771-66979ce602088329.js","7660","static/chunks/7660-f5d56ac6a9ee6cc8.js","4147","static/chunks/4147-ef3df161a800b88b.js","9783","static/chunks/9783-e1d5b54e9718b38b.js","8084","static/chunks/8084-f1229cc5d0501fe0.js","2024","static/chunks/2024-e03e13f5faa34f45.js","7796","static/chunks/app/%5Blocale%5D/(default)/server/%5Bname%5D/%5Bauthor_name%5D/page-c3a3facf548ee943.js"],"default"] 29:T11c8,# Custom Context MCP Server This Model Context Protocol (MCP) server provides tools for structuring and extracting data from text according to JSON templates. ## Features ### Text-to-JSON Transformation - Group and structure text based on JSON templates with placeholders - Extract information from AI-generated text into structured JSON formats - Support for any arbitrary JSON structure with nested placeholders - Intelligent extraction of key-value pairs from text - Process AI outputs into structured data for downstream applications ## Getting Started ### Installation ```bash npm install ``` ### Running the server ```bash npm start ``` For development with hot reloading: ```bash npm run dev:watch ``` ## Usage This MCP server provides two main tools: ### 1. Group Text by JSON (`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

Custom Context MCP Server

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