tleTabs"] 23:I[85939,["7626","static/chunks/f6ab4fea-4425dae3da7674a3.js","5232","static/chunks/5232-db5923cda78a4960.js","6465","static/chunks/6465-6b79f1dc957ae3f4.js","9388","static/chunks/9388-d1a745fd53a10850.js","9732","static/chunks/9732-8158987224fdfa35.js","1358","static/chunks/1358-b7408a1693b186e2.js","2439","static/chunks/app/%5Blocale%5D/(default)/tag/%5Bname%5D/page-e01bef69b5169e46.js"],"default"] 26:I[41664,["7626","static/chunks/f6ab4fea-4425dae3da7674a3.js","5232","static/chunks/5232-db5923cda78a4960.js","6465","static/chunks/6465-6b79f1dc957ae3f4.js","9388","static/chunks/9388-d1a745fd53a10850.js","9732","static/chunks/9732-8158987224fdfa35.js","1358","static/chunks/1358-b7408a1693b186e2.js","2439","static/chunks/app/%5Blocale%5D/(default)/tag/%5Bname%5D/page-e01bef69b5169e46.js"],"Pagination"] 24: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 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 t

#json-templates

Custom Context MCP Server