Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    MCP Protocol Architect — Production Data Bridge System

    1

    Architect and deploy production-ready MCP servers with full JSON-RPC 2.0 compliance and secure tool definitions.

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    See it in action

    You say

    Build a Node.js MCP for the Stripe API to fetch customer data. Security: API Key. Expert level, Lite Mode, English.

    Your agent does

    --- PHASE 1: Architecture & Setup --- 1. TITLE: Stripe Customer Data MCP 2. RISK CLASSIFICATION: CATEGORY B (Financial) - Mandatory Human-in-the-Loop for write actions. 3. ARCHITECTURE: [Mermaid Diagram] ... 🟢 PHASE 1 COMPLETE. Type [C] to continue to Phase 2.

    What you get

    Generate complete MCP servers without placeholders or elided code blocks.Map external APIs to LLM-accessible tools and read-only resources.Visualize system architecture with automated Mermaid.js diagrams.Implement secure JSON-RPC 2.0 communication for AI tool-calling.

    About this skill

    The problem

    Building production-ready Model Context Protocol (MCP) servers often results in shallow implementations with brittle error handling, missing schema validations, and security gaps. Developers waste hours boilerplate-ing JSON-RPC 2.0 structures instead of focusing on data integration.

    What it does

    • Generates full-stack MCP server implementations using Standard I/O or Server-Sent Events (SSE).
    • Architects specific 'Resources' for read-only data and 'Tools' for executable functions based on your API docs.
    • Enforces strict security protocols, including mandatory human-in-the-loop triggers for high-risk operations.
    • Produces complete, functional code without lazy placeholders or elided logic.
    • Includes Mermaid.js diagrams for system architecture and data security flows.

    Frameworks & tools

    Supports Node.js, Python, and any tech stack compatible with the MCP SDK. Utilizes JSON-RPC 2.0, OAuth2, and SQL integrations.

    Why this beats prompting it yourself

    Standard LLM prompts often hallucinate protocol specs or skip complex middleware. This skill uses a phased execution protocol to manage context window limits, ensuring every line of code from auth layers to connection pooling is production-grade and fully realized.

    Use cases

    • Connect enterprise SQL databases to LLMs with read-only resource mapping.
    • Build GitHub or Jira bridges for automated issue management and repo analysis.
    • Deploy secure API wrappers with OAuth2 authentication for internal tools.
    • Create local filesystem agents for controlled code execution and file manipulation.

    Known limitations

    Requires the user to provide specific API documentation or data schemas for accurate tool definition.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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