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Model Context Protocol (MCP)

website·Indexed 32 days ago

https://mcp.com.ai

Resources and architectural guidance for the Model Context Protocol (MCP), focusing on integrating AI agents with enterprise APIs. Topics include building MCP servers from OpenAPI and Arazzo specs, implementing Zero Trust architectures via the MCP Connect Authority, scaling tools, cost optimization, and avoiding technical debt through the use of stateless servers and the HAPI MCP Registry.

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What's inside MCP

  1. Overview of OrcA for VS Code

    OrcA (Orchestrator for APIs and Agents) is a VS Code extension part of the HAPI MCP Stack. It allows developers to open OpenAPI or Arazzo contracts, preview the MCP tools they generate, run them as local MCP servers using the HAPI CLI, and connect them to AI assistants (like GitHub Copilot or Claude) without leaving the editor.

  2. Overview of HAPI Capability Graph and Capability Planning

    The HAPI Capability Graph and Capability Planning provide structured guidance for AI agents to manage tool selection and execution flow. This addresses the non-deterministic nature of agents by helping them determine which action is appropriate at a given moment, the required sequence of operations (what must happen first), and the appropriate time to stop and request human intervention.

  3. Overview of HAPI MCP Registry

    The HAPI MCP Registry is a centralized, public registry of MCP (Model Context Protocol) servers. It acts as a discovery and execution layer that turns existing APIs into AI-ready tools, allowing AI agents to discover, understand, and safely use real-world capabilities without the need for manual API adaptation or custom integration for every agent.

  4. Overview of the Lenny Rachitsky Podcast Transcripts MCP Server

    The Lenny Rachitsky Podcast Transcripts MCP Server is a solution that allows marketers, product managers, and creators to search, analyze, and repurpose podcast content using natural language queries. It leverages the Model Context Protocol (MCP) to provide a standardized way for AI models to access and extract insights from podcast transcripts.

  5. Overview of OrcA for VS Code

    OrcA is a VS Code extension part of the HAPI MCP Stack. It allows developers to discover OpenAPI (Swagger) and Arazzo contracts, preview their corresponding MCP tools, run those tools locally on a free port, deploy them, and connect them to an AI assistant.

  6. Overview of La Rebelion Labs Services

    La Rebelion Labs provides a suite of developer-first and AI-first tools focused on MCP infrastructure, AI agents, APIs, and automation. Their ecosystem includes:

    • HAPI MCP Stack: Tooling for exposing APIs as MCP-compatible tools, running MCP servers, and integrating AI agents across local, cloud, on-premise, and air-gapped deployments.
    • My Clawster: An enterprise portal for managing agent infrastructure, configurations, plug-ins, workflows, and runtimes.
    • Clawne Me: A user-facing AI assistant for interacting with agents and workflows via simplified interfaces.
    • Educational Resources: Technical articles, labs, templates, and courses available via their websites (rebelion.la, mcp.com.ai, clawne.me, clawster.my).

  7. Overview of MCP at Scale architectural challenges

    As Model Context Protocol (MCP) deployments move from experimental chatbots to production-grade agentic systems, several critical engineering challenges emerge at scale. These include:

    • Token Economics & Efficiency: Managing the cost and latency associated with massive tool contexts to prevent token bloat.
    • Dynamic Discovery: Transitioning from static tool injection to search-driven tool loading to handle large numbers of tools.
    • Governance & Security: Implementing 'Connect Authorities' to prevent Shadow AI and ensure secure access.
    • High-Volume Orchestration: Maintaining performance and stability while handling thousands of concurrent agent sessions.

  8. Implement a hybrid AI and deterministic system architecture

    The most effective production systems use a hybrid design where AI handles intent and ambiguity, while deterministic systems handle execution.

    Key pattern:

    1. AI interprets intent: The AI determines what the user wants.
    2. Deterministic system executes: A predefined workflow defines how it happens.

    Instead of exposing many low-level tools to an LLM, expose a single deterministic outcome (a workflow). The AI triggers the workflow, and the system guarantees the result, reducing token consumption and increasing reliability.

  9. Enterprise AI Architecture Components

    A mature enterprise AI architecture converges toward the following components to move from experimentation to a business operating model:

    • Governed Model Infrastructure: Secure and compliant AI model hosting.
    • Modular Orchestration Layers: Flexible coordination of AI tasks.
    • AI-Native Integration Protocols: Standardized interfaces for agent-to-system communication.
    • Agent-Driven Workflows: Networks of agents that execute workflows, retrieve info, and trigger automations.
    • Business-Accessible Automation Interfaces: Tools that allow non-technical users to leverage AI automation.

  10. Implement an API-first, MCP-enabled architecture

    To ensure long-term architectural stability, separate core business capabilities from access mechanisms. Instead of building 'MCP-first', adopt an 'API-first, MCP-enabled' approach. This involves maintaining a stable integration contract (via REST, gRPC, GraphQL, or messaging) as the foundation and layering the Model Context Protocol (MCP) as an adapter on top. This prevents the need to rebuild core infrastructure as AI protocols, agents, and models evolve.

  11. Integrate existing OpenAPI APIs with HAPI MCP

    HAPI MCP acts as an interpreter that transforms existing OpenAPI-described APIs into controlled MCP capabilities. This allows organizations to use REST and gRPC as the systems of execution while using MCP as the contract for AI consumers, eliminating the need to rebuild existing business architecture for AI integration.

  12. Expose APIs as tools for ChatGPT using OpenAI UI SDK and MCP

    The OpenAI UI SDK combined with the Model Context Protocol (MCP) allows developers to expose their APIs as structured tools. This integration enables large language models (LLMs) like ChatGPT and other Web UIs to call these APIs directly as tools.