HAPI MCP — The Headless API Stack for Model Context Protocol
No shadow code. No shadow IT. Just your APIs, instantly connected to AI agents.
Turn your existing OpenAPI specs into MCP tools in seconds! Not months. No rewrites, no duplicate logic, no technical debt. Your APIs become AI-ready while you keep control.
$ curl -fsSL https://get.mcp.com.ai/hapi.sh | bashTeams rebuild API logic in agent code. Duplicate business rules in LLM prompts. Lose governance. Accumulate technical debt. There's a better way.
No shadow code: Your API is the single source of truth. No duplicate logic in agent layers.
No shadow IT: Same auth, same policies, same audit trails. Governance flows through automatically.
No rewrites: OpenAPI specs become MCP tools instantly. Update the spec, tools update.
Works with your existing stack: HAPI Server for auto-generation, runMCP for scaling, OrcA for orchestration, QBot + chatMCP for interfaces. Deploy in seconds! Not hours, not weeks.
Three simple steps to transform any API into a powerful, usable tool
OpenAPI, Swagger, REST - HAPI CLI works with any API specification format.
One simple CLI command transforms your API into a usable MCP Server.
Your API is now ready as a tool, AI agent, or testing interface.
Monthly, tactical updates on MCP, OpenAPI-first patterns, and how teams ship AI without rewrites.
See HAPI MCP in action — from spec to MCP tools to OrcA-run workflows across QBot and chatMCP.
Real business value. No technical debt. Deploy AI faster without compromising governance.
Your APIs are the runtime. No duplicate logic in agent layers. One source of truth, always in sync.
OpenAPI to MCP tools automatically. What takes competitors months happens before you finish your coffee.
Auth, RBAC, rate limits, audit trails—inherited from your APIs. Compliance teams sleep better.
runMCP handles serverless elasticity and long-running tasks. Cold-start fast, stay warm for throughput.
OrcA orchestrates multi-step tasks deterministically. No brittle prompt chains or guesswork.
Works with any MCP client—ChatGPT, Claude, QBot, custom agents. No vendor lock-in.
Ship AI initiatives without ballooning cost. Keep teams focused on outcomes, not rewrites and integration sprawl.
Design with OpenAPI/Arazzo, run with MCP. Clear contracts, policy inheritance, and versioned workflows keep risk low.
Deploy once. HAPI Server + runMCP scale tools; QBot/chatMCP give fast feedback; OrcA keeps executions deterministic.
Turns OpenAPI into MCP tools automatically—contracts stay in sync with your source of truth.
Autoscaling execution and testing for MCP tools; cold-start fast, stay warm when workflows run long.
Deterministic planning and orchestration for multi-tool tasks; no brittle prompt spaghetti.
CLI TUI for power users to interact with MCP tools directly from terminal or scripts.
Conversational client that speaks MCP natively for support, ops, and internal assistants.
Build agentic systems from standard APIs—no custom glue. Connect MCP clients across platforms.
“We pointed HAPI at our Swagger and had MCP tools in production in a week.”
“Security loved it—policies and audit trails stayed exactly as before.”
“OrcA plus runMCP gave us deterministic, scalable workflows without prompt spaghetti.”
No. HAPI MCP lifts your existing OpenAPI specs directly into MCP tools. Your auth, validation, and business rules remain unchanged.
It’s the Headless API model: your API is the runtime. HAPI Server reflects it as MCP; runMCP scales it; OrcA orchestrates it. No duplicate logic.
Any MCP client: ChatGPT, Claude, QBot, Agentico.dev, chatMCP, bespoke orchestrators—vendor-neutral by design.
Your API remains the single source of truth. Policies, RBAC, rate limits, and audit logs flow through automatically; no shadow logic.
Scoped credentials, per-tool permissions, and auditable calls are inherited from your API layer. HAPI adds guardrails and observability for regulated environments.
Monthly, tactical updates on MCP, OpenAPI-first patterns, and how teams ship AI without rewrites.
See HAPI MCP in action — from spec to MCP tools to OrcA-run workflows across QBot and chatMCP.
You’ve built an MCP server. It accesses data, performs actions, and works perfectly in your local development environment.
Now what?
To make your tools truly useful, they need to be accessible—whether by your team, your organization, or the global community of AI developers. This guide covers how to take your MCP server from localhost to production, and how to register it so it can be discovered.
AI agents are getting smarter — and more dangerous.
Not because they reason better, but because they act without boundaries.
Agent Skills exist to fix that.
In the rapidly evolving landscape of AI, Skills have emerged as a transformative force, enabling developers to extend the capabilities of AI models far beyond their native functions. Whether you're building chatbots, virtual assistants, or complex AI-driven applications, mastering Skills is essential to unlocking new levels of performance and user engagement.
Setting up a Model Context Protocol (MCP) server is easier than you might think! It is a straightforward process that doesn't require extensive DevOps skills. Whether you're testing, packaging, or scaling globally, the HAPI MCP Stack equips you with everything you need to launch your server in just seconds.
In this guide, we'll explore 4 different ways to run and deploy MCP servers using the HAPI CLI.
Bonus: Agent Skills provided to automate MCP server deployment.
The "Hello World" phase of the Model Context Protocol is over.
As enterprises move from experimental chatbots to production-grade agentic systems, they are hitting the invisible walls of scale: token bloat, latency, governance, and discovery. What works for ten tools fails catastrophically at ten thousand.
Your MCP just became a memory hog. And it’s quietly burning your budget.
If your Model Context Protocol (MCP) catalog is growing into the hundreds—or thousands—of tools, you’re already facing the next invisible scalability wall: token bloat.
And it’s not a theory anymore.
What's the lesson from Brian Casel's experience about the future of SDKs, no-code workflows, and automation?
In short: SDKs are dead - well, at least for many use cases in modern application development.
Transcripts from podcasts are treasure troves of insights, ideas, and stories. However, accessing and utilizing this wealth of information can be challenging for marketers, product managers, and content creators—especially those without deep technical expertise.
Lenny Rachitsky posted on LinkedIn about sharing transcripts of his podcast episodes, I decided to format the transcripts for easy consumption by language models and AI tools.
