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.
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.
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.
Today, we crossed a major milestone.
👉 The HAPI MCP Registry is now available in the ChatGPT marketplace, officially aligning with the ecosystem driven by OpenAI.
And this is bigger than a feature release.
This is a shift in how APIs and AI finally connect.
"We already solved this problem... and somehow we forgot."
Back in the day, database engineers learned this lesson the hard way.
We didn't call it "AI cost optimization." We called it bad query design.
And it hurt.
Enterprise AI is entering a new phase. Not the hype phase. Not the experimentation phase. The operational phase — where organizations must make AI safe, governed, and useful for real teams.
Over the last year, a clear pattern has emerged inside large enterprises experimenting with AI automation. What starts as scattered experimentation quickly evolves into a structured platform strategy.
Something subtle but massive just happened in developer tooling. The IDE Is No Longer the Center of Development — Agent Orchestration Is.
For decades, the IDE was the center of software development. Everything revolved around it: edit → run → debug → commit.
Now something else is emerging.
A control plane for AI agents.
If every AI agent needs its own custom integration... you don't have an AI strategy. You have an integration nightmare.
Traditional APIs were built for humans and frontends. AI agents change the equation.
And this is where most teams misunderstand Model Context Protocol (MCP).
If your AI agent can access user data without asking for passwords... you win trust. If it can't... you lose the deal.
If you deploy an MCP Server manually from scratch, OAuth becomes a project.
If you deploy an MCP Server using HAPI MCP from an OpenAPI specification, OAuth becomes a configuration option.
That's a little big difference.
Everyone wants AI agents. No one wants AI debt.
MCP enthusiasm is real. Enterprise constraints are also real.
Security. Auth. Compliance. Deployment pipelines. Audit logs. None of that disappears because we’re excited about agents.
The hard truth? Most teams building MCP servers today are moving fast — and quietly laying the foundation for the next generation of technical debt.
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.
