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.
Every team adopting MCP eventually hits the same fork in the road: reimplement your business capabilities as an MCP server, or expose what you already have through one.
The first path feels faster on day one. It's also how a second, hidden application — a shadow of the one you already trust — gets built without anyone deciding to build it.
AI has dramatically reduced the cost of writing code. It has not reduced the cost of owning it.
That distinction may become one of the most important architecture lessons of the AI era.
Today, almost anyone inside an engineering organization can open an AI coding assistant and say:
"Here is my API. Build an MCP server for it."
If your tool server needs to interrupt the model to ask a question, ask why the question wasn't answerable before the call started.
MCP elicitation is a real, spec-defined capability, and it solves a real problem. It's also becoming a convenient place to hide a design problem instead of fixing it.
The expensive part of enterprise AI is often not the model. It is rebuilding what already works.
Many software companies have spent years, and sometimes decades, turning business rules, customer needs, operational lessons, security controls, and hard-won reliability into production APIs.
Then AI arrives, and the first instinct is often: We need to rebuild the platform for agents.
Your API is already production-hardened. It has authentication, validation, rate limits, monitoring, and a battle-tested OpenAPI spec describing every operation. None of that changes when an AI agent becomes the caller instead of a browser or a mobile app.
The most valuable MCP server may be the one that refuses to become your application.
MCP is moving toward a clearer architectural truth: servers should be stateless.
Every line of business logic you duplicate into an MCP creates another place where your system can eventually disagree with itself.
And that disagreement has a cost.
Every few months, our industry declares the next abstraction layer "the new HTTP."
First it was SOAP.
Then REST. Then GraphQL. Then gRPC.
Now, it's MCP.
