API-First for AI.
Turn existing OpenAPI specs into MCP servers you can deploy, secure, and operate like real software.

Website · Docs · MCP Servers · Run MCP · CLI Tool

Most orgs don’t have an “AI problem”.
They have an integration and governance problem.

• Your tools live behind APIs
• Your teams ship OpenAPI specs (or should)
• Your agents/IDEs need safe, auditable access
• You need control: identity, policy, rate limits, logs, approvals, isolation

MCP.com.ai is the bridge:

• OpenAPI → MCP (standard-driven)
• Deploy anywhere (local, cloud, enterprise)
• Security-first patterns (authority outside the model)

A growing catalog of MCP servers that follow real-world needs:

• clear tool design
• minimal schemas (reduce token bloat)
• safe defaults (timeouts, retries, pagination)
• predictable auth patterns

Under La Rebelion Labs, the HAPI MCP Stack helps teams run MCP “like a platform”:

• HAPI Server — run MCP + REST side-by-side (Headless API)
• runMCP — deploy + route hosted MCP endpoints
• HAPI Registry — discover tools/servers (and support dynamic discovery)
• QBot / OrcA — agent UX + orchestration (coming/iterating fast)

The goal: MCP Lifecycle Management — from API specs → build → eval → deploy → operate → retire.

1. Pick a server from the org repos
2. Run locally or cloud (http always!)
3. Connect from your client/IDE

If you already have an OpenAPI spec, you’re 80% done.

• Treat your OpenAPI as the contract
• Generate MCP tools from operations
• Add policy, auth, and eval checks

See docs: OpenAPI → MCP guidance and templates (linked from MCP.com.ai docs).

Reasoning stays with the model. Authority stays with the system.

We optimize for:

• no long-lived secrets in the model
• scoped, short-lived credentials
• policy enforcement at the boundary
• audit logs by default
• transport that fits the environment (cloud, enterprise, airgap)

If you’re building “remote MCP” in regulated environments, this is the only way it survives compliance.

Good MCP isn’t just “it runs”. It’s:

• correct tool selection
• stable schemas
• predictable pagination
• safe error behavior
• regression coverage with real prompts

We support evaluation patterns inspired by modern “skills” tooling:

• scenario suites
• tool-call assertions
• latency + cost budgets
• golden traces

• Platform teams building internal AI enablement
• DevOps/SRE who will be on-call for “AI integrations”
• API teams who already own OpenAPI contracts
• Security teams who need guardrails, identity, and auditability
• Enterprise architects tired of one-off agent glue

We love contributions that make MCP more operational:

• new MCP server examples
• OpenAPI → MCP mapping improvements
• eval suites for servers
• security patterns (auth, policy, isolation)
• docs that help non-developers succeed

How to contribute

1. Open an issue describing the server or improvement
2. Provide a minimal spec + example prompts
3. Submit PR with tests (when possible)

If you want MCP to be more than a demo, follow the ecosystem:

• MCP.com.ai updates (docs + servers)
• HAPI MCP Stack releases (deploy, registry, lifecycle)

Building MCP isn’t the hard part.
Operating MCP safely is the product.