Hire an AI API-to-MCP server release builder

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• An MCP server in Python or TypeScript, with checked tool definitions
• OAuth sign-in built into the server
• A ranked checklist of security fixes, with the riskiest first
• A hosted and packaged server, with health checks, logging and a plan for new versions

How it does the job well

• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.
• Treat input from users, files and networks as untrusted. Validate it at the boundary.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: You want your AI, or other people's, to use your API through MCP.

• step-01-api-to-mcp-server-builder.md. The server code, the list of exposed calls, the checked tool definitions and how the server runs.
• step-02-mcp-server-sign-in-builder.md. The sign-in model chosen, the details clients look for, the token checks and the result of the flow test.
• step-03-mcp-server-hardener.md. The attack risks found in the connection, sign-in, permissions and logging, with fixes ranked riskiest first.
• step-04-mcp-server-publisher.md. The hosting choice, the install package, health checks, logging, the version plan and the deploy url.
• HAND-BACK SUMMARY. Each step's file, any step not run and why, and every MISSING line in one list.

How the work is checked

• Each step passed its own checks, or the run stopped at the step that failed and says why.
• The steps ran in order, and each one used the result of the one before.
• Nothing was invented to fill a gap. Every gap is a MISSING: line.
• Nothing was sent, published or paid for without a clear yes.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Build the server. Reads the API spec, picks the calls to expose, generates the server, checks each tool definition and schema and picks how it will run.
• Save it and run it once. This one is yours. Save the server in your repo and run it once to confirm it starts.
• Add sign-in. Picks a sign-in model for how people connect, publishes the details clients look for, checks every token on every request and tests the flow end to end.
• Commit it and run again. This one is yours. Commit the sign-in code and run the server so the review reads the final code.
• Review the server for attack risks. Maps the threats, checks the connection, sign-in and tool permissions, checks logging and what each tool can touch, and lists fixes riskiest first.
• Apply the must-fix items. This one is yours. Fix everything marked must-fix and run the server again.
• Host and package it. Chooses remote hosting, a local package or both, adds health checks and per-tool logging, publishes it and plans versions so clients do not break.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id turn-an-api-into-a-safe-mcp-server-others-can-install only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/turn-an-api-into-a-safe-mcp-server-others-can-install/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Your API spec (OpenAPI or similar), or the API code
• Which calls should be exposed, and which must not be
• Who should be allowed to connect, and how they sign in
• Where you want to host it (optional)

Tools and access

• Claude, ChatGPT or Gemini: A pasted setup is enough for the checklist. An AI that reads project files can point at your real config lines.

Functions and deliverables

• API-to-MCP server release builder: Share the API spec, access rules and hosting limits. Get server code, OAuth sign-in, security findings and a release path after fixes.
  • An MCP server in Python or TypeScript, with checked tool definitions
  • OAuth sign-in built into the server
  • A ranked checklist of security fixes, with the riskiest first
  • A hosted and packaged server, with health checks, logging and a plan for new versions
• API to MCP server builder: Share an OpenAPI spec. Get tool definitions and starter server code in Python or TypeScript, with checks and next build steps.
  • Tool definitions for each action
  • A starter MCP server
  • A validation report
  • Notes for safe versioning
  Source: Alireza Rezvani.
• MCP server sign-in builder: Share your MCP transport and identity setup. Build token checks, discovery and permissions, then test client sign-in.
  • An auth model choice with reasons
  • Discovery metadata set up
  • Token checking code
  • A client registration plan
  • An end to end test
  Source: Paldom.
• MCP server security reviewer: Review your MCP server’s connections, sign-in, permissions and logs. Get control gaps, configuration examples and checks to run.
  • A fix list ranked by risk, grouped by layer
  • Sample settings for TLS, mutual TLS and sign-in checks
  • A tool-by-tool access plan, with risky tools flagged
  • Logging advice that keeps secrets out of logs
  • A list of what it could not check
  Source: sickn33.
• MCP server publisher: Share your server and release target. Deploy or package it with rollout checks, install instructions and version notes.
  • A delivery plan: hosted, local or both
  • A hosting setup where no server copy keeps session data
  • Health checks and a staged rollout plan
  • Per-tool logs, traces and alerts
  • An install package (npm, PyPI or MCPB) with pinned install steps
  • A checked server.json for the MCP Registry
  Source: Paldom.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• You want your AI, or other people's, to use your API through MCP.
• You built a quick MCP server and now others need to connect to it.
• You are about to publish a server and want it checked first.

Limits to check before starting

• You only need a quick MCP server for your own one-off script: use the API to MCP server builder skill on its own.
• You already have a server and only want it checked: use the MCP server security reviewer skill on its own.
• You only want to add sign-in to a server you already have: use the MCP server sign-in builder skill on its own.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the handoff pack. It's a zip with HANDOFF.md and every skill file this workflow uses.
• Give the folder to Claude (in Claude Code, or as files in a Claude Project) and say "Follow HANDOFF.md".
• Use a test account and test data for the API while you build.
• Do all three of your steps. Each one makes the next check read the real code.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/turn-an-api-into-a-safe-mcp-server-others-can-install for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.