Hire an AI MCP server planner

Everything it can hand back

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

• A tool list with a name, a one-line "use when" description, inputs and results for each tool
• Each tool marked as read-only or as one that changes data, with a confirm step for changes
• A setup plan covering language, how it connects and where secrets are kept
• Error rules for bad input, a down app and rate limits
• A test plan of real questions to ask an AI, and a list of checks not yet done

How it does the job well

• Descriptive tool names — search_users not query1; agents pick tools by name and description
• Typed parameters with Zod/Pydantic — every input validated, optional params have defaults
• Structured output — return JSON for data, markdown for human-readable content
• Fail gracefully — return error content with isError: true, never crash the server
• 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.

Mistakes it avoids

• Saying you built, ran or tested anything.
• Fifteen vague tools where three clear ones do the job.
• Putting a key, token or password anywhere in the output.
• 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 Claude or another AI to look things up or act in your helpdesk, database or internal API.

• Job summary. What the AI must do, which system it reaches and which actions only read.
• Tool list. Each tool's name, when to use it, inputs with types and defaults, and what it returns.
• Resources and prompts. Data the AI can read first and reusable prompt templates, only where they help.
• Setup plan. Language, how the server connects, and where secrets are kept.
• Error and safety rules. What each tool returns on failure, and which changes need your yes.
• Test plan. Real questions to ask an AI, and the tool each one should pick.

How the work is checked

• Every tool has a "use when" sentence, typed inputs and a result shape.
• No tool both reads and changes.
• No secret appears in the plan or the code.
• Every API fact comes from the user's material, or is marked to check.
• 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

• Pin down the job. It confirms what the AI cannot do today, which system it must reach and which actions only read.
• Choose tools, resources and prompts. Actions become tools. Data the AI should read first becomes a resource. Jobs you repeat become prompt templates.
• Design each tool. Each gets a name like verb_noun, a description of when to use it, typed inputs with defaults and a clear result.
• Plan errors and login. It writes failure results the AI can act on, input checks at the edge, and keeps secrets in environment variables.
• Plan the tests. It writes questions for a real AI, the tool each should pick and the error cases to try.
• List what is unchecked. Facts it could not confirm from your docs, and anything it did not run, go in an open checks list.

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 specialized-mcp-builder 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/specialized-mcp-builder/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

• The app, data or API your AI should reach
• What the AI should do there, split into looking things up and changing things
• API docs or two real sample answers with private data removed (optional)
• Your language, TypeScript or Python, and whether it runs on your own computer or a server (optional)
• How login works, such as an API key or OAuth. Name the type only. Never paste a real key or token.

Helpful context (optional)

• Two or three real answers from the API, with private data removed.
• The ten questions people will ask your AI.
• The API's rate limits and login type, copied from its docs.

Tools and access

• Claude Code: It can build the server, run it and test it against a real AI. A chat can only plan.
• Claude, ChatGPT or Gemini in a chat: Good for the tool list and the plan, before any code is written.

Functions and deliverables

• MCP server planner: Plan an MCP server so your AI can use your app or API. Get named tools, typed inputs, error rules and a test plan a developer can build from.
  • A tool list with a name, a one-line "use when" description, inputs and results for each tool
  • Each tool marked as read-only or as one that changes data, with a confirm step for changes
  • A setup plan covering language, how it connects and where secrets are kept
  • Error rules for bad input, a down app and rate limits
  • A test plan of real questions to ask an AI, and a list of checks not yet done
  Source: Michael Sitarzewski and Agency Agents contributors.
• 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.
• Tool connector builder: Share API docs and the tasks your AI needs. Get an MCP server plan, tool definitions, setup steps and tests to run.
  • A working MCP server
  • Tool names and descriptions
  • Error handling that guides the AI
  • Test questions
  • Setup steps
  Source: Anthropic.
• TypeScript MCP server builder: Describe your tools and data. Get a TypeScript MCP server project with tools, resources and tests.
  • A project folder with package.json, tsconfig and a .gitignore file
  • A server file with your tools, input checks and error handling
  • Resources and prompts, if you asked for them
  • Steps to run the server and test it with MCP Inspector
  Source: GitHub Copilot community.
• 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 Claude or another AI to look things up or act in your helpdesk, database or internal API.
• A developer asks "what should the tools be?" before writing any code.
• Your AI keeps calling the wrong tool or sending bad inputs, and you think the tool names and descriptions are the cause.
• You must decide if the server can only read, or can also create, update or delete.

Limits to check before starting

• You only need clear names and descriptions for a few tools. Use the MCP tool set designer.
• You have an API spec and want working server code. Use the API to MCP server builder, or the TypeScript MCP server builder or Python MCP server starter builder.
• Your problem is how people sign in to the server. Use the MCP server sign-in builder.

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.

• Add the pack to your AI as a skill. If you cannot, start a new chat and paste SKILL.md, then references/source-agent.md.
• Paste your brief. Say which app the AI should reach, what it should do there and what you know of the API. Leave out keys, tokens and customer data.
• Answer its questions. It asks once for anything missing, then marks the gap MISSING instead of guessing.
• Read the tool list first. For each tool, ask if a stranger could tell from the name and description when to use it.
• Hand the plan to a developer. Or ask for starter code and run it in Claude Code or a code editor. A chat cannot run or test a server.
• Run the test plan with a real AI. Send back any wrong picks so the descriptions can be fixed.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/specialized-mcp-builder 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.