AI Agent and MCP Tools
Agents & MCP covers giving AI the ability to act. The multi-agent designer plans a team around a chief-of-staff agent that breaks a goal into tasks, hands them to specialist agents, tracks progress and asks a person before risky steps, and exports each agent's instructions, a handoff schema, a diagram and starter files for Claude subagents and the OpenAI Agents SDK. The system prompt generator does the same for a single agent, and the tool schema builder writes the function definitions agents call, in OpenAI, Anthropic, Gemini and MCP shapes.
6 free tools · no sign-up · no ads
All Agents & MCP (6)
Multi-Agent System Designer
Plan a chief-of-staff agent and specialist agents for a goalAgent System Prompt Generator
Write a system prompt with tools, guardrails and stop rulesAI Tool Schema Builder
Build function-calling tool definitions for OpenAI, Claude and MCPMCP Config Generator
Connect MCP servers to Claude, Cursor, VS Code and moreMCP Server Generator
Scaffold an MCP server in TypeScript or Python from tool definitionsOpenAPI to MCP Converter
Turn an OpenAPI spec into MCP tools and a server
Which tool do I need?
Plan agents that work together on a goal
Choose a pattern and specialists, set budgets and approvals, and export prompts and starter files.
Write instructions for one agent
Define its tools, limits, escalation rules and when to stop.
Add an MCP server to Claude, Cursor or VS Code
Get the right JSON, TOML or command for each client from one definition.
Expose your own app to AI assistants
Scaffold an MCP server, or generate one from your OpenAPI spec.
About Agents & MCP
The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data. The config generator writes the exact entries for Claude Desktop, Claude Code, Cursor, VS Code and other clients. The server generator scaffolds a TypeScript or Python MCP server from your tool definitions, and the OpenAPI converter turns an existing REST API description into MCP tools and a server that calls it.
Guides
- The Chief of Staff Agent: Designing Multi-Agent Systems That Finish the JobHow an orchestrator agent plans, delegates to specialists and asks a person at the right moments: four patterns, when one agent is better, delegation messages, budgets and failure modes.
- How to Set Up MCP Servers in Claude, Cursor, VS Code, Codex and Gemini CLIWhat MCP is, local stdio versus remote servers, the exact config file and format for each AI client, keeping API keys out of shared files, and how to fix a server that will not connect.
- How to Build an MCP Server in TypeScript or PythonTools, resources and prompts, a first server with the official TypeScript and Python SDKs, stdio or HTTP, wrapping an API, auth, testing with MCP Inspector and tool design tips.
Frequently asked questions
What is a chief-of-staff agent?
It is an orchestrating agent that owns the goal. It plans the work, delegates tasks to specialist agents with clear briefs, checks their results, keeps a running status and escalates decisions to a person, much as a human chief of staff does for an executive.
When is a single agent better than several?
Most of the time. Several agents add cost, latency and more places to fail. They help when a task splits into independent parts that can run in parallel or need different tools and context, so start with one agent and split only when it struggles.
What is MCP and why does it matter?
The Model Context Protocol is an open standard for connecting AI apps to tools and data sources. A server written once can be used from any MCP client, such as Claude, Cursor or VS Code, instead of building a separate plugin for each.
Is it safe to connect MCP servers to my AI assistant?
Treat an MCP server like any software you install: it can read and change whatever you give it access to. Use servers from sources you trust, keep tokens in environment variables, limit file and account access, and review actions before approving them.