# MCPHero Meta-MCP Generator

> Use this tool when you need to automate the creation of custom tools for your AI agent, reducing token burn and increasing efficiency by generating, deploying, and hosting production-ready MCP servers with exact tool requirements. It solves problems such as repetitive API calls, database parsing, and calculation errors, and provides an interface for natural language input and tool output. Use MCPHero Meta-MCP Generator when building integrations or automating workflows to create reusable tools that can be called with minimal tokens.

Canonical page: https://skillsregistry.net/skills/mcphero-meta  
JSON: https://api.skillsregistry.net/v1/skills/mcphero-meta

## Description

# MCPHero Meta-MCP

Let your agent build its own tools. MCPHero Meta-MCP exposes an AI-powered wizard pipeline as MCP tools — describe what you need in plain English, and the agent generates, deploys, and hosts a production MCP server with the exact tools required. Once built, the agent calls those tools on every future run instead of re-generating context from scratch.

**Why it matters:** Every time an agent calls an API, parses a database, or runs a calculation, it burns tokens on schemas, error handling, and output parsing. MCPHero flips this — a 50,000-token integration becomes a 50-token tool call on every subsequent run. Your agent doesn't just *use* tools, it *creates* them and keeps them forever.

## What you get

- **15 MCP tools** covering the full lifecycle: create sessions, describe requirements in natural language, review AI-suggested tools, configure env vars, set auth, generate code, and deploy — all from inside your agent
- **Self-hosted MCP servers** with bearer token auth, ready to connect to any MCP client
- **No CLI required** — works entirely through the MCP protocol (OAuth 2.1 handled by your client)

## Quick start

Add to your MCP client config:

```json
{
  "mcpServers": {
    "mcphero": {
      "url": "https://api.mcphero.app/mcp/meta/mcp"
    }
  }
}
```

Then ask your agent: *"Build me an MCP server for my PostgreSQL database"*

## Tools

| Tool | What it does |
|------|-------------|
| `wizard_create_session` | Start a new server build session |
| `wizard_chat` | Describe requirements in natural language |
| `wizard_start` | Begin AI tool suggestion |
| `wizard_list_tools` | Review suggested tools |
| `wizard_refine_tools` | Iterate on tool definitions |
| `wizard_submit_tools` | Confirm tool selection |
| `wizard_suggest_env_vars` | Get env var suggestions |
| `wizard_list_env_vars` | Review suggested env vars |
| `wizard_refine_env_vars` | Iterate on env vars |
| `wizard_submit_env_vars` | Submit env var values |
| `wizard_set_auth` | Generate bearer token |
| `wizard_generate_code` | Generate server code |
| `wizard_regenerate_tool_code` | Regenerate a single tool |
| `wizard_deploy` | Deploy and get server URL |
| `wizard_state` | Poll async operation status |

## Learn more

- [Documentation](https://mcphero.app/docs/meta-mcp)
- [GitHub](https://github.com/arterialist/mcphero-skills)
- [Agent Skills for MCPHero MCP Server](https://github.com/arterialist/mcphero-skills/tree/main/skills/meta-mcp-wizard) — use with Claude Code, OpenCode, Cursor, and more

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-05-11

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/mcphero/meta)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "mcphero-meta"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/mcphero-meta` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/mcphero-meta/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt