Metadata

name: meta-mcp-wizard
description: Use the MCPHero Meta-MCP server inside AI clients (Claude Desktop, Cursor, etc.) to create, deploy, and manage MCP servers through the wizard pipeline. Use this skill when the user wants to connect the Meta-MCP server, build MCP servers interactively via MCP tools, or asks about the meta-mcp endpoint at api.mcphero.app.

Meta-MCP Wizard

The MCPHero Meta-MCP server lets agents build persistent tools without leaving their MCP client. Instead of re-explaining an API or re-generating SQL on every run, the agent creates a hosted MCP server once and calls it forever — saving 95-99% on token usage for recurring tasks.

Connect it to Claude Desktop, Cursor, or any MCP client, and your agent can build, deploy, and register new MCP servers on the fly.

Meta-MCP endpoint: https://api.mcphero.app/mcp/meta/mcp

Setup

Add the Meta-MCP server to your MCP client config:

Claude Desktop

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "mcphero": {
      "url": "https://api.mcphero.app/mcp/meta/mcp"
    }
  }
}

Config file locations:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json

Cursor and other MCP clients

Add the same URL https://api.mcphero.app/mcp/meta/mcp to your client's MCP server config. The client handles OAuth 2.1 authentication automatically on first use.

Available Tools

The Meta-MCP server exposes these tools:

Tool	Purpose
`wizard_create_session`	Start session; returns `server_id`
`wizard_chat`	Send requirement message; returns `is_ready` flag
`wizard_start`	Transition to tool suggestion (async)
`wizard_list_tools`	List suggested tools with IDs, code, params
`wizard_refine_tools`	Refine with feedback (async)
`wizard_submit_tools`	Confirm selection by tool UUID list
`wizard_suggest_env_vars`	Re-trigger env var suggestion (async)
`wizard_list_env_vars`	List env vars with IDs and descriptions
`wizard_refine_env_vars`	Refine env vars with feedback (async)
`wizard_submit_env_vars`	Submit values as `{uuid: value}` dict
`wizard_set_auth`	Generate bearer token
`wizard_generate_code`	Trigger code gen (async)
`wizard_regenerate_tool_code`	Regenerate single tool code (sync)
`wizard_deploy`	Deploy and get server URL
`wizard_state`	Poll current state — use after all async steps

Async vs Sync Tools

Async (trigger then poll wizard_state): wizard_start, wizard_refine_tools, wizard_suggest_env_vars, wizard_refine_env_vars, wizard_generate_code

Sync (wait for result): wizard_create_session, wizard_chat, wizard_list_tools, wizard_submit_tools, wizard_list_env_vars, wizard_submit_env_vars, wizard_set_auth, wizard_regenerate_tool_code, wizard_deploy, wizard_state

The Wizard Pipeline

1. wizard_create_session    → Returns server_id (save it, needed everywhere)
2. wizard_chat (loop)       → Gather requirements; stop when is_ready: true
3. wizard_start             → Transition to tool suggestion (async → poll)
4. wizard_list_tools        → Review AI-suggested tools
5. wizard_refine_tools      → Iterate on tools until satisfied (optional, async → poll)
6. wizard_submit_tools      → Confirm selection by tool UUID list
7. wizard_list_env_vars     → Review suggested env vars
8. wizard_refine_env_vars   → Iterate on env vars (optional, async → poll)
9. wizard_submit_env_vars   → Provide values as {uuid: value} dict (call even if empty)
10. wizard_set_auth         → Generate bearer token
11. wizard_generate_code    → Trigger code generation (async → poll)
12. wizard_deploy           → Deploy → returns server_url + bearer_token

Always call wizard_submit_env_vars, even when wizard_list_env_vars returns []. Pass an empty dict {} so the backend transitions to the next state.

State Machine

The setup_status field in wizard_state tells you where you are:

gathering_requirements     → User is chatting about requirements
tools_generating           → LLM is generating tool suggestions (async, poll)
tools_selection            → Tools ready for review/selection
env_vars_generating        → LLM is generating env var suggestions (async, poll)
env_vars_setup             → Env vars ready for review/submission
auth_selection             → Ready for auth setup
code_generating            → LLM is generating code (async, poll)
code_gen                   → Code ready for review
deployment_selection       → Ready to deploy
ready                      → Server deployed and live

States ending in _generating are transient — poll until they transition. The processing_status field is the reliable check: "idle" means done, "processing" means wait, "error" means check processing_error.

Polling Pattern

After any async tool, poll wizard_state until processing_status is "idle":

wizard_state(server_id) → check .processing_status
  "processing"  → wait and call again
  "idle"        → ready for next step
  "error"       → check .processing_error

Connecting a Deployed Server to MCP Clients

After wizard_deploy, construct the full server URL:

https://api.mcphero.app{server_url}

where server_url is the relative path returned (e.g., /mcp/<server-id>/mcp).

Claude Desktop config

{
  "mcpServers": {
    "my-server": {
      "url": "https://api.mcphero.app/mcp/<server-id>/mcp",
      "headers": {
        "Authorization": "Bearer <bearer_token>"
      }
    }
  }
}

Key Tips

Free tier: Max 5 tools per server. wizard_submit_tools will error if more are selected.

server_id is everything: Save the UUID returned from wizard_create_session. Every subsequent call needs it.

Empty env vars: Even if wizard_list_env_vars returns [], you must still call wizard_submit_env_vars with {} so the backend transitions to the next state.

Regenerate without redeploy: After using wizard_regenerate_tool_code, the code change takes effect immediately for already-deployed servers (the server auto-remounts).

Create MCP Servers using Meta-MCP

安装技能