Meta mcp wizard
Agent Skills for mcphero.app - let your OpenClaw or coding agents create deployed MCP servers on the fly in just 30 seconds.
npx -y skills add arterialist/mcphero-skills --skill meta-mcp-wizardAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
Copied from the file, not written here
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.
SKILL.md
6.3 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it
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).
What ships with it: 1 file
3.0 KB alongside SKILL.md
evals/
- evals.json3.0 KB
Gives 0 of the 12 instructions most mcp tooling skills give in ~1.4k tokens
Counted across 638 of the 750 authors here whose files we hold, read 2026-08-07
- Create ten complex or independent read-only evaluation questionsin 69 of 638, across 15 files
- Test servers using MCP Inspectorin 61 of 638, across 19 files
- Provide actionable error messages with specific next stepsin 54 of 638, across 12 files
- Prioritize comprehensive API coverage over specific workflows or workflow toolsin 54 of 638, across 12 files
- Use TypeScript and Streamable HTTP for remote servers or clientsin 54 of 638, across 8 files
- Define structured output schemas where possiblein 50 of 638, across 8 files
- Use Zod or Pydantic for input schemasin 47 of 638, across 5 files
- Fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
- Load framework documentation using WebFetchin 45 of 638, across 3 files
- Verify each evaluation answer independentlyin 45 of 638, across 3 files
- Implement API client with authentication and paginationin 45 of 638, across 3 files
- Define input schemas with validationin 27 of 638, across 9 files
Said here and by no other author read
- add the meta-mcp endpoint to client config
- save the returned server_id
- include the server_id in every subsequent call
- loop the chat tool until is_ready is true
- call wizard_submit_env_vars even when list is empty
- submit at most five tools per server
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.