danielcy

danielcy / coze-mcp-for-openclaw

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Coze MCP and Skill Management for OpenClaw

38
0
80% credibility
Found Mar 10, 2026 at 17 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A tool that connects published workflows from Coze to an agent system via callable tools and generates descriptive skill files for OpenClaw.

How It Works

1
🔍 Find the Coze bridge

You discover a simple way to bring your clever Coze workflows into OpenClaw, making your AI assistant even smarter.

2
🔗 Link your Coze workspace

You share private details from your Coze account so it can see and use your published helpers.

3
🛠️ Create skill folders

It automatically builds easy-to-use folders with full instructions for every one of your workflows, ready for OpenClaw.

4
📋 Browse your helpers

You check out the full list of available workflows, seeing names, descriptions, and what info they need.

5
▶️ Run a workflow

Pick any helper by name, add simple details if needed, and watch it deliver results instantly.

🎉 OpenClaw supercharged

Your AI assistant in OpenClaw now taps into all your Coze magic, handling tasks effortlessly and saving you tons of time.

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AI-Generated Review

What is coze-mcp-for-openclaw?

This Python project runs a FastMCP server that connects ByteDance Coze workflows to the MCP protocol, letting AI agents list published flows from your workspace, fetch their input params, and execute them via simple tool calls like `run_workflow_by_name`. It solves the gap for Coze MCP支持 in agent frameworks by caching workflows for speed and providing formatted JSON outputs with token usage. A bonus script auto-generates OpenClaw skill folders (SKILL.md) from your Coze studio workflows, complete with param docs.

Why is it gaining traction?

It delivers instant Coze MCP server and client tools without custom API glue, standing out for Coze flow github users who want MCP服务 for agents. Devs dig the refreshable cache and debug links in responses, making Coze的自 定义 MCP a breeze versus raw httpx calls. Ties into rising github coze api and coze workflow demand for agent loops.

Who should use this?

AI agent builders on OpenClaw needing Coze MCP配置 to run no-code workflows like chatbots or data pipelines. Backend devs at startups using Coze studio mcp for github coze agent prototypes, or teams asking "coze 支持 mcp 吗" while scaling bots.

Verdict

Worth a spin for Coze的 mcp experiments if you're in the ecosystem, but 16 stars and 0.8% credibility score signal early-stage maturity—light docs and no tests mean prototype only. Pairs well with coze ai github for quick wins.

(178 words)

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