Glm MCP vs Openclaw MCP — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Glm MCP vs Openclaw MCP
In-depth architectural comparison of the Glm MCP and Openclaw MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Glm MCP
Coding Agents · Local stdio
Quality: 56/100 (Good) | Auth: API Key required
Openclaw MCP
Coding Agents · Local stdio
Quality: 64/100 (Good) | Auth: API Key required
Verdict Summary: Choose Glm MCP if you need specialized Coding Agents tools running via a local process. Choose Openclaw MCP if your workspace requires Coding Agents integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Glm MCP when:
You need dedicated capabilities in the Coding Agents domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
You need dedicated capabilities in the Coding Agents domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (Free / Open Source).
You have access to required keys: OPENCLAW_URL, OPENCLAW_GATEWAY_TOKEN, OPENCLAW_AGENT_ID, OPENCLAW_MODEL, OPENCLAW_TIMEOUT_MS, AUTH_ENABLED, MCP_CLIENT_ID, MCP_CLIENT_SECRET.
Run GLM (Zhipu/Z.ai) as a real sub-agent inside Claude Code or GitHub Copilot. GLM gets its own agent loop (read/write/edit/run) on your repo — not a single LLM call — with peak-aware Opus-vs-GLM routing, diff/dry-run/git-revert oversight, and a usage ledger. 10x cheaper than Opus. Requires a Z.ai GLM Coding Plan key.
MCP server for OpenClaw AI assistant integration. Enables Claude to delegate tasks to OpenClaw agents with sync/async tools, OAuth 2.1 auth, and SSE transport for Claude.ai.
Category & Scope
Tools & Capabilities Breakdown
Glm MCP Tools (6)
Repository-level GLM agent loop
Read, write, edit, list, and Bash tools
Dry-run diff preview and Git revert guidance
Peak-aware model routing
Live progress notifications
JSONL usage ledger and status reporting
Openclaw MCP Tools (7)
openclaw_chat
Send a message to OpenClaw and get a response
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Glm MCP is categorized under Coding Agents and uses a local stdio subprocess. In contrast, Openclaw MCP belongs to Coding Agents using local stdio subprocess. Select Glm MCP when you need capabilities focused on coding agents and Openclaw MCP when you require tools for coding agents.
Get OpenClaw gateway status and health information
openclaw_chat_async
Send a message to OpenClaw asynchronously. Returns a task_id immediately that can be polled for results. Use this for potentially long-running conversations.
openclaw_task_status
Check the status of an async task. Returns status, and result if completed.
openclaw_task_list
List all tasks. Optionally filter by status, session, or instance.
openclaw_task_cancel
Cancel a pending task. Only works for tasks that haven't started yet.
openclaw_instances
List all configured OpenClaw instances. Shows instance names, URLs, and which is the default. Use instance names in other tools to target a specific OpenClaw gateway.