glm-mcp — GLM as a cheap delegate for your AI coding agent

GLM (Zhipu / Z.ai) as a ~10x cheaper delegate for your AI coding agent. Your expensive main model — Claude Opus, Copilot's default, or Codex — orchestrates and reviews; GLM does the actual work, billed on cheap GLM tokens. GLM exposes an Anthropic-compatible /v1/messages endpoint, so it drops into anything that already speaks Anthropic. This repo wraps it as an MCP server with four tools, plus one-command installers for Claude Code, GitHub Copilot, and Codex. The same server powers every edition.

   

How it works

flowchart TD
    You["You"]
    Main["Main agent (Claude Opus / Copilot / Codex)<br/>orchestrates + reviews"]
    Srv["glm MCP server (stdio)<br/>4 tools"]
    Rt["Router<br/>peak-aware model pick + cost bias"]
    Zai[/"Z.ai Anthropic endpoint<br/>POST /v1/messages"/]
    Loop["glm_agent tool loop<br/>read_file / write_file / edit_file<br/>list_dir / run_bash — on your repo"]
    Led[("usage.jsonl<br/>every GLM call: model + tokens")]

    Repo[("your repo")]

    You --> Main -->|"glm_agent(task, workdir)"| Srv
    Srv --> Rt --> Zai
    Zai -->|"tool calls"| Loop
    Loop -->|"tool results"| Zai
    Loop -->|"reads / writes / runs"| Repo
    Zai --> Led
    Srv -->|"summary + GLM STATS<br/>(model, tokens, est. cost)"| Main
    Main -->|"review · diff · revert"| You

Plain-English walkthrough:

1. You ask the main agent for work.
2. The main agent delegates via glm_agent — it passes a goal plus an absolute workdir.
3. The server's router picks a GLM model (peak-aware) and calls the Z.ai /v1/messages endpoint; the cost bias keeps GLM the default.
4. GLM runs its own agent loop (read_file / write_file / edit_file / list_dir / run_bash) directly against your repo, then stops with a summary.
5. The server returns a concise summary + a GLM STATS block (model, tokens, est. cost) to the main agent.
6. The main agent reviews; every GLM call is also appended to the usage.jsonl ledger.

Token economics. Delegated work bills GLM tokens (~10x cheaper). The main model only pays for orchestration + review. A near-100% GLM share requires the full-GLM launcher (claude/glm-code.mjs), because a hybrid main agent always carries per-turn session context — that context is the floor on its token share.

The four tools

Tool	Cost	What it does
glm_agent	GLM tokens	GLM as a real coding agent in your repo (read/write/edit/run). dry_run: true previews a diff and writes nothing; after a real run a git-checkpoint revert line is printed.
glm_delegate	GLM tokens (opt-in)	Pure text generation — text in, text out. Hidden by default (glm_agent handles text-only tasks too); set GLM_DELEGATE=on to expose it.
glm_recommend	free (local)	GLM-vs-main-model advisory: which engine, which GLM model, confidence, and reasons. No GLM call.
glm_status	free (local)	Peak window, active model, usage-ledger totals (proof of GLM spend), and config health. No GLM call.

Live progress. glm_agent streams MCP progress notifications while it runs — current iteration, token count, and tok/s — shown live in Claude Code and mapped to tool.execution_progress in VS Code Copilot. This heartbeat also keeps long calls alive on clients that reset their timeout on progress, and cancelling a run stops GLM promptly (partial changes are shown and revertable). max_tokens defaults to auto (uncapped/generous; the orchestrating agent may pass a number to cap a call). The server uses an idle/stall timeout (GLM_STALL_TIMEOUT_MS, 2 min), so an actively-streaming turn is never cut off. If a very long run is still cancelled by your client's tool-call timeout, raise it with MCP_TOOL_TIMEOUT / CLAUDE_CODE_MCP_TOOL_IDLE_TIMEOUT.

Install

(a) Claude Code

npx glm-mcp-claude --key YOUR_ZAI_KEY

Installs globally by default (user-scoped): the MCP server, a full-tool glm subagent, a PreToolUse auto-routing hook, and an optional glm-code full-GLM launcher. Restart Claude Code, then run glm_status to confirm api_key_loaded: true. Full details: claude/README.md.

(b) GitHub Copilot / VS Code

npx glm-mcp-copilot --key YOUR_ZAI_KEY            # current workspace
npx glm-mcp-copilot --global --key YOUR_ZAI_KEY   # every workspace

Installs the MCP server in agent mode, a GLM custom agent (subagent), a PreToolUse auto-routing hook, and delegation instructions files. Reload the VS Code window, open Copilot Chat in Agent mode, start the glm server. Full details: copilot/README.md.

(c) Codex

Install the published Codex package:

npx glm-mcp-codex --key YOUR_ZAI_KEY

Installs a Codex MCP registration, a glm custom agent, the glm-delegate skill, and an advisory UserPromptSubmit/PreToolUse hook. The config gives GLM tools a 30-minute timeout and prompts before mutating calls. Restart Codex, review the hook with /hooks, and run glm_status. Full details: codex/README.md.

(d) Any MCP client / Glama / Docker

The standalone glm-mcp package — no installer needed for Cursor, Windsurf, Claude Desktop, Glama, etc.:

{
  "mcpServers": {
    "glm": {
      "command": "npx",
      "args": ["-y", "glm-mcp"],
      "env": { "GLM_API_KEY": "YOUR_ZAI_KEY" }
    }
  }
}

For containers, the repo-root Dockerfile runs the same server:

docker build -t glm-mcp .
docker run --rm -i -e GLM_API_KEY=YOUR_ZAI_KEY glm-mcp

The server boots and answers MCP introspection without a key — set GLM_API_KEY only for actual GLM calls.

Editions at a glance

	Claude Code -> claude/	GitHub Copilot (VS Code) -> copilot/	Codex -> codex/
npm package	glm-mcp-claude	glm-mcp-copilot	glm-mcp-codex
Install	npx glm-mcp-claude --key ...	npx glm-mcp-copilot --key ... (+ --global)	npx glm-mcp-codex --key ...
MCP server	user-scoped (claude mcp add glm -s user)	VS Code agent mode (mcp.json)	~/.codex/config.toml (or trusted project config)
Subagent	glm subagent (~/.claude/agents/glm.md)	GLM custom agent (glm.agent.md)	glm custom agent (~/.codex/agents/glm.toml)
Auto-routing hook	PreToolUse, Task matcher (glm_subagent_router.mjs)	PreToolUse, fires on all calls (glm_router_hook.mjs)	UserPromptSubmit + PreToolUse, advisory only
Delegation policy	appended to ~/.claude/CLAUDE.md	.instructions.md files	glm-delegate skill + optional AGENTS.md snippet
Full-GLM launcher	glm-code.mjs (Claude only)	—
Docs	claude/README.md	copilot/README.md	codex/README.md

Parity. All three editions expose the same four tools and a subagent while using the same server underneath. Codex uses its native custom-agent, skill, and hook surfaces; its hook is advisory only and must be trusted by the user. Only Claude ships the standalone glm-code full-GLM launcher.

Configuration

All knobs live in .env (git-ignored). Location per edition: Claude ~/.claude/glm-mcp/.env (set during install); Copilot ~/.glm-mcp/glm-mcp/.env; Codex ~/.codex/glm-mcp/.env. Codex sets tool_timeout_sec = 1800 because its default MCP tool timeout is 60 seconds. Full reference with comments: claude/glm-mcp/.env.example.

Var	Default	Meaning
GLM_API_KEY	—	Your Z.ai / Zhipu GLM Coding Plan key. Required for GLM calls.
GLM_BASE_URL	https://api.z.ai/api/anthropic	Anthropic-compatible endpoint (/v1/messages).
GLM_USE_HAIKU	off	off calls GLM directly so all tokens stay on GLM; on allows the Haiku-orchestrated subagent (spends some Claude tokens).
GLM_COST_BIAS	7	How hard to favor GLM. 7 ≈ 98–100% of eligible tasks to GLM. Lower (e.g. 1.5) to send more hard tasks to the main model; 0 = capability only.
GLM_MAX_CONCURRENT	1	GLM caps in-flight requests (~1); keep at 1 unless your tier allows more.
GLM_CAP	off	off = generous (up to GLM_MAX_TOKENS_CEILING); on enforces GLM_MAX_TOKENS.
GLM_MAX_TOKENS	32768	Hard per-call limit applied only when GLM_CAP=on.
GLM_MAX_TOKENS_CEILING	131072	Generous default used when the cap is off.
GLM_MAX_RETRIES	4	Retries on 429 / concurrency / 5xx with exponential backoff.
GLM_TIMEOUT_MS	300000	Per GLM HTTP request timeout (5 min).
GLM_AGENT_MAX_ITERS	30	Max tool-loop turns for glm_agent before it stops.
GLM_AGENT_BASH_TIMEOUT_MS	120000	Per-run_bash command timeout inside glm_agent.
GLM_OFFPEAK_MODEL	glm-5.2	Candidate model(s) for auto off-peak. Comma list allowed; router auto-picks.
GLM_PEAK_MODEL	glm-5.2	Candidate model(s) for auto at peak. Comma list allowed; include a no-surcharge model (e.g. glm-4.7) to dodge the peak tax.
GLM_CHEAP_MODEL	glm-4.5-air	The cheap model (used in the full-GLM launcher's Haiku slot).
GLM_PEAK_START_CN	14	Peak window start, China hour (UTC+8).
GLM_PEAK_END_CN	18	Peak window end (exclusive), China hour (UTC+8).

Peak-aware routing & cost

China peak window is 14:00–18:00 (UTC+8). The glm-5.x family carries a surcharge at peak (~3x peak / ~2x off-peak), so when auto lands on a glm-5.x model at peak the router routes less work to GLM; if you list a no-surcharge model (e.g. GLM_PEAK_MODEL=glm-5.2,glm-4.7) the router prefers it at peak and GLM stays fine to use. The cost bias keeps GLM the default either way — even at peak it is cheaper than the main model.

What stays on the main model: sensitive / secret code, vision input, parallel fan-out,

128K context, latency-tight loops, and heavy dependent tool-loops (the router's hard overrides).

Proof it's really GLM

• usage.jsonl ledger — every GLM call is appended on disk with model + input_tokens + output_tokens. Claude: ~/.claude/glm-mcp/usage.jsonl; Copilot: ~/.glm-mcp/glm-mcp/usage.jsonl. Independent of the Z.ai dashboard.
• glm_status — prints the cumulative ledger totals (calls, tokens, per-model counts).
• === GLM STATS === block — printed after every glm_agent run: model, tokens delegated, iterations, files changed, est. cost vs Opus.

If the ledger is empty, GLM was never called — the work ran on the main model.

Oversight & safety

• dry_run: true on glm_agent — GLM proposes a full diff and writes nothing; approve before applying.
• Git checkpoint revert line — printed after every real glm_agent run (when the workdir is a git repo), so you can undo in one command.
• Key isolation — GLM_API_KEY lives only in the git-ignored .env; it is never baked into the npm packages (scripts/publish-server.mjs scans every pack for .env / usage.jsonl / node_modules and fails loudly).
• Data residency — GLM traffic goes to Z.ai servers in China. Keep secrets and regulated code on the main model; the router's sensitive flag forces it there.

Development / CI

CI (see .github/workflows/ci.yml) runs: syntax checks on the server and every installer/hook/script, the keyless stdio smoke (scripts/smoke-stdio.mjs) that asserts the four-tool MCP handshake with no key on disk, a Docker introspection test (initialize piped into the built image), and an npm-pack secret scan (scripts/publish-server.mjs). PRs welcome — see CONTRIBUTING.md.

License

MIT © djerok · Canonical repo: https://github.com/djerok/glm-mcp