glm-mcp-copilot — GLM as a cheap delegate for GitHub Copilot (VS Code)

Use the GLM model (Zhipu / Z.ai) as a ~10× cheaper delegate inside GitHub Copilot / Copilot Chat (VS Code agent mode). It's the same GLM MCP server used by the Claude Code version — Copilot calls glm_agent / glm_delegate / glm_recommend / glm_status to offload work to GLM.

Sibling package: glm-mcp-claude (the Claude Code version). Same server, different host.

What you get

• The glm MCP server registered in VS Code (agent mode) — tools:
  • glm_agent — GLM works your repo directly (read/write/edit/run), returns a concise summary + stats.
  • glm_delegate — GLM drafts text you place. Opt-in (hidden by default; glm_agent handles text-only tasks too) — set GLM_DELEGATE=on to expose it.
  • glm_recommend — free advisory: GLM vs the default model.
  • glm_status — usage ledger (proof of GLM tokens spent) + config.
• A GLM custom agent (subagent) — restricted to the glm tools, so it must delegate to GLM (the Copilot analog of the Claude glm subagent). Pick it from the chat mode dropdown or hand off to it.
• A delegation-policy instructions file so Copilot offloads to GLM automatically.
• A PreToolUse auto-routing hook (glm_router_hook.mjs) — fires before the default model does work itself and nudges delegating to GLM (the Copilot analog of the Claude glm_subagent_router hook). Installed to .github/hooks/glm.hooks.json per-project, or ~/.copilot/hooks/glm.hooks.json globally. Non-blocking (always allows the tool call). VS Code preview feature.

Prerequisites

• VS Code with GitHub Copilot + Copilot Chat, and Agent mode available (MCP support).
• Node.js ≥ 18.
• A Z.ai / Zhipu GLM Coding Plan API key — https://z.ai (the only paid key needed).

Install

# from npm:
npx glm-mcp-copilot --key YOUR_ZAI_API_KEY

# or clone the repo and run the Copilot installer:
git clone https://github.com/djerok/glm-mcp
node glm-mcp/copilot/install-copilot.mjs --key YOUR_ZAI_API_KEY

Run it from your project folder (it sets up that workspace). It:

1. installs the GLM MCP server to ~/.glm-mcp/glm-mcp/ and runs npm install,
2. writes your key to that server's .env,
3. registers the server in .vscode/mcp.json (VS Code's servers format),
4. installs the GLM custom agent → .github/agents/glm.agent.md,
5. writes .github/copilot-instructions.md (the delegation policy),
6. installs the PreToolUse auto-routing hook → .github/hooks/glm.hooks.json.

Global (all projects)

Set it up once for every workspace with --global:

npx glm-mcp-copilot --global --key YOUR_ZAI_API_KEY

Global mode writes to VS Code's user config instead of one workspace:

• the glm server → the user mcp.json (all workspaces),
• the GLM custom agent → ~/.copilot/agents/glm.agent.md,
• the delegation policy → ~/.copilot/instructions/glm.instructions.md (with applyTo: '**'),
• the PreToolUse auto-routing hook → ~/.copilot/hooks/glm.hooks.json,
• and it registers those locations + enables agent mode in user settings.json (chat.agentFilesLocations, chat.instructionsFilesLocations, chat.agent.enabled).

Uses the current (non-deprecated) instructions mechanism — .instructions.md files, not the old codeGeneration.instructions settings array (deprecated in VS Code 1.102; the installer migrates off it). Use --vscode-user-dir PATH if your VS Code User folder isn't auto-detected (Insiders/VSCodium/portable).

Then in VS Code: Reload Window → open Copilot Chat → Agent mode → start the glm server (MCP: List Servers). Ask Copilot to do a coding task; it will call glm_agent.

How it differs from the Claude Code version

Essentially full parity — Copilot now has all three primitives:

• glm_* MCP tools in agent mode (same server as the Claude edition).
• A GLM custom agent (subagent) restricted to the glm tools — the analog of the Claude glm subagent (forced to delegate to GLM). Invoke it from the mode dropdown or via an agent handoff.
• A PreToolUse agent hook (glm_router_hook.mjs) that auto-nudges delegation — the analog of the Claude glm_subagent_router hook: before the default model does work itself, it suggests delegating to glm_agent (non-blocking; it only injects advisory context, never denies a tool call).
• Instructions files steer delegation (the CLAUDE.md equivalent).

Small differences that remain:

• VS Code hooks ignore the matcher, so the hook fires on every tool call and filters by tool_name internally (and advises at most once per session to stay quiet).
• VS Code hooks are a preview feature; flip them on in Copilot settings if your build hides them.
• There is no separate glm-code full-GLM launcher (Claude's standalone all-GLM entry point).

Everything else — the GLM agent loop, peak-aware model pick, cost bias, token cap, usage ledger, dry_run oversight — is the same server, so it behaves identically once a tool is called.

Example: the GLM agent in VS Code

Pick the GLM agent from the chat mode dropdown (or let the main agent hand off to it) and give it a self-contained task. Because the agent is restricted to the glm tools, it can't edit or run anything itself — it reads to understand, then routes the work to GLM. The activity feed looks like this:

GLM: Add model routing instructions
  Work in the repository at C:\Users\ericc\Desktop\robotr. Implement the request by adding…
  🔍 Searched for files matching **/*.md — 50 matches          ← read-only recon to specify the task
  📖 Read CLAUDE.md
  🔍 Searched for files matching .gitignore — 1 match
  ⚙  glm_agent(task="Add model routing instructions…", workdir="C:\Users\ericc\Desktop\robotr")
       iter 1/30 · 0 tok · 0 tok/s
       iter 1/30 · 640 tok · 42 tok/s
       iter 2/30 · write docs/routing.md
       iter 2/30 · 1,240 tok · 38 tok/s          ← live progress: iteration · tokens · tok/s · action
  === GLM STATS ===
  model: glm-5.2 | 3,180 tokens delegated to GLM | est. $0.006 (~10x cheaper)

The read/search steps are expected and cheap — they're how the agent builds a precise task; only glm_agent (billed to GLM) does the writing. If you'd rather skip the recon on a task you've already specified, tell it: "call glm_agent directly with workdir … — no repo scanning first." glm_delegate does not appear here by default: glm_agent also handles text-only subtasks, so the orchestrator can't mis-route to the text-only tool (expose it with GLM_DELEGATE=on if you want it).

Configuration

Same .env knobs as the Claude version, in ~/.glm-mcp/glm-mcp/.env: GLM_API_KEY, GLM_BASE_URL, GLM_COST_BIAS, GLM_CAP, GLM_MAX_TOKENS, GLM_OFFPEAK_MODEL / GLM_PEAK_MODEL, etc. See glm-mcp/.env.example.

Verifying GLM usage

glm_status (or ~/.glm-mcp/glm-mcp/usage.jsonl) logs every GLM call (model + tokens) — independent proof that work ran on GLM, not Copilot's default model.

Security

• Your key lives in ~/.glm-mcp/glm-mcp/.env (git-ignored) — not committed, not in the npm package.
• GLM routes through servers in China — keep secrets/regulated code on the default model.

License

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