Zed setup

Two independent pieces: the context server (lets Zed’s agent panel submit and watch queue jobs) and the model provider (lets Zed chat with whatever backend is serving your models). You can set up either without the other.

1. The context server (zed-extension/)

This is a real Zed extension — zed-extension/extension.toml + zed-extension/src/lib.rs, compiled to WASM — not a hand-edited settings.json block. It registers rewriter-queue mcp as a context server and defaults its arguments to ["mcp"], so you don’t have to remember to add that yourself.

Build and install it as a dev extension:

rustup target add wasm32-wasip2
cargo build -p job-queue --release   # the rewriter-queue binary itself

In Zed: open the command palette and run zed: install dev extension, then point it at the zed-extension/ directory. Zed builds and loads the WASM extension itself — you don’t need to invoke the WASM build by hand.

Tell it where the rewriter-queue binary is, if it’s not on your PATH, in settings.json:

{
  "context_servers": {
    "rewriter-queue": {
      "command": {
        "path": "/absolute/path/to/rewriter-queue/target/release/rewriter-queue"
      }
    }
  }
}

Leave out command entirely if rewriter-queue is already on PATH (e.g. cargo install --path job-queue).

Point it at a queue server via ~/.config/rewriter/config.toml:

[queue]
url = "http://192.0.2.10:8003"   # wherever `rewriter-queue serve` is running

…and the token it expects, in ~/.config/rewriter/queue-token. Without a [queue] section, every command (including the ones the context server runs on your behalf) falls back to a local, file-backed queue — fine for trying this out on one machine, but you’ll usually want a real server if you’re also running the inference backend on a GPU box (see remote-deployment.md).

Once it’s connected (check the context-servers panel for a green status, not a red error dot), just ask Zed’s agent about your jobs in plain language — “what jobs are in the queue?”, “submit a synthesis run for ./src with a 20-iteration budget”, “download job 3’s artifacts to ~/Desktop” — and it calls the right tool (queue_submit/queue_list/queue_status/queue_artifacts/ queue_artifact/queue_download/queue_fetch/queue_cancel) itself. There’s no slash command for this — MCP tools are things the model calls on its own, not something you invoke directly, unless a server also exposes MCP prompts (this one doesn’t).

A live terminal view is also available without going through Zed’s agent at all: .zed/tasks.json in this repo defines a “Queue: watch” task that runs rewriter-queue watch.

2. The model provider

This is unrelated to the extension above — it’s Zed’s own openai_compatible provider type, pointed at whatever OpenAI-compatible server (llama.cpp, vLLM, etc.) you’re running your models on. Add it to settings.json:

{
  "language_models": {
    "openai_compatible": {
      "my-backend": {
        "api_url": "http://192.0.2.10:8001/v1",
        "available_models": [
          {
            "name": "your-model-id",
            "display_name": "My Model",
            "max_tokens": 128000,
            "capabilities": {
              "tools": true,
              "images": false,
              "parallel_tool_calls": false,
              "prompt_cache_key": false,
              "chat_completions": true
            }
          }
        ]
      }
    }
  }
}

"tools": true matters if you want Zed’s agent to actually call tools through this model — verified working with both llama.cpp’s --jinja mode and vLLM’s --enable-auto-tool-choice, but not every locally-served model reliably emits well-formed tool calls. If the agent starts guessing at shell commands instead of calling a tool by name, that’s usually the model’s tool-calling support, not a Zed or context-server bug — sanity-check by sending the same tool schema directly to the model’s /v1/chat/completions endpoint and see what comes back before assuming the wiring is broken.

Running two backends side by side (e.g. a general model and a coding-specialized one) on the same box just means two openai_compatible entries pointed at two different ports — see remote-deployment.md for sizing multiple backends into one machine’s memory.