Local MCP server

Published guide: dynolab.dev/guide.html

Dyno includes a stdio MCP bridge for assistants and agent tools on your Mac. It exposes the Research Lab through the official MCP Python SDK. It is not an inference server, and it does not listen on a network port.

Setup

Start Lab → Experiments → Start lab in Dyno, or run dyno lab --port 8980. The bridge connects to that existing loopback service. Starting MCP alone does not start a Lab worker, download a model, or submit an experiment.

From a checkout of this repository:

python3 -m venv .venv
source .venv/bin/activate
pip install -e '.[serve,mcp]'
dyno mcp --port 8980

A stdio server waits for an MCP client; it has no interactive terminal prompt. Configure your client's MCP server settings using either the installed dyno executable (use its absolute path if your client does not inherit PATH), or the launcher bundled in an updated Dyno app:

{
  "mcpServers": {
    "dyno": {
      "command": "/Applications/Dyno.app/Contents/MacOS/dyno-cli",
      "args": ["mcp", "--port", "8980"]
    }
  }
}

The launcher resolves its own runtime after relocation. Adjust the app path if Dyno is installed elsewhere. The mcpServers envelope is a common client format; use your client's equivalent command and argument fields when its format differs. The 0.2.0 release contains this launcher. Upgrade older installations from GitHub Releases.

Tools

Tool	Purpose	Effect
`serving_capabilities`	Discover resident capture support on an inference port	Read only
`serving_inspect`	Capture norms using loaded weights	Brief extra GPU/workspace use, no weight copy
`lab_health`	Check service availability	Read only
`lab_jobs`	List recent experiment metadata	Read only
`lab_job`	Read configuration, status, results or errors	Read only
`lab_submit`	Submit `inspect`, `compare`, `probe` or `sae`	Loads a separate model and runs an experiment
`lab_cancel`	Cancel the selected experiment worker	Stops that worker only
`lab_artifacts`	List HTTP download links for saved artifacts	Read only

Tool arguments

Tool	Arguments
`lab_health`, `lab_jobs`	None
`lab_job`, `lab_cancel`, `lab_artifacts`	`job_id`: returned job identifier
`lab_submit`	`operation`, `model`, `settings` object; keep model/operation out of settings
`serving_capabilities`	`port` (default 8971)
`serving_inspect`	Required `prompt`, `layers`; optional `port` (8971), `max_input_tokens` (128)

For a first resident capture, ask your connected assistant: “Check the Dyno serving capabilities on port 8971, then capture layers 4 and 8 for the raw prompt ‘The capital of France is’ using the loaded model.” Choose layers that exist in your model. This returns measurements directly; there is no job ID to poll.

The resource dyno://lab/openapi returns the service's OpenAPI schema. Large arrays stay in artifact files; use the SDK or HTTP URLs to download them.

Example lab_submit arguments:

{
  "operation": "inspect",
  "model": "mlx-community/Qwen1.5-0.5B-Chat-4bit",
  "settings": {
    "prompt": "The capital of France is",
    "layers": [4, 8],
    "max_input_tokens": 256
  }
}

Submission returns a job ID immediately. Call lab_job to poll that ID, then lab_artifacts for files. Only one experiment can run at a time. A model ID may download weights; a local model path uses your existing download.

Resources and privacy

The MCP bridge and Python SDK use the same job API. The native UI's memory/GPU admission check does not apply to these clients. Check headroom and serving activity before submitting, especially for a large model. Separate processes still compete for unified memory and GPU bandwidth. Cancellation never stops a serving model; the bridge cannot reconfigure or unload inference servers.

Job inputs and results are stored locally, but a connected assistant can read them and may send tool results to its model provider. Choose which jobs and prompts you expose accordingly. There is no remote MCP transport, shell tool, arbitrary file reader, or automatic connection to an assistant in this release.

Troubleshooting

Connection refused: start Lab and match the configured port.
MCP dependency missing: install .[mcp] from the checkout, or use the updated app launcher.
A job is already running: poll its status or cancel that Lab job.
Older Lab service: quit the instance that owns the old Lab process and reopen the updated build.
Model error: inspect the job error and verify its architecture, layers and input limits. Hybrid Qwen layers are covered by regression tests; support is not universal across every MLX model.

serving_inspect connects directly to an updated Dyno inference endpoint and does not require the Lab job service. Use it for read-only norms; use lab_submit for isolated jobs.