FarmVibes.AI MCP server

The farmvibes-mcp package lets Model Context Protocol clients inspect FarmVibes.AI workflows, submit runs, and retrieve results over stdio. It uses the existing Python client directly, so local and remote clusters behave the same way.

Install

Use a dedicated Python 3.11 environment. Do not install the MCP SDK into the FarmVibes service environment: the current MCP and service web stacks require different Starlette, Uvicorn, and OpenTelemetry versions.

python -m venv .venv-mcp
.venv-mcp/bin/pip install ./src/vibe_core ./src/farmvibes_mcp

The server uses the Python client’s existing target discovery:

remote_service_url and private/remote_api_token for a configured remote cluster;
service_url for a configured local cluster;
http://127.0.0.1:31108/ otherwise.

Run farmvibes-ai remote status to recover a missing remote URL or token. Tokens are never MCP arguments or results and do not belong in MCP configuration.

Configure an MCP client

For VS Code, add .vscode/mcp.json:

{
  "servers": {
    "farmvibes": {
      "type": "stdio",
      "command": "${workspaceFolder}/.venv-mcp/bin/farmvibes-mcp"
    }
  }
}

Clients that use the mcpServers format can launch the same executable:

{
  "mcpServers": {
    "farmvibes": {
      "command": "/absolute/path/to/.venv-mcp/bin/farmvibes-mcp"
    }
  }
}

Tools

Tool	Purpose
`list_workflows`	List workflows available on the selected cluster.
`describe_workflow`	Show a workflow’s inputs, outputs, parameters, and tasks.
`submit_run`	Submit GeoJSON geometry, a time range, and optional parameters.
`list_runs`	List runs, optionally filtering IDs and response fields.
`get_run`	Get run metadata and task details without the potentially large output.
`get_run_output`	Get a run’s status and output.
`cancel_run`	Request cancellation of a pending or running run.

submit_run requires a GeoJSON geometry and timezone-aware ISO 8601 timestamps. Submission is nonblocking: poll with get_run, then call get_run_output when the run is done.

Useful requests include:

“List the available FarmVibes workflows.”
“Describe the inputs and parameters for helloworld.”
“Run helloworld over this GeoJSON polygon from 2021-02-01T00:00:00+00:00 to 2021-02-11T00:00:00+00:00.”
“Check that run and show its output when it finishes.”

Try it in Codespaces

The repository devcontainer supports the local k3d cluster in Codespaces. A 32-core Codespace is fastest, but the setup is otherwise the normal local workflow:

python -m venv /workspaces/.venv-mcp
uv pip install --python /workspaces/.venv-mcp/bin/python \
  -e ./src/vibe_core -e ./src/farmvibes_mcp

FARMVIBES_AI_CONFIG_DIR=/workspaces/fv-config \
  /workspaces/.venv-mcp/bin/farmvibes-ai local setup \
  --auto-confirm \
  --cluster-name farmvibes-mcp \
  --servers 1 \
  --agents 1 \
  --worker-replicas 1 \
  --disable-telemetry \
  --host 127.0.0.1

Point the MCP process at the same configuration directory:

FARMVIBES_AI_CONFIG_DIR=/workspaces/fv-config \
  /workspaces/.venv-mcp/bin/farmvibes-mcp

Delete the test cluster when finished:

FARMVIBES_AI_CONFIG_DIR=/workspaces/fv-config \
  /workspaces/.venv-mcp/bin/farmvibes-ai local destroy \
  --auto-confirm \
  --cluster-name farmvibes-mcp

Current scope

This initial server supports stdio only. It does not expose prompts, resources, asset downloads, arbitrary existing-asset submission, or a blocking wait tool.