This command runs an MCP (Model Context Protocol) server so AI agents and automation tools can query KernelCI results and drive Maestro jobs.
MCP support is an optional extra:
pip install kci-dev[mcp]
Read-only dashboard query tools (trees, builds, boots, tests, hardware,
known issues) are always available and need no configuration. Maestro
node lookup tools are enabled when the configured instance has an api
URL, and job retry/checkout trigger tools when it also has a pipeline
URL and a token. See the config file documentation.
Use the top-level --instance option (kci-dev --instance staging mcp) to
select which configured instance the server uses.
Run with the default stdio transport for local agents:
kci-dev mcp
Example Claude Code registration:
claude mcp add kernelci -- kci-dev mcp
Run as an HTTP server (streamable HTTP transport):
kci-dev mcp --transport http --host 127.0.0.1 --port 8000
The HTTP transport has no authentication layer: anyone who can reach the port can call the exposed tools, including the job-triggering ones, using the token from your configuration. Keep it bound to 127.0.0.1, prefer the stdio transport for local use, and do not expose the port beyond hosts you trust.
Tools that change state (retry_job, trigger_checkout) are annotated
as non-read-only so MCP clients can ask for confirmation before calling
them.
Responses are sized for context-limited clients: get_summary returns
compact aggregates unless detail=true is passed, and the list tools
paginate (default limit of 20) and accept a fields list to return
only the named keys per entry. Prefer status/arch filters, small
limits and field projection when exploring large trees.