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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
notify_orchestratorC

Record stage completion and ask pipeline-runner for deterministic next transition.

validate_deferred_pathsC

Validate deferred item file paths and attempt path correction where possible.

get_pipeline_statusB

Return current pipeline status and best-effort next transition summary.

skip_stageA

Skip the current pipeline stage and advance to the next one.

Use when a stage is not needed for this task (e.g. skipping security review for a docs-only change, or skipping prd for a small bug fix). Only the current stage can be skipped. Gates that the skipped stage would have satisfied are auto-approved.

Returns the result including new current_stage and progress_line.

set_pipeline_modeB

Set the pipeline mode to supervised, assisted, or autopilot.

satisfy_gateC

Set a supervision gate to satisfied (true).

update_notesA

Merge key-value pairs into _notes in pipeline-state.json.

This is the ONLY correct way to write _notes.* fields. Do not use editFiles for _notes — all pipeline-state writes must go through MCP tools to maintain a single authoritative writer and prevent concurrent-write conflicts.

Performs a shallow merge at the top level of _notes: each key in updates replaces the corresponding key in _notes (or creates it if absent). Keys not present in updates are left unchanged.

replay_stageA

Reset a single stage to not_started for re-execution without clearing the pipeline.

Only available in supervised mode — too risky for autopilot/assisted. Cannot replay a stage if a downstream stage has already completed (must replay the chain from that point).

Preserves _notes for continuity. Increments the stage's retry_count. Writes a record to _notes._replay_log[].

pipeline_initA

Initialise a new pipeline state file from the template.

Requires confirm=True to proceed — this prevents accidental invocation mid-run. Use when starting a brand-new feature or hotfix pipeline.

If a pipeline-state.json already exists, it will be overwritten.

_bypass_active_check is an internal flag used by pipeline_reset to skip the active-pipeline guard. Do not set this from user-facing calls.

pipeline_resetA

Reset the current pipeline state and start a new pipeline run.

Identical to pipeline_init but semantically signals resetting an existing pipeline rather than creating a fresh one. Requires confirm=True. Use when a pipeline has closed and the user wants to start the next feature, or when explicitly restarting a failed/stale pipeline.

run_commandA

Execute a shell command in the workspace root and return stdout, stderr, exit code.

Use this for running tests (pytest, playwright), linters (black, ruff), formatters, build steps, or any other shell command the pipeline needs to execute hands-free. Intended for use by pipeline agents, not general chat.

Windows note: Do NOT use pytest-xdist parallel flags (-n auto, -n N) in commands passed to this tool. Worker subprocesses inherit stdout/stderr pipe handles and can prevent the tool from returning if a worker exits abnormally. Use plain pytest tests/ -q instead.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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