Tools provided
Tools the agent exposes — your AI client calls them automatically when it needs them.
- run_full — Run the agent end-to-end: fetch Jira sprint history, run Monte Carlo velocity forecast, generate LLM summary, optionally post to Notion and Slack.
- analyze_velocity — Given raw sprint data, compute velocity statistics and Monte Carlo delivery forecast deterministically (no LLM, no network).
- forecast_delivery — Run Monte Carlo simulation from a velocity distribution to produce P50/P80/P95 delivery dates.
- create_notion_report — Create a Notion page with the velocity forecast report.
- send_slack_summary — Post a velocity forecast summary to a Slack channel.
- list_capabilities — List the agent's static capabilities and credential slots.
- plan_inputs — Plan/brainstorm the inputs for a tool: returns questions, schema, and a ready-to-edit example.
- discover_intent — Proactive intent discovery: state a goal, get clarifying questions and a proposed input before running.
