session-analyst

Session Analyst

Pull session data → aggregate patterns → produce a structured report → optionally hand off to slides/email tools for distribution.

Practice runs, SIP calls, and meeting-bot joins all land as sessions. Use ttai:list_sessions as the source of truth; ttai:list_sip_calls / ttai:list_meeting_bots only if you need the live call or bot schedule.

Prerequisites

Load ttai-agent (features/mcp) before any ttai: call (prefix ttai:; some clients show mcp__ttai__…).

Data model (what a session gives you)

Each session from ttai:list_sessions / ttai:get_sessions_batch includes:

• Identity: scenario_id, scenario_name, user_name, user_email
• Lifecycle: status, created_at, completed_at, duration_minutes
• evaluation_results: final_score, strengths, weaknesses, and report_card[] — per-topic {topic, score, note, weight}
• improvement_results: improvement_areas, action_items, resources
• extraction_results: structured variables (if the scenario extracts them)
• transcript_url (signed URL — fetch it for the conversation text) and analytics_url (human-viewable analysis page)

report_card topics are the backbone of aggregation: they are consistent within a scenario because they come from its rubric.

Workflow

Step 1: Scope

1. Load ttai-agent/kb/operating-model.md. Reuse a current, verified workspace context; otherwise call ttai:list_organizations. Team analysis almost always needs an org_id — pass it on every call, along with is_org: true on ttai:list_sessions for org-wide data.
2. Resolve the scenario: ttai:list_scenarios if the user gave a name, not an ID.
3. Confirm the window and population: which scenario(s), which date range (from_date / to_date), which people (user_email filter), how many sessions.

Step 2: Pull

• ttai:list_sessions with scenario_id, date filters, and pagination (page, limit). Iterate pages until you have the requested population — check the page metadata rather than assuming one page is everything.
• Sessions missing evaluation_results: either exclude them from scoring aggregates (note the count), or backfill — call ttai:post_process_session for each, then re-fetch after a wait and check that evaluation_results appeared. Backfill only when the user needs completeness.
• Deep dives (outliers, disputed scores): ttai:get_sessions_batch with the session IDs, then fetch transcript_url contents for the actual conversation.