Activity Insights

Flow: Trigger → Connector check → Resolve board → Build query → Fetch insights → Synthesize.

Input

• Optional: argument describing the analysis (e.g., "rep activity this week", "call breakdown for the team").
• Optional: rep name, date range, or grouping preference.

Output

A synthesized summary with specific numbers and rep-level breakdowns. Never raw JSON.

Knowledge

• get-activity-insights returns aggregated counts — not individual activity records. For activity history with content, use get-timeline-items.
• Default time range is 7 days — no need to pass a date if the user asks about "recent" or "this week".
• groupBy accepts 1–2 fields: ["type"] for activity breakdown; ["userId", "type"] for per-rep breakdown; ["userId"] for rep totals only.
• board_id is required — always resolve it before calling the tool.
• Present results as natural language insights — never dump the raw results array.

Tools (MCP)

• get-activity-insights — aggregated activity stats (counts, duration) grouped by rep, type, or item.
• get_user_context — user identity and account info.
• search / get_board_info / get_board_items_page — resolve boards and items.
• list_users_and_teams — resolve rep names to user IDs for filtering.
• get-timeline-items — fallback for item-level activity detail (content, not aggregates).

When to use each groupBy

Step 0: Connector check

1. Call mcp__monday__get_user_context. On error → print connector install prompt, stop.

Step 1: Resolve board

get-activity-insights requires a board_id.

1. If a board name is clear from the argument → mcp__monday__search or mcp__monday__get_board_info to resolve the ID.
2. If multiple CRM boards exist → ask: "Which board should I analyze — Deals, Contacts, or Leads?"
3. No board context → mcp__monday__search({ query: "deals" }) to find the most likely CRM board. Still ambiguous → ask.

Step 2: Build query parameters

Parse the argument:

groupBy:

• Reps, "who", "by rep", "each rep" → ["userId", "type"]
• "breakdown", "by type", "what kind" → ["type"]
• Default → ["userId", "type"]

Date range:

• "this week" → 7-day default (no params needed)
• "last month" → fromDate: 30 days ago
• "today" → fromDate: start of today
• Explicit dates → parse and pass fromDate / toDate

Filters:

• Rep name mentioned → resolve via mcp__monday__list_users_and_teams, pass userIds
• Item/deal name mentioned → resolve item ID, pass itemIds (or redirect to get-timeline-items for content)

aggregationType: default count. Use sum / avg for duration questions ("how long were the calls").

Step 3: Fetch and synthesize

1. Call mcp__monday__get-activity-insights with resolved parameters.
2. If total_results === 0: "No activities found on <board> in the last <window>. The team may not have logged activities yet, or try a wider date range."
3. Synthesize results:
   • Lead with the headline: "Your team logged 92 activities this week."
   • Break down by type: "42 emails, 28 calls, 15 meetings, and 7 notes."
   • Call out top performers if grouped by rep: "Alex led with 34 activities, followed by Sam (28) and Jordan (19)."
   • Flag low activity if relevant: "4 reps logged fewer than 5 activities this week."

Cross-skill handoffs

• To log-activity: insights show a rep with zero activity → "Want to log a recent call or meeting for them?"
• To run-sequence: low outreach volume → suggest enrolling contacts in a sequence.
• From daily-briefing: briefing surfaces activity gaps → this skill provides the full breakdown.
• To get-timeline-items: user asks for actual content (what was said, meeting notes) rather than counts.

Error handling reference

Completion criteria

• [ ] Connector check passed.
• [ ] board_id resolved before calling the tool.
• [ ] Query parameters derived from context — not hardcoded defaults.
• [ ] Results synthesized into natural language — no raw JSON.
• [ ] Zero-result case handled gracefully.
• [ ] Item-level content requests redirected to get-timeline-items.