Connect Claude to Assembled, the workforce management platform for modern support teams. Inspect schedules, forecasts, and real-time performance across queues, agents, and sites. Diagnose SLA misses, run what-if scenarios, and analyze trends without exporting a single report. Take action directly from chat: adjust forecasts ahead of peak events, publish overtime when you're trending hot, or open VTO when you're overstaffed. Combine Assembled data with your HRIS, BI tools, or Slack to answer cross-system questions no single tool can. Built for WFM teams and support leaders who need answers in seconds, not hours.
overtime_seconds), - approved paid time off (pto_seconds), - accepted voluntary time off (vto_seconds). Use this when the user asks "how much OT / PTO / VTO is each agent scheduled for next week" or similar workforce-management questions. Each row is one agent in the resolved filter scope, including agents with zero activity (their buckets are 0).interval, in seconds) and are in UTC. Values are heads: the average number of agents working during the bucket. Heads add up across queues, sites and channels within a bucket, but not across buckets; average them over time (or multiply by the bucket length in hours to get agent-hours). - required / committed: uploaded by the BPO; null when nothing was uploaded for that bucket. - scheduled: productive time on the schedule (schedule_id, or the master schedule when omitted). - actuals: in-adherence time from cached metrics; null for future buckets and partial for today. Compare scheduled or actuals against required to find under- or over-staffed intervals.contacts_received (and cases_opened) for legacy companies, or case_volume and units_of_work for companies on the newer metrics configuration. When both case_volume and units_of_work are present, the channel's configured contact input (case volume or units of work) determines which one the forecast is built on, and that setting is not included in this response. Report both and name the field you are quoting rather than presenting either as the single "volume" number. The bucket interval and schedule are chosen automatically: bucket size is picked from the window length (15m for <1 day, 1h for 1 day to 1 week, 1 day for >1 week) and the company's master schedule is always used. These are intentionally not exposed as tool arguments. Windows longer than ~6 months (184 days) are rejected: forecasted vs actual is intended for operational analysis. For year-plus historical trend analysis, use the analytics/metrics tool instead, or split the question into several shorter windows and call this tool per window.queue, site, team, or skill MUST be set; channel alone is rejected. To resolve a user-facing name to an external ID, call get_workforce_config() first and match on the appropriate *[].name. If nothing matches, ask the user to clarify rather than guessing. If the server rejects the request with 400, the rejection details from the upstream JSON body — agent_count, filters_applied, tier_caps, and a suggested_action string — arrive as keys on error itself; a non-JSON rejection arrives as text in error["error"] instead. Read `suggested_action` and follow it verbatim on retry rather than guessing permutations.is_default is True when nothing is saved for that channel/queue: the row then shows Assembled's built-in defaults (shrinkage 0, agent_productivity 8, average_handle_time 10, ...), not the values of the saved channel-default row, with any SLA-group service level applied on top. updated_at is only set on explicitly saved rows. shrinkage_source is 'flat' when the shrinkage value is used directly and 'forecast' when the effective shrinkage comes from the shrinkage forecast; the forecast-derived value itself is not included. Units match the queue Settings UI: shrinkage and service_level_percent are percentages in [0, 100] (30.0 means 30%), average_handle_time is minutes per contact, agent_productivity is work items handled per agent per hour, service_level_seconds is seconds, and concurrency is simultaneous contacts per agent. Pages are limit / offset with total (rows matching the filters) in the response; keep requesting with offset += len(staffing_parameters) until offset >= total.summary (total counts per dimension) and the full channels list (usually small). Entity rows are either a browse page for one dimension, search hits, or empty when summary_only is true. Browse (no `query`) — paginate one dimension at a time: - Omit filter_type to default to queues (page through queue names). - Use filter_type = sites, skills, teams, or schedules to list those instead. The schedules dimension returns the company's active schedules sorted master-first; each entry has a live flag (true for the master) and an id that can be passed as schedule_id to get_schedule to read a non-master schedule. - Use page (1-based) and page_size (max 500; default 100). - Check has_more for additional pages. Search (`query` set) — substring match on names (case-insensitive): - Omit filter_type to search all of queues, sites, skills, teams, and schedules (each type capped at 50 matches to keep payloads small). - Set filter_type to a single type to search only that list (up to page_size matches, capped at 500). - Use this to resolve a user phrase (e.g. "support", "draft") to an external id before calling tools that require UUIDs. Summary only — set summary_only=true to get counts plus channels only (no queue/site/skill/team/schedule rows). Use for "how many queues do we have?" Note: query_metrics uses names directly; use this tool when another tool needs external IDs or you need to confirm an entity exists. Returns GetWorkforceConfigResult with summary, channels, pagination metadata (page, page_size, filter_type, has_more, summary_only), optional echoed query, and typed lists (queues, sites, skills, teams, schedules).Click any prompt to copy it.
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25
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3
Prompts
ChatGPT, Claude
Platforms
Category
Work ManagementWorks with
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