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Summer

by ai.gosummer
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Read tickets opened and closed over 1, 7 or 30 company-local calendar days or all recorded history

summer_reports_ticket_activity
Read-onlyIdempotent

Read tickets opened and closed over 1, 7 or 30 company-local calendar days or all recorded history. Includes zero-filled hourly, daily or monthly bars, period totals, open tickets now and closures without usable dates. backlog contains from, historyEstimated and bounded points (at, open) for open issues over time, reconstructed from creation and latest valid closure dates. Past reopen cycles and resolved issues without dates cannot be reconstructed. The final backlog point equals openNow. Read-only Intercom archives are excluded from operational issues, messages and closures in ticket activity, service metrics and samples; coverage.archivedImports gives their count. Original archives are retained unchanged. rolling contains a dense series of original-timestamp trailing counts with windowSeconds, stepSeconds, from and points (at, opened, closed). Windows are one hour for 1d, 24 hours for 7d/30d, and 24 hours, 7 days, 30 days or 365 days for all-time depending on history length. At most 722 points include the exact observation time; lookback before the selected period is included in rolling counts, while period totals remain exact calendar counts. Reopened tickets are open; a completed run is not a closure. Changes nothing.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "company": "<company>",
      "environment": "<environment>"
    },
    "name": "summer_reports_ticket_activity"
  }
}

Input Schema

NameRequiredDescriptionDefault
periodNoCompany-local calendar days including today. 1d is hourly today; all includes full recorded history, grouped monthly after 120 days.30d
companyYes
environmentYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations, the description discloses substantial behavioral traits: Intercom archives are excluded from operational counts with a coverage.archivedImports counter, past reopen cycles and undated resolved issues cannot be reconstructed, the final backlog point equals openNow, and 'Changes nothing.' These are meaningful caveats an agent could not derive from the readOnly/destructive hints alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is front-loaded with the core purpose, which is good, and the length is partly justified since there is no output schema to explain return values. However, sentences are long and dense, and some material (archive exclusion / 'original archives are retained unchanged') is repeated, hurting readability and efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden of explaining returns, and it thoroughly covers bars, period totals, openNow, backlog composition, and the rolling dense series including window/step logic and point caps. For a complex analytical report tool this is complete enough for an agent to call and interpret it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 33% (only 'period' is documented), so the description must compensate, and it does: it explains company-local calendar days, hourly vs daily vs monthly grouping, and the rolling window sizes per period. Company and environment parameters are not described, but they are near-self-explanatory (uuid, staging/production enum), keeping this just shy of 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb + resource ('Read tickets opened and closed') and scopes it ('over 1, 7 or 30 company-local calendar days or all recorded history'), so the operation is unmistakable. It doesn't explicitly differentiate itself from the many summer_reports_* siblings, which keeps it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through period semantics and the return-shape explanation, but there is no explicit 'use this when...' or reference to alternative report tools for related questions. An agent can infer it returns ticket activity statistics but is given no routing guidance versus siblings like summer_reports_service_metrics or summer_reports_cancellations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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