inbox_statisticsRetrieve conversation volumes per inbox for an organization to track support workload and identify high-traffic channels. Provide an organization id to get inbox-level statistics.
Statistics per inbox of an organization (conversation volumes).
| Name | Required | Description | Default |
|---|---|---|---|
| organizationId | Yes | The organization id. Use organization_list to find it. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered. The description adds only the observation that the statistics are conversation volumes, and says nothing about time window, granularity, or whether results are paginated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One short sentence with the resource front-loaded and no wasted words. It is efficient for a single-parameter read tool, though the parenthetical placement makes the scope slightly awkward.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With full annotation coverage, a 100%-covered required parameter, and no output schema, an agent can call this correctly. However, the description never indicates what the statistics measure over (time range, breakdown dimensions), leaving the expected result shape entirely unstated in a suite full of overlapping analytics tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and the single parameter is documented in the schema itself, including a pointer to organization_list. The phrase 'of an organization' restates what the schema already conveys, so nothing materially new is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific resource (statistics per inbox) and scopes it to an organization, with a clarifying parenthetical that the statistics concern conversation volumes. That is much more informative than a tautology, though it never distinguishes itself from sibling analytics tools like conversation_stats, conversation_totals, or conversation_metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no when-to-use guidance at all. In a suite containing conversation_stats, conversation_stats_by_member, conversation_metrics, and conversation_totals, the agent is given no cue for when per-inbox aggregation is the right pick over those alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.