classification_statisticsCount how many conversations belong to each classification for an organization to analyze support ticket distribution.
How many conversations fall into each classification of an organization.
| 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, and destructiveHint=false, so the safety profile is fully covered by structured data. The description adds nothing beyond that profile: it does not state whether closed/spam conversations are counted, whether a time range applies, or how the grouping is structured, which matters for an aggregate tool.
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 sentence with zero filler and the resource/scope front-loaded. It is appropriately sized for the schema complexity, though it is arguably thin for a statistics endpoint that has filtering behavior worth stating.
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 no output schema, the description must at least imply the return shape, and it does (a count per classification). However, it omits whether results respect filters, time windows, or conversation states, leaving the agent unable to predict the exact scope of the counts.
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?
There is a single required parameter (organizationId) at 100% schema description coverage, and the schema itself already tells the agent to use organization_list to find it. The description adds no parameter meaning, so the baseline of 3 for fully documented parameters applies.
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 relationship: conversation counts grouped by classification, scoped to an organization. It conveys the aggregate nature of the tool clearly, but does not distinguish itself from siblings like classification_list, classification_impact, or conversation_stats, so an agent must infer which classification-related aggregate to call.
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?
No when-to-use guidance, no prerequisites, and no mention of alternatives such as conversation_stats, conversation_totals, or classification_list. The agent learns what the tool returns but not when it is the right choice over neighboring statistics tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.