conversation_list_meta

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

With no annotations provided, the description must convey behavioral traits. It only mentions fetching counts, leaving unclear aspects like read-only nature, side effects, authentication needs, or rate limits. The minimal text fails to disclose these key behaviors.

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?

The description is extremely concise (4 words) but omits critical details, making it under-specified rather than efficiently concise. It sacrifices completeness for brevity, scoring average on this dimension.

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?

Given the tool has 5 optional parameters and no output schema, the description should explain the return format (e.g., separate counts per status) and how parameters affect results. It fails to provide this context, leaving the agent uninformed about what to expect.

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?

The description adds no information about the parameters beyond what the schema provides. With schema description coverage at 40% (only 'q' and 'status' have descriptions), the description should clarify ambiguous parameters like 'labels', 'team_id', and 'inbox_id', but it does not.

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?

The description 'Get Conversation Counts' clearly states the tool's function as retrieving counts of conversations, distinguishing it from sibling tools like 'conversation_list' that list conversation details. However, it does not specify whether counts are aggregated or per status, slightly reducing specificity.

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 guidance is provided on when to use this tool versus alternatives such as 'conversation_list' or 'conversation_filter'. The tool's purpose of getting counts is distinct, but the description lacks explicit usage context, such as prerequisites or exclusion criteria.

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