analysis_frequency

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

With no annotations, the description carries the full burden. It does disclose the core behavior — tallying items and returning a ranked subset — but says nothing about ordering guarantees, tie-breaking, case sensitivity, or how the result set is truncated. The presence of an output schema relieves it of describing the return shape.

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?

A single eleven-word sentence with the operation front-loaded and no filler. It is efficient, though the terseness leaves '_item_' vague enough that it trades away some precision.

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?

For a simple two-parameter tool with an output schema, the essentials are mostly covered, but the one non-obvious parameter (top) and questions like case sensitivity go unaddressed in both schema and description. Adequate, not complete.

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 0%, so the description must compensate. 'Each item' loosely maps to the items array, but the top parameter (default 20) is never mentioned or explained — 'the most common ones' only hints at a limit without identifying it or its default.

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 gives a specific verb+resource: 'Count occurrences of each item' and adds the output framing 'return the most common ones'. That distinguishes it from pure aggregators like analysis_stats. It does not name or contrast any sibling explicitly, which keeps it from a 5.

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, no exclusions, and no mention of alternatives such as analysis_stats or ai_keywords for related text counting tasks. The usage is only implied by the verb 'count'.

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