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

Annotations already declare the safe read-only, idempotent, closed-world profile. The description adds genuine behavioral context beyond that: the return shape (one row per platform present in the filtered response set) and the ordering (sorted by visibility desc). It omits pagination/limits, which keeps it short of a 5.

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?

Two dense sentences, front-loaded with the resource scope and followed by the row/sort semantics. Every clause carries information; nothing is repeated from the name or annotations.

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 usefully covers the return value (per-platform rows with named metrics, sorted by visibility). Annotations handle safety and the 10-param schema is fully documented. The only real gap is the absence of any routing guidance among the many analytics siblings.

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%, so every parameter (tags, monitors, segments, countries, platforms, dates, interval) is already documented in the schema. The description's 'filtered response set' hints that the optional filters apply, but adds no syntax or semantics beyond what the schema supplies. Baseline 3 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 and scope: 'Per-AI-platform aggregates' with an explicit metric list (visibility, share-of-voice, sentiment, position, used_count) scoped to a single brand. The 'per-AI-platform' framing distinguishes it from the other list-*-analytics siblings, though it never names an alternative directly.

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 is provided. The agent is not told how this differs from list-brand-analytics-tool, list-monitor-analytics-tool, list-prompt-analytics-tool, or the get-*-time-series tools, nor when this aggregate view is preferred over them. Only the 'aggregate' framing implicitly signals non-time-series usage.

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