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

Annotations already cover safety (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description's added value is the data semantics: 'Figures are the newest snapshot (totals to date), not a per-day sum.' This is a meaningful behavioral note that prevents misreading the metrics as daily activity. It also clarifies that metrics are lifetime totals per network, adding context beyond the structured fields.

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 two sentences with no redundant filler. It front-loads the core purpose, then provides essential scoping and interpretation details. Every clause adds useful information, making it an efficient and well-structured definition.

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 convey the return shape, and it does: it lists the metrics, ranking order, and time filter. It also flags the snapshot semantics to set expectations. Minor gaps remain, such as not specifying the response format or whether platform is filtered, but these are inferable from the schema and the nature of the tool.

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 only 33% (only the 'days' parameter has a default noted). The description compensates partially by referencing 'last N days' (mapping to the 'days' parameter) and 'per network' (mapping to the 'platform' parameter), but it does not explain the 'limit' parameter or enumerate the supported platforms. The schema's enums and min/max provide some structure, so the gap is moderate rather than severe.

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 clearly specifies the query ('Which listings performed best on social'), the resource (listings), and the exact metrics (lifetime views/likes/comments/shares/saves per network). It also defines the ranking criterion ('ranked by views') and the time window ('last N days'), making the tool's function unmistakable. This also differentiates it from sibling list tools like list_properties and search, which handle broader listing retrieval.

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?

The description gives clear context for when to use the tool: when needing social performance for listings, with a defined time window and per-network breakdown. It does not explicitly name alternatives or exclusions, but the use case is unambiguous for an agent. The snapshot vs. per-day clarification also guides correct interpretation of results.

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