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

With no annotations provided, the description carries the burden of behavioral disclosure. It transparently reveals a key behavior: '`period` is accepted for backwards compatibility and ignored' – that is a non-obvious quirk that could surprise users. It also clarifies that the data is a snapshot, not historical. This goes beyond basic read/write safety to disclose important operational constraints.

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 compact: a one-sentence summary followed by a two-sentence caveat. Every sentence contributes value: the first defines what it does, the second explains the fundamental limitation, and the third resolves the misleading 'period' parameter. No filler or redundancy.

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 that an output schema exists (context signal), the description need not explain return values. It covers the core semantics (snapshot vs. time series), the ignored parameter, and the breakdown dimensions. It could briefly mention default behavior when no filters are provided, but the optional parameters make this inferable. Overall, the tool is simple enough that this description is nearly 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?

The input schema has no descriptions for any parameters (0% schema coverage), so the description must compensate. It explains that the snapshot can be broken down 'by region, category, rating tier and price segment', giving meaning to region and category as filter/grouping dimensions, and explicitly clarifies the period parameter is ignored. This adds significant meaning beyond the raw schema, although it does not detail expected value formats.

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 opens with 'Current market snapshot by region, category, rating tier and price segment', which clearly identifies the tool as providing a current distribution. It then explicitly contrasts with a time series: 'not a time series', which distinguishes it from what the name 'trend_analyzer' implies and from sibling tools like market_analytics that might provide trends. This is a specific verb (provides) and resource (market snapshot) with clear scope.

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 states clearly when to use this tool: to get a current market snapshot. It also gives an explicit limitation: 'we do not store historical snapshots, so no growth or change-over-time can be computed', telling the agent when NOT to use it for trends. However, it does not name an alternative tool for time-series analysis, so it lacks a direct 'use X instead' recommendation.

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