Voyscout Price History

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

With readOnlyHint=true already declared, the description adds valuable behavioral context: percentages are over observed days rather than the calendar span, and the tool is descriptive only and does not recommend whether to buy. This goes beyond the annotation without contradicting it.

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, purposeful sentences. The first front-loads the core statistics and output scope, and the second clarifies the percentage basis and adds a non-advice caveat. No unnecessary words.

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 one-parameter read-only tool with no output schema, the description fully describes what is returned and how to interpret it. The destination slug is already defined in the schema, so nothing essential is missing for correct invocation.

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 schema already documents destination as a required slug with 100% coverage. The description only says 'one destination' and adds no additional semantic details about the parameter, so it remains at the schema-coverage baseline.

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 names the resource (one destination) and the exact statistics returned (min, median, share of tracked days dearer than the last measurement), and explicitly says the sample points are omitted, which distinguishes it from a price-history tool. It lacks an explicit verb, but the intended operation is unambiguous.

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 phrase 'without the sample points' implies that price_history is the better tool when sample points are needed, and 'Descriptive only' warns against using this tool for buy decisions. However, it never explicitly names sibling tools or gives concrete selection criteria beyond these implications.

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

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

Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context: the output is deliberately historical, limited to destinations with published price series, and includes two additional data elements (number of measured days and calendar span). No contradiction exists, and the description goes beyond the annotation by explaining what data the reader will actually see.

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 a single, front-loaded sentence that states the core action and object first, then packs the qualifying details into a compact clause. A second sentence reinforces the historical scope without redundancy. Every word earns its place, and there is no extraneous filler.

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 parameter-less list tool without an output schema, the description does a good job of explaining what will be returned: destinations with published price series, measured-day counts, and calendar spans. It is arguably complete for invocation purposes, though it does not mention ordering, pagination, or clarify the domain-specific meaning of 'rail,' which an agent might need to interpret the response.

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 tool has zero parameters, so the baseline is 4 per the rubric. The description does not need to explain parameter behavior because there are no inputs to configure; its focus on the output fields is appropriate and sufficient.

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 names a specific verb ('list') and resource ('every destination with a published price series'), then scopes the content with the measured-days and calendar-span details. The 'Historical observations only' qualifier sets it apart from the sibling tools, which focus on history comparison and prices rather than destination-level measurement coverage.

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 provides clear usage context: it returns historical measurement coverage for all destinations with a published price series. It does not explicitly name when to avoid this tool or mention alternatives like current_vs_history or price_history, but the historical-only restriction gives the agent enough situational understanding to select it correctly.

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

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

The read-only annotation is reinforced and supplemented by the description: it is historical observations only, never a live quote or recommendation. The mention of a downsampled sample and regime breaks gives useful behavioral context beyond the annotation.

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, front-loaded sentence states the core purpose before providing clarifying constraints. Every clause adds value, with no filler or repetition of schema details.

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?

Although there is no output schema, the description names the key output categories (span, stats, downsampled sample, regime breaks) and the main exclusions (live quote, recommendation). It could go further on empty-history edge cases or formatting, but it provides enough for an agent to call the tool confidently.

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 already provides 100% description coverage for both parameters, including the destination slug format and rail enum. The tool description adds little parameter-specific detail beyond confirming the destination scope, so the baseline of 3 is appropriate.

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 names the exact resource (price series for one destination) and enumerates the returned components: span, stats, downsampled sample, and regime breaks. It also differentiates from siblings by explicitly stating it is historical only and never a live quote or recommendation.

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 clearly establishes when to use the tool: when historical price data for a destination is needed. It also provides a when-not signal by ruling out live quotes and recommendations, though it does not explicitly name sibling tools like current_vs_history as alternatives.

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