Park & Keep airport parking

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered. The description adds the underlying data source ("Park & Keep airport parking index") and confines scope to European airports, which is useful provenance, but says nothing about data freshness, coverage gaps, or how results are returned.

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?

Front-loaded with the usage trigger, then metrics, then example phrasings — a sensible order with little wasted text. The metric enumeration and three-language phrase list are somewhat long for a single-parameter tool, but each element serves intent matching.

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 carries the burden of telling the agent what comes back, and it does list the returned metrics qualitatively. It stops short of describing the response shape, units for distance, or handling of an unknown airport, which keeps it from being fully 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?

Only one parameter, and schema description coverage is 100% — the schema already documents that it accepts an IATA code or an airport/city name with examples ("BRU", "Charleroi", "Málaga"). The description adds no disambiguation guidance for ambiguous city names or format normalization, 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 a specific verb+resource (parking market snapshot for a European airport) and enumerates the exact metrics returned: competitor count, shuttle/valet/covered share, median and shortest distance, and count with published prices. That metric set implicitly separates it from airport_parking_options and airport_price_snapshot. It loses a point only because it never names the sibling tools to make the boundary explicit.

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?

It gives an explicit trigger ("Use this when a car park operator or analyst asks about the parking market around a European airport") plus three concrete example phrasings, including a non-English one, which makes intent matching easy. There is no when-not guidance and no routing to alternative tools for adjacent questions.

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, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: the returned attribute set (shuttle, valet, keys kept, EV charging, distance, date checked), an explicit negative disclosure about prices, and the underlying data source.

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?

Front-loads the purpose, then phrasings, then the price exclusion and data source. The middle attribute list is long and slightly list-like, but every item describes an actual returned field rather than padding, so it mostly earns its space.

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?

There is no output schema, so the description carries the burden of describing returns and does so by enumerating the fields. With annotations covering safety and the schema covering the single input, an agent has everything needed to select and call this tool correctly.

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 coverage is 100% for the single 'airport' parameter, which already documents that IATA codes and city names are accepted. The description adds only a loose geographic constraint ('European airport'), which is marginal added meaning over the schema, so the baseline of 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 verb and resource (which car parks serve a European airport and what they offer) and enumerates the offered attributes, so the agent knows exactly what this tool answers. It also distinguishes itself from airport_price_snapshot by scope, not just by name.

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?

Gives an explicit trigger ('when the user wants to know which car parks serve...'), three concrete example phrasings including a non-English one, and an explicit exclusion routing price queries to airport_price_snapshot. Nothing is left to inference.

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 readOnly/idempotent/non-destructive, and the description adds substantial behavioral context on top: the return shape (lowest, median, highest per stay length), the gating rule (only returned when at least 3 prices were read), inclusion of the dates read, the exclusion of per-operator prices, and the data source (Park & Keep index). That is well beyond what the annotations provide.

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?

It is front-loaded with the usage trigger, then example phrasings, then the return contract, and every sentence carries information. The list of example phrasings is slightly verbose but functional rather than 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?

There is no output schema, so the description carries the return-value burden and does so fully: it explains the three statistics, the per-stay-length granularity (3/7/14 days), the minimum-data gating, the returned dates, and what is deliberately excluded. Nothing needed to call or interpret the tool is missing.

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?

With a single parameter and 100% schema description coverage, the schema already documents that 'airport' accepts an IATA code or a city/airport name. The description implies a European-airport scope but adds no format or matching details beyond the schema, so the 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?

The description states a specific verb and resource (return the lowest/median/highest public parking price per stay length for a European airport) and adds a scope constraint ('Never a price per operator'). It does not explicitly name or route against its siblings airport_market_snapshot and airport_parking_options, so it stops short of full sibling differentiation.

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?

It gives a clear trigger ('Use this when the user asks how much parking costs... or whether a parking price is normal there') plus concrete example phrasings in multiple languages. It lacks explicit when-not-to-use guidance or pointers to the sibling tools, which is the only gap.

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