RoxSkin HYROX

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description usefully specifies that the profile is public and content-bearing rather than a mutation, but says nothing about behavior for an unknown/invalid handle or whether the response is trimmed for private data.

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 that names the resource first and then enumerates its contents. No filler, no repetition of the title, and nothing that fails to earn its place.

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 compensates by enumerating the returned fields (location, division, best time, race results, upcoming races, sponsor link), which is exactly what an agent needs to decide relevance. Only minor gaps remain, such as behavior on an unknown handle.

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% and the single 'handle' parameter is fully documented there ('without @'), so the baseline of 3 applies. The description adds no syntax, format, or lookup-semantics detail beyond what the schema provides.

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+resource ('One athlete's public profile') and enumerates the fields returned, so the agent knows exactly what it retrieves. The singular 'One athlete's' implicitly separates it from the search_athletes sibling, but no sibling is named explicitly.

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?

Usage is only implied: you need a handle and want the full profile of a single athlete, versus search_athletes for discovery. No explicit when-to-use, when-not-to-use, or alternative is stated, so the agent must infer the routing itself.

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 establish this is a read-only, closed-world lookup, so the bar is lower; the description still adds conditional behavior ('when available' for the city guide) and characterizes the athlete payload as those 'who want sponsors'. It stops short of describing not-found/error behavior.

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 that leads with the core purpose before nesting details. Dense parenthetical enumeration of guide contents is justified given no output schema, though it reads as a long run-on.

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 compensates by enumerating the return payload (facts, guide sub-sections, athletes), and the 'when available' caveat flags non-uniform responses. Missing only edge-case behavior for invalid or unknown slugs.

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 at 100% schema coverage, so the baseline is 3; the description earns extra by giving the parameter's provenance ('from search_races'), telling the agent the slug is a value produced by another tool rather than an arbitrary string.

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 (get) and resource (race by slug) and enumerates exactly what comes back: facts, race-weekend guide, and athletes seeking sponsors. This distinguishes it clearly from siblings like get_training_guide or get_athlete.

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?

Explicitly tells the agent where the slug comes from ('from search_races'), which routes from the sibling search tool. It does not state when not to use it or what happens for an unknown slug, so it falls short of a full when/when-not treatment.

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 openWorldHint=false, so the safety profile is covered. The description adds the return format (markdown), which is useful, but says nothing about caching, size, or authentication expectations.

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 first sentence is front-loaded and effective, but the bulk of the description is a 45-item slug list that duplicates the schema's enum exactly. This redundancy bloats the definition without adding information the agent doesn't already receive from the schema.

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 read-only content retrieval with no output schema, the description covers purpose, output format, and valid inputs adequately. Safety is delegated to annotations, which is appropriate; the only minor gap is absent guidance on guide selection.

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 0%, but the parameter is a 45-value enum whose slugs are self-descriptive, so the schema largely documents itself. The description only repeats the same enum verbatim, adding no meaning beyond the structured data. Baseline 3 fits given the enum carries the semantics.

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 ('Read one of the guides') plus the output format ('as markdown'), which distinguishes it from sibling retrievers like get_athlete and get_race. It is clear, though it doesn't explicitly contrast itself with the closest sibling how_sponsorship_works.

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 enumerated slugs imply the usable contexts, but there is no explicit when-to-use/when-not guidance or reference to an alternative tool. Usage is only inferable from the content list.

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 openWorldHint=false, so the agent knows this is a safe, non-mutating lookup and does not need the description to establish that. The description adds content-scope context (the 15 spots, default prices, refund rules, FAQ) but says nothing about return format (text vs JSON) or length, so it is useful but not rich.

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 compact sentence that front-loads the subject and enumerates the covered material without padding. Slightly awkward phrasing ('How RoxSkin works' plus a list) but every clause earns its place.

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 zero-parameter, read-only documentation tool with no output schema and full annotation coverage, the description is sufficient to call it correctly. Only the return format is unstated, which is a minor gap for a knowledge-fetch 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?

The tool takes zero parameters, which is the baseline-4 case: there is nothing for the description to disambiguate. No parameter documentation is missing.

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 concrete informational resource (sponsorship mechanics for RoxSkin, sponsor spots with default prices, refund rules, FAQ), so an agent knows exactly what content it returns. It does not, however, contrast itself with the other knowledge-style sibling (hyrox_format), leaving the boundary between the two implicit.

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?

Usage is implied: it is a zero-parameter read of sponsorship documentation, and the topic words make the triggering question obvious. There is no explicit statement of when to use this versus hyrox_format or the get_* tools, so the guidance stays at the implied level.

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 openWorldHint=false, so the safe, self-contained read profile is covered. The description adds useful provenance ('from the official rulebook'), which signals authoritative, non-user-specific content, but it says nothing about whether results are static or versioned. No contradiction with annotations.

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 that states the subject matter and the source, with no filler or redundancy. Nothing could be removed without losing information.

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 no-parameter, no-output-schema reference tool, the description adequately scopes what the agent will receive (format plus per-division station loads) and where it comes from. It would be slightly stronger if it indicated the result is a fixed reference set rather than live race data.

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 takes zero parameters, so there is no parameter semantics to document; baseline is 4. The description's mention of 'by division' correctly signals that all divisions are covered in a single call rather than selected via input.

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 content delivered: the HYROX race format (8 x 1 km runs + 8 stations) and station loads by division. That is specific enough to separate it from get_race, get_training_guide, and search_races, though it never explicitly contrasts itself with those siblings or states a verb for how the content is retrieved.

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?

There is no guidance on when to call this versus get_race or get_training_guide, and no stated trigger condition. The agent must infer that this is a static rules-reference lookup rather than a data query, with nothing in the text confirming that 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 readOnlyHint=true and openWorldHint=false, so the safe-read profile is covered. The description adds no further behavioral detail such as pagination, result limits, or ordering. With annotations carrying the safety burden, this is adequate but thin.

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 with zero filler; the resource and its optional filters are packed into one efficient clause.

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 filter-list tool with no output schema and annotation-covered safety, the description supplies what an agent needs: the entity returned and the available filters. Minor gaps remain around result ordering and pagination, but nothing essential to calling it correctly 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?

Schema coverage is 0%, so the description must carry the parameter load, and it does: it names all three filters and enumerates the valid division values (open, pro, doubles, pro_doubles, relay, adaptive), which is meaningfully more than the bare schema. It does not specify the country format (e.g. ISO code) or race slug shape, keeping it from a 5.

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 ('List HYROX athletes looking for sponsors'), which is clearly distinct from sibling search_races and the get_athlete lookup. It does not explicitly name an alternative tool, so it falls short of a 5.

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 word 'optionally' implies all filters are optional and the tool works as a broad discovery list, but there is no explicit when-to-use or when-to-prefer-get_athlete guidance. Usage is implied by the sponsor-seeking framing rather than stated.

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 openWorldHint=false, so the safety profile is covered. The description adds the return contents (dates, venue, divisions, ticket status, page links), which is useful context, but says nothing about pagination, result ordering, or default limit behavior beyond the schema.

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 sentences, zero waste, with the core purpose and return shape front-loaded. Nothing extraneous.

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 enumerates the return fields, and the schema fully documents all inputs. It is nearly complete for a search tool; only pagination/ordering behavior is left unaddressed.

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 all five parameters (including the from/to semantics and the free-text query examples) are already documented in the schema. The description adds no format or syntax detail beyond what is in 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 (Find) and resource (upcoming HYROX races) plus the filter dimensions (city/name text, country, date range). This clearly separates it from siblings like get_race (single race) and search_athletes.

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?

Usage is implied by the name and filters – search when looking for races by location or date. However, there is no explicit guidance on when to use this vs get_race for a specific known race, nor any stated prerequisites or limitations.

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