BenchLeader

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

Annotations already declare the tool as a safe, read-only, idempotent, closed-world operation. The description adds useful context by enumerating what information is covered, including method version, data date, cadence, coverage, licence, and citation, which goes beyond the annotation safety profile.

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 with no filler. Every item in the list corresponds to a meaningful piece of metadata the tool provides.

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 the low complexity, zero parameters, and rich annotations, the description is nearly complete for an about tool. It explains the informational content returned, though it does not describe output format or structure; because no output schema exists, that is a minor remaining gap rather than a major omission.

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 are no parameter semantics to explain. The baseline score for a parameterless tool is 4, and the description appropriately does not attempt to document inputs.

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 identifies the tool as an informational resource about BenchLeader, listing specific content such as method version, data date, coverage counts, licence, and citation. It does not explicitly contrast itself with sibling tools like get_benchmark or get_leaderboard, but the metadata focus is evident.

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 content list—this is the tool for background and citation information—but there is no explicit statement of when to use it versus siblings or when not to use it. For a zero-parameter about tool the intent is inferable, yet the description provides no direct routing guidance.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds real behavioral context: the statistical rule that a lead is only claimed outside the combined margin of error, plus a note that it links the comparison page. It does not mention fallback behavior for ambiguous model names, but the statistical-claim caveat is genuinely useful and non-obvious.

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 the output enumeration. The list of verdict components is dense but each item maps to a distinct returned section; slightly run-on, though nothing is wasted.

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 explaining returns and does so thoroughly (verdict, ratios, category wins, recommendations, link). Only gap is what happens on ambiguous or unknown model identifiers, which matters for a free-text identifier parameter.

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% with all four parameters documented, including accepted identifier forms and effort defaults, so the baseline is 3. The description adds no parameter-level detail beyond what the schema already supplies.

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 ("head-to-head comparison of two models") and enumerates the verdict dimensions it returns: quality lead, price and speed ratios, category wins, recommendations. This is clearly distinguishable from siblings like get_model, search_models, and get_leaderboard, which retrieve single entities or lists rather than a pairwise verdict.

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 purpose implies when to use it (you have two specific models and want a verdict), but there is no explicit when-to-use/when-not guidance or named alternative such as get_model or get_leaderboard for single-model lookups. Usage must be inferred from the described output 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, idempotentHint=true, openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: the payload embeds top models as one row per model ordered best-first, which tells the agent the call fans out beyond a flat record.

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 sentence, front-loaded with the object ('One benchmark') and then the field inventory. Dense but every clause names a real return field; the long comma chain is slightly harder to scan than a short bulleted shape would be.

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 describing returns and does so field by field, including the embedded model rows and their ordering. Minor gaps remain: nothing about behavior for an unknown benchmark identifier or how caveats/status are presented.

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%, so both the identifier resolution rules and the limit (1-25, default 10) are already documented in the schema. The description's 'top models ... best result first' reinforces what limit controls, but adds no format or edge-case detail beyond that. 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 specifies the exact resource (a single benchmark) and enumerates the fields it resolves: measured quantity, unit, publisher/source, Index weighting, status/caveats, and top models. That distinguishes it from list_benchmarks and get_leaderboard in scope, though 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?

The 'One benchmark' framing implies this is the per-record lookup used after list_benchmarks has surfaced an id, but the description never states when to prefer it over get_leaderboard or compare_models, and gives no exclusions or prerequisites.

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, so the bar is lower; the description earns credit for disclosing the less obvious semantics that a row is one model at its best reasoning-effort configuration, that configurations=true changes the grain of results, and that results are paged. It stops short of describing row contents or ordering/tie-break behavior, which matters given there is no output 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?

Front-loads what the leaderboard is before listing optional modes, and every clause maps to an actual parameter or behavior. It is a single dense, run-on sentence with an ellipsis in the category list, which costs a little readability but wastes no 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 six-parameter read-only list tool with no output schema, the description covers the essential grain, filtering, and paging behavior (paging detail lives in the schema's offset field). The notable remaining gap is what fields each returned row contains, which an agent must learn by calling.

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%, so the schema already documents all six parameters including category and developer examples. The description adds only marginal semantic value beyond that: it clarifies the default grain is best reasoning-effort per model and that configurations flips it, but contributes no syntax or format details the schema lacks. Baseline 3 is correct.

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 resource and verb: a leaderboard of LLMs built from a daily composite of public benchmarks, one row per model. It also disqualifies the confusion with siblings by making clear this is the ranking view (not get_benchmark, get_model, or compare_models), and the modes it supports are enumerated.

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?

Describes the usage modes clearly: default overall rank, rank by a category score, expand to every model × effort configuration, or restrict to open weights / one developer. It never names a sibling as the alternative (e.g. search_models to find a model first, compare_models for head-to-head), so routing guidance is implicit rather than explicit.

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, idempotentHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds value beyond that by disclosing the payload's shape — including that it returns the model's other reasoning-effort configurations and benchmark results with publisher and source links — which is meaningful for a read tool with no output 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?

A single front-loaded sentence that leads with the resource and then enumerates the returned data. The enumeration is long but each item corresponds to real payload content, so little is wasted.

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 explaining return values and does so comprehensively. The only remaining gap is error/empty behavior for an unrecognized model identifier.

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 three parameters are already documented, including the effort default ('best-ranked one') and the results_limit cap. The description reinforces the effort concept by mentioning 'its other reasoning-effort configurations' and 'strongest independent benchmark results', but adds no syntax or format detail beyond the schema.

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 precisely ('one model's BenchLeader profile') and enumerates the fields returned, so an agent knows this is a single-model detail lookup rather than a list or comparison. It stops short of naming siblings like search_models or compare_models, so the boundary with them is only implied.

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 singular 'one model' scope, but there is no explicit when-to-use guidance, no statement of when to prefer compare_models or search_models, and no mention of what happens when a model id is unknown or ambiguous.

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, closed-world, so the safety profile is covered. The description adds genuinely useful context beyond them: the exact per-row content (weight, model coverage, status, caveats) and that results are paged.

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 what the tool returns, followed by two short operational sentences. Minor redundancy between 'Filter by category or a search query' and the schema descriptions, but no wasted bulk.

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 well by enumerating the fields each benchmark record contains. Combined with full parameter documentation, an agent has enough to call it correctly; only upstream routing versus singular get_benchmark is unstated.

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%, so all five parameters are already documented, including query examples and offset paging. The description only restates category/query filtering and paging, adding no syntax or semantics beyond the schema — baseline 3.

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 ('The benchmarks BenchLeader reads') and enumerates the fields each row carries, so an agent knows exactly what comes back. It is clearly the plural/list counterpart to get_benchmark, but it never names that sibling to make the distinction 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?

'Filter by category or a search query. Paged.' implies how to narrow results, but gives no explicit when-to-use guidance, no exclusion against get_benchmark for a single benchmark, and no note on when to page versus filter.

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 useful behavioral detail by disclosing what each match contains (id, rank, Index, reasoning-effort configurations), which tells the agent the call is cheap and informative. It does not mention the 25-result cap or ranking order, which are minor omissions given the schema documents limit.

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 filler, with the matching criteria and the return payload front-loaded before the downstream-tool pointer. Every clause carries 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?

There is no output schema, and the description compensates by enumerating the returned fields and their purpose, so the agent knows what it will get back. Combined with a fully documented two-parameter schema and complete annotations, nothing needed to call this 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 100%, so the baseline is 3; the description earns a bump by giving concrete query examples ('opus', 'gemini 4', 'deepseek') that clarify loose fuzzy matching across names, aliases, ids and developers. It adds nothing for limit, which the schema already explains.

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 ('Find models') plus the exact match keys (name, alias, id, developer) with concrete examples. It also names the downstream siblings (get_model, compare_models), so an agent can distinguish it from a single-model lookup without opening the schema.

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 'for use with get_model and compare_models' implies the workflow (search first, then fetch/compare), which is clear context. It stops short of an explicit rule such as 'use this when you only know a partial name; use get_model when you have the exact id,' so no true when-not guidance.

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