OpenChainBench

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds genuine return-behavior context: providers the bench does not measure arrive in `missing`, and below-floor providers return rank null and must not be reported as losses. That is non-obvious and useful beyond the structured fields.

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 core purpose, then usage, then edge-case handling in short paragraphs. The examples and missing/rank-null notes each earn their place; the description is a touch long but stays focused.

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 explaining the shape of results (rank, value, success rate) and the two ambiguous edge cases (missing providers, null rank). An agent can call and interpret this correctly without further information.

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 every parameter (including tier, chain, region, benchmark, providers) is fully documented in the schema itself. The description adds the recommendation to source the benchmark slug from search_benchmarks but no syntax beyond what the schema provides, so 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?

States a precise verb and resource ('puts two or more named providers side by side on one benchmark') and enumerates the returned fields (rank, measured value, success rate). It is clearly separable from siblings like recommend_provider, which the 'user names the candidates themselves' framing implicitly contrasts against.

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 concrete when-to-use conditions with user-phrasing examples ('Alchemy or QuickNode?') and tells the agent to call search_benchmarks first for the slug. It stops short of explicitly naming recommend_provider as the alternative for unspecified candidates.

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, openWorld, and non-destructive behavior. The description adds rich context beyond that: the exact output shape, error response for unknown slugs, cohort behavior for RPC benchmarks, and an explicit warning never to compare public with private rows. 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?

Well-structured with bullets and examples, and the key output detail is front-loaded. It is somewhat long, but the length is largely justified by the absence of an output schema and the need to explain cohort/parameter interactions.

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 no output schema and four parameters, the description is complete: it describes return values, error handling, parameter scoping, cohort behavior, and citation guidance. An agent has everything needed to call the 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%, so baseline is 3. The description goes beyond the schema by explaining how optional chain/region scope sub-slices, how tier interacts with cohort output, and what the defaults are. This added interpretation of parameter interactions earns a 4.

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: 'Returns full detail for one benchmark' and enumerates the returned content (rankings, sparkline, quote, methodology). This clearly distinguishes it from list/search siblings by emphasizing single-record detail, but it never names a sibling tool for contrast, 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?

Provides three concrete user-question-to-call examples and explains scoping via chain/region and tier selection. It gives clear context for when to use optional parameters but does not state when to prefer a sibling tool (e.g., list_benchmarks to find slugs) or when not to use this tool.

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, openWorld), so the bar is lower. The description still adds real behavior: drafts are filtered out, and an answer whose benchmark has no defensible leader says so in `answer` rather than naming a winner. It also discloses the per-row shape. It stops short of noting pagination/volume, which keeps it off a 5.

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 core purpose, then usage guidance, then the return shape. The row-shape block earns its space because there is no output schema. Slightly long, but no sentence is 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?

With no output schema, the description carries the return-value burden and does so: it enumerates the row fields and explains citation intent (`url` vs `benchmarkUrl`). Combined with the explicit when-to-use and follow-up routing, an agent has everything needed to select and invoke it.

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 optional `benchmark` slug filter is fully documented in the schema, so the baseline is 3. The description references the `benchmark` field only as a returned value, not as an input filter, so it adds no semantics 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?

States a precise verb and resource ('Returns every published answer page') and enumerates the payload semantics: a plain question, the answering sentence from live data, and the source benchmark. This is clearly differentiated from sibling get_benchmark, which is described as the 'full ranking behind it.'

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 says when to call it ('when the user asks a question in words rather than by benchmark name') and supplies two concrete example questions. It also names the follow-up tool and the field to pass ('call get_benchmark with the returned benchmark slug'), removing all routing ambiguity.

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 safety is covered. The description adds genuinely useful behavior beyond that: the catalogue exceeds 200 entries so the listing is capped and filterable, not exhaustive, and it names the valid categories. It could have noted the default/cap explicitly, hence 4.

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 in one sentence, then usage, then constraints and categories. Slightly long with the trailing category list duplicating the schema, but every section is functional and no sentence is 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?

No output schema exists, and the description compensates by describing the returned row fields. Combined with usage routing, capping behavior, and category list, an agent has everything needed to call and interpret this 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?

Schema description coverage is 100% (limit range and category enum documented in the schema), so the baseline is 3. The description reinforces that filtering is by category but adds no syntax or semantics 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?

States a specific verb+resource ('compact index of what OpenChainBench measures') and precisely enumerates the row shape (slug, title, category, metric, value, leader). It is clearly distinguishable from search_benchmarks and get_benchmark, which are named separately.

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 explicit triggers ('what does OpenChainBench cover?') and an explicit alternative with rationale: search_benchmarks for provider/chain questions because it ranks against their words and 'costs far less to read.' It also routes to get_benchmark for drilling in.

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?

Beyond the readOnly/idempotent/openWorld annotations, it discloses the return semantics: measurements plus caveats, not an endorsement, that a leader is only a leader of one benchmark over its stated window, and that use-case caveats come back in a `guidance` field. With no output schema, this return-shape context is exactly what the description needs to supply.

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 core purpose is front-loaded in the first sentence and the parapraphs are logically ordered (what it does, what to pass, what comes back). It is slightly longer than necessary, and the closing "pass it on, it is usually more useful than the ranking itself" is mild editorial padding rather than agent-relevant instruction.

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 read-only, three-parameter query tool with no output schema, the description covers purpose, input semantics, return content, and the caveat/`guidance` contract, which is everything an agent needs to call it correctly. No behavioral gap remains that the structured fields don't already fill.

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 the baseline is 3, and the description goes further by specifying that `use_case` should be free-form user language rather than normalized terms, reinforced with examples. It does not add format or interaction detail for `chain` and `region` 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 states a specific verb and resource (recommend a provider) and frames it as answering "which should I use for X" by selecting the benchmarks that measure X. It distinguishes itself as goal-driven and measurement-based rather than a raw comparison, though it never names compare_providers or get_benchmark 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?

It gives clear invocation guidance — "Pass the user's goal in their own words" — with four concrete example phrasings, which tells the agent what kind of input belongs here. It does not state when to prefer compare_providers or get_benchmark instead, so there is no explicit exclusion 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 safety (readOnly, idempotent, non-destructive, open world), so the description rightly spends its budget on ranking semantics: pass the user's phrasing through, partial-name matching, compact rows, and the meaning of an empty result. It stops short of describing scoring/freshness or pagination beyond the limit param, so it is strong but not exhaustive.

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 purpose, then trigger condition, then a compact example block, then return/next-step note. Every sentence earns its place and the examples are the cheapest possible way to convey query construction.

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?

No output schema exists, yet the description tells the agent what comes back (compact rows), what to do next (get_benchmark), and how to handle the null case. 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 baseline is 3. The description goes beyond it by explaining that query should be verbatim user phrasing where provider names, chain names and goals all match, which is genuine guidance on how to construct the argument rather than restating 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?

States a specific verb and resource: 'Ranks the benchmark catalogue against a question in the user's own words and returns the best few matches.' This clearly separates it from siblings like list_benchmarks (enumerate) and get_benchmark (fetch one full ranking).

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?

Explicit when-to-use trigger ('when somebody asks which provider, chain or service is fastest, cheapest, most reliable or best'), plus three worked examples mapping natural questions to calls, plus the routing instruction to open a result with get_benchmark and the empty-result fallback behavior.

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