Japanese Order Eval

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so the bar is low. The description adds genuinely non-structured context: the response deliberately omits the expected answer, and input should be treated as untrusted document data. It stops short of describing return shape or failure 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?

Two compact sentences with the retrieval behavior front-loaded and no filler. The second sentence about untrusted input is slightly ambiguous as stated, but the overall size is appropriate.

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 single-parameter read tool with full annotation coverage and no output schema, the definition is close to adequate, but it leaves the required id undocumented and gives no sense of what a returned 'case' contains beyond the reference date.

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% and the description never mentions the single required 'id' parameter, its format, or where to obtain valid ids. With one undocumented parameter, the description does not compensate for the schema gap.

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 ('Get one synthetic case and its reference date') and adds a meaningful scope qualifier ('without its expected answer') that distinguishes it from the scoring siblings. The singular 'one case' implicitly separates it from list_order_cases, 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?

No when-to-use guidance is given. The agent is not told when to prefer this over get_order_eval_contract, list_order_cases, or the scoring tools, nor what the case is intended to be used for. Usage must be inferred entirely from the name and the 'without its expected answer' clause.

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 declare readOnlyHint, idempotentHint, openWorldHint=false, destructiveHint=false, covering safety. The description adds that this is a prerequisite read step before generating predictions, which is useful sequencing context, but doesn't disclose more (e.g., format, caching, limits).

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 short sentences, front-loaded with what it returns and followed by when to use it. No waste, though could be slightly more structured.

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-param read tool with annotations and no output schema, the description covers the main content and the sequencing. It's adequate; no return value details needed since no output schema, but the description could enumerate the fields more clearly.

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?

Zero parameters, so baseline is 4 per rules. The description doesn't need to cover parameters, and schema coverage is moot.

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 being retrieved ('extraction rules, output schema, suite scope and commercial availability') and the verb 'get'. It distinguishes itself from siblings like score_order_batch or list_order_cases, though it doesn't explicitly name alternatives.

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 clear timing guidance: 'Read before generating predictions,' which tells the agent when to call it. No explicit exclusions or named alternatives, but the sequencing instruction is concrete.

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, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds a genuine content-level caveat beyond that structured data: the result is limited to six cases and deliberately omits expected answers, which prevents the agent from assuming a scoring-ready payload. Return format and per-case fields remain unspecified.

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 short sentences with no filler; the scope (six synthetic Japanese cases) is front-loaded and the caveat about missing answers follows immediately. 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?

For a zero-parameter list tool with no output schema, the description supplies the essentials: what is returned, how many, and what is withheld. It could go further by noting whether case identifiers are included for use with get_order_case, but nothing critical 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. The schema is empty and closed (additionalProperties=false), consistent with the description's no-argument list operation.

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?

Names a specific verb (List) and resource (order-evaluation cases) and quantifies the scope (six synthetic Japanese cases), which delimits it from the singular get_order_case sibling. It stops short of explicitly stating it is the enumeration counterpart to get_order_case/score_order_batch, so sibling differentiation 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 only implied: the agent can infer this is the discovery step before fetching a specific case or scoring, but no when-to-use, prerequisite, or named alternative is given. The note that expected answers are absent hints another tool supplies them, yet the sibling get_order_eval_contract is never mentioned.

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 read-only, idempotent, non-destructive, closed-world safety. The description adds useful behavioral context: missing cases fail, machine-readable gate status/clearances are returned, and no orders are executed.

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?

Three short, front-loaded sentences with no filler. Every sentence carries purpose, failure behavior, return content, or execution boundary.

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?

Covers purpose, failure mode, output summary, and non-execution, which is decent given rich annotations. However, with no output schema and no parameter descriptions, it leaves the predictions shape and the exact form of gate status/clearances underspecified.

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%, so the description should explain the predictions parameter. It only restates the max of six from the schema and implies missing cases fail, without explaining the required id/output object structure.

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 (Score) and resource (predictions), plus batch limit and failure mode. It distinguishes from order execution, but does not explicitly differentiate from the sibling score_order_output.

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?

No when-to-use or when-not-to-use guidance is given, and no alternative sibling tool is named. Batch scope is implied by 'up to six predictions' but not framed as a usage condition.

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 closed-world, so safety is covered. The description adds one genuinely useful behavioral fact — 'Does not execute orders' — clarifying this is a pure comparison, not an order action. It says nothing about what is compared or returned, so it adds only modest value beyond the 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?

Three terse sentences: purpose, timing, and a boundary disclaimer, all front-loaded with no filler. Every sentence contributes, though the framing is clipped enough that some needed detail (what 'id' is) was omitted in favor of brevity.

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?

The tool is low-complexity (2 params, no output schema, no nested objects), and annotations cover the safety profile. However, with 0% parameter documentation and no output schema, the description should have clarified the meaning of 'id' and what the comparison yields; that omission keeps it only minimally 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?

Schema description coverage is 0%, so the description carries the full burden for two undocumented parameters. 'One prediction ... against a fixed answer' loosely implies the free-form 'output' is the prediction, but the required 'id' parameter is never explained (is it a case id? an eval id?). With zero schema help, this leaves a real gap.

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: 'Compare one prediction against a fixed answer,' which reads as a scoring/evaluation operation. The word 'one' implicitly distinguishes it from the sibling score_order_batch, and 'Does not execute orders' rules out confusion with the get_order_case family. It stops short of naming siblings explicitly, so it is clear but not maximally differentiated.

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 gives the timing condition: 'Run after generating your prediction.' That is actionable context an agent can use. It does not name when to choose this over score_order_batch, but the 'one prediction' phrasing provides an implicit single-vs-batch signal.

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