China Policy, Licensing & Country Information

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

Beyond the annotations, the description discloses meaningful behavior: paginated structured output with source_class, provenance, snapshot, pagination and limits; no inference/aggregation; snapshot-limited coverage; empty data array on no match; and argument errors for missing/invalid input. This is strong behavioral disclosure, especially with no output schema present.

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 compact, front-loaded with the primary purpose, and every remaining sentence adds operational value: return shape, no-inference behavior, coverage limits, empty-result behavior, and error conditions. There is no filler or unnecessary elaboration.

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 tool's moderate complexity and no output schema, the description covers the essential operational details: required inputs, optional filtering, paginated results, result field categories, empty results, error handling, and snapshot scoping. An agent has enough information to call the tool correctly and interpret the response shape.

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 schema already explains every parameter, including exact-match and substring-match semantics. The description adds only a high-level mention of 'country' and 'institution' inputs and that country is required, which is redundant with the schema. Therefore baseline 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 states a specific action ('Find'), a concrete resource ('recorded institutions and roles for a country'), and an optional filter ('institution text'). It also clarifies the tool's scope as a read-only query over recorded facts, which distinguishes it from aggregation or inference tools among the siblings.

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 description gives clear context: use it to retrieve recorded country-institution facts with optional institution filtering, and it notes that no new inference or aggregation is performed. It does not explicitly name alternatives or state when not to use it, but the usage context is unambiguous enough for an agent to select it appropriately.

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, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral details: it performs no inference or aggregation, coverage is limited to the available snapshot, no match returns an empty array, and invalid input causes an argument error. These go beyond the annotations and inform the agent of expected behavior, including error handling. No contradiction.

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 concise, front-loads the purpose, and uses several short sentences to cover purpose, returns, behavior, and errors. Every sentence adds information without fluff.

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 query tool with fully documented parameters and annotations, the description is complete. It covers purpose, return structure (paginated records with source_class, provenance, snapshot, pagination, limits), behavioral guarantees (no inference, snapshot-limited, empty array on no match), and error handling. No critical information is missing for an agent to call it 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 covers all 4 parameters with detailed descriptions (product, limit, offset, snapshot). The description only restates that product is required and mentions error on invalid input, which adds minimal semantic value beyond the schema. Since schema coverage is 100%, the baseline is 3, and the description does not significantly enhance parameter understanding.

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), resource (licence catalog entries), and method (by recorded product or commodity text). Distinguishes from siblings like find_ccc_products by specifying the domain (licence catalog) and the search mechanism. Clear and specific.

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 context that it searches recorded text and returns structured records, but does not explicitly state when to use this over sibling find tools like find_ccc_products or find_country_institutions. No exclusions or alternatives are mentioned, so an agent must infer usage from the domain name. The description does mention limitations (snapshot coverage) but no comparative 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?

Beyond the read-only and idempotent annotations, the description adds valuable behavioral details: it performs no inference or aggregation, returns only recorded facts, is limited to the available snapshot, returns an empty data array on no match, and raises argument errors on invalid inputs. This goes well beyond what annotations alone convey.

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?

Every sentence earns its place: purpose, inputs, return shape, behavioral guarantees, error behavior, and coverage limits are all stated without fluff. The primary action is front-loaded before secondary details.

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 query with five well-documented parameters and no output schema, the description covers what is returned, pagination-relevant fields, empty-result behavior, error behavior, and snapshot limitations. An agent has enough information to call this tool correctly without needing additional context.

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%, and each parameter already has detailed descriptions covering exact matching, bounds, defaults, and snapshot semantics. The description only names issue and document_number, adding no new meaning beyond the schema, so baseline 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 opens with a clear verb and resource: 'Find entries in a gazette issue', and adds an optional filter by document number. This clearly differentiates it from sibling tools, none of which target gazette items.

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 description gives a clear use context: querying a gazette issue by label, optionally filtered by document number. It does not explicitly name alternatives or when-not-to-use conditions, but the resource-specific phrasing makes the intended usage unambiguous.

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, and non-destructive behavior, so the safety profile is covered. The description adds useful context by stating that the tool takes no arguments and returns only the standard document, not records or facts, but it does not describe the document's structure or any pagination/format details.

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 with no wasted words. The core purpose is front-loaded, and the clarifying exclusions ('not business records or an assessment of facts') are valuable for agent decision-making.

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, idempotent tool, the description is complete enough. It tells an agent what the tool returns, what it does not return, and that no arguments are needed. The lack of an output schema is offset by the clear high-level description of the document.

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?

There are zero parameters, and the description explicitly confirms 'Takes no arguments,' so there is no ambiguity about invocation. Baseline for zero-parameter tools is 4, and the description removes any doubt about whether arguments are needed.

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 ('Retrieve') and a clear resource ('definitions of information formation classes, provenance fields and snapshot/version metadata'), and further clarifies that it returns the information-standard document rather than business records or factual assessments. It is clear on its own, though it does not explicitly differentiate itself from likely-related siblings such as get_standard_metadata.

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 description implies its use case: call this tool when you need the standard definitions/metadata document, and it explicitly says it does not return business records or factual assessments. However, it does not name alternatives or explain when to prefer this over sibling tools like get_standard_metadata or search_standard_metadata.

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, and destructiveHint=false, and the description adds meaningful behavioral detail beyond them: no new inference or aggregation, snapshot-limited coverage, empty data array on no match, and argument errors on invalid input. This gives the agent a clear picture of what will and will not happen.

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 appropriately sized and front-loads the core purpose in the first sentence. The behavioral notes and return format are dense but useful. There is minor redundancy ('Inputs: query' and 'Required: query') but it does not significantly detract.

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 there is no output schema, the description compensates well by naming the return structure (source_class, provenance, snapshot, pagination, limits) and edge behaviors (empty array, argument error). Combined with fully documented parameters, an agent has enough context to invoke and interpret results 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 description coverage is 100%, so the schema fully documents all four parameters, including query matching semantics, pagination limits, and snapshot behavior. The description only restates that query is required and mentions 'Inputs: query', adding no substantive parameter meaning 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 clearly states a specific verb ('Find') and resource ('policy and document facts'), with a precise lookup method ('by title or document-number text'). It distinguishes this from analytical or inference tools by emphasizing 'Recorded facts only', but it does not explicitly contrast itself with sibling search tools like search_recalls or search_standard_metadata.

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 description gives clear context for when this tool is appropriate: only for recorded policy/document facts, not for inference or aggregation. It also sets expectations about snapshot coverage. However, it does not name alternative tools or state explicit exclusion conditions, leaving some routing to inference.

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