China Visa & Regulation Data

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

Annotations only cover read-only and closed-world semantics; the description adds substantially more: per-call cost (1 prepaid credit), the exact 402 payment_required failure mode with buy links, a 'not legal advice' caveat, and a dataset freshness date. This is the kind of operational context annotations cannot express.

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?

Dense single paragraph that front-loads the trigger, then inputs, then outputs, then cost — a sensible priority order. Slightly overpacked, with the input example duplicating the schema and the source-link inventory bordering on excess, but every clause carries real 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 naming the return fields (visa_free_for_ordinary_passports, max_days) and the route taxonomy with conditions, validity windows and sources. Combined with the cost and failure-mode disclosure, an agent has everything needed to call and interpret 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 parameter is already documented there, so the description's 'English country name, e.g. Germany' adds no new syntax or format detail. Per the rubric, a fully covered single-param schema defaults to 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+resource ('whether a passport holder can enter mainland China without a visa') and even scopes the answer to duration. The 'mainland China' qualifier cleanly separates it from any sibling that might cover other regions, and the output summary makes the tool's contract unambiguous.

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?

Opens with an explicit trigger ('Use when someone asks whether...'), which is exactly the when-to-use guidance an agent needs. It also routes the agent to get_pricing for the credit-purchase path. It stops short of stating when NOT to use it (e.g., Hong Kong/Macau/Taiwan, or non-ordinary passports), which is the only reason it isn't a 5.

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 readOnlyHint/openWorldHint annotations, the description adds genuinely useful behavioral facts: the tool is free, requires no API key, and is rate-limited to 50 free-tool calls per IP per day in total. Auth and rate-limit disclosures are exactly the kind of context annotations cannot express.

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?

It is front-loaded (trigger first), then inputs, outputs, and cost in tight sequence. Slightly dense — the free-limit detail appears both in the trigger clause and the trailing cost sentence — but every line carries actionable 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 parameterless, read-only tool with no output schema, the description fully compensates by describing the shape of the returned JSON. Nothing an agent needs to invoke or interpret this tool 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?

There are zero input parameters, so the baseline is 4. The description correctly states 'Inputs: none,' which matches the empty schema and prevents the agent from guessing at required args.

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 specific verb and resource ('get pricing' / cost and how to buy) and enumerates exactly what is returned: free-tool limit, credit packs, Stripe Payment Links, USDC-on-Base steps, and per-tool cost. No sibling (eligibility, regulation changes, capability list) overlaps with pricing, so the agent can distinguish it instantly.

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 explicit trigger conditions: 'Use when the user asks about cost, hits the free limit, or needs more calls.' That is strong when-to-use guidance, though it names no exclusions or alternative tools — which is acceptable since no sibling covers pricing.

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 only declare readOnlyHint=true and openWorldHint=false; the description goes well beyond that by disclosing the cost model (1 prepaid credit per call), the required 'Authorization: Bearer <key>' header, and the exact failure mode (402 payment_required with buy links). That is exactly the behavioural context an agent needs before spending credits.

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?

One dense sentence block that is front-loaded with usage, then Inputs, Output, and Cost in a predictable order. Every clause earns its place, though the cost/payment detail could be a separate sentence for readability.

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 fills that gap by enumerating the returned fields (date, source, title, summary, tags, link). Combined with the auth/cost disclosure and full parameter semantics, an agent has everything needed to call this 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 67% (limit is undocumented in-schema) and the description compensates by giving limit's range and default ('1-20, default 5') plus concrete topic examples ('trade', 'export control') and the since format (YYYY-MM-DD). It adds real meaning over the schema, though it does not explain how topics are matched (substring vs. tag equality).

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 ('brief a foreign business on recent official Chinese regulations and policy releases') and narrows the sources to State Council English releases and MOFCOM. The sibling set (visa eligibility, pricing, capabilities) is clearly unrelated, so the agent can route here without ambiguity.

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?

Opens with an explicit use case ('Use to brief a foreign business on...') which gives clear context for invocation, and points to get_pricing for the credit/payment path. It stops short of naming when NOT to use it or which alternative to prefer for non-China or non-official sources.

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 only cover readOnlyHint and openWorldHint. The description adds genuinely useful behavior the annotations can't: it is free, requires no API key, and enforces a rate limit of 50 free-tool calls per IP per day. That rate-limit and auth disclosure is exactly the kind of context the rubric credits; it stops short of richer detail (e.g., what happens when the limit is hit).

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 imperative use-first guidance, then Inputs, Output, and Cost in a compact labeled sequence. Every sentence carries information and nothing is redundant.

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, so the description carries that burden and does so adequately, spelling out the JSON fields returned (tool, cost, data coverage, dataset dates, auth method) plus cost/rate-limit terms. An agent has everything needed to decide to call it and interpret the result.

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 the baseline is 4. The description reinforces this with "Inputs: none," leaving no ambiguity that the tool takes no arguments.

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 and enumerates exactly what the tool returns: each tool, its cost, data coverage, dataset dates and auth method. That scope is clearly distinct from the sibling action tools (check_visa_free_eligibility, get_pricing, latest_regulation_changes, sample_eligibility).

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?

"Use first to see what this server covers and which tools are free" gives explicit ordering guidance and a clear reason to call this before exploring siblings. It does not name any when-not condition or contrast against a specific alternative, so it falls short of the top band.

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 cover read-only/open-world, and the description adds genuinely new operational context: free of charge, no API key required, and a concrete rate limit of 50 free-tool calls per IP per day. It also describes the returned sample's contents, which the absent output schema cannot 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?

Three tight sentences, front-loaded with the purpose and then the routing decision, followed by cost/limit facts. Every clause carries information an agent needs; there is no 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?

For a one-parameter, annotation-covered, output-schema-less tool, the description covers purpose, alternative routing, cost, auth, rate limit, and the shape of the returned sample. Nothing required to call or interpret it 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 description coverage is 100% for the single parameter, so the schema already documents the English country name and the 'Germany' default. The description repeats that information without adding format, validation, or fallback behavior beyond it, 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 and resource ('preview the shape and quality of eligibility data') and explicitly scopes itself as a sample rather than the full dataset. It names the sibling check_visa_free_eligibility as the full-data alternative, so an agent can distinguish the two without opening either 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?

Gives an explicit when-to-use ('before paying', to preview shape and quality) and a when-to-use-the-other ('For full routes, conditions, dates and all sources use check_visa_free_eligibility'). Nothing about tool selection is left to inference.

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