Lanmao Free AI Credits

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered structurally. The description adds that ALL fields are returned (useful relative to summary-style list responses), but omits any note on how locale changes the payload.

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 the operation and scope. The bilingual pairing duplicates the same content, but it is brief enough that no sentence is wasteful.

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 only gestures at the return value ('all fields'). For a simple idempotent read that is borderline adequate, but the locale parameter's effect on the response remains unexplained.

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?

With 0% schema description coverage, the description must carry parameter meaning. It explains 'id' only superficially ('取一条...by id') and says nothing at all about 'locale', whose enum values control which language the returned fields are localized into — a materially important, undisclosed parameter.

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+scope: fetch one free AI credit offer by id, returning all fields. That distinguishes it implicitly from list_offers and search_offers (plural/multi-record tools), though it never names them.

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 rather than stated: 'by id' tells the agent it needs a known identifier, which contrasts with the search/list siblings, but there is no explicit when-to-use or when-not-to-use 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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds the 'free' filter and web-page parity, but says nothing about ordering, pagination, or result shape.

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 compact, front-loaded sentence per language with no filler. The bilingual duplication is mildly redundant but justified for a locale-aware tool.

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 one-optional-param read tool with no output schema and full annotation coverage, the description is broadly sufficient for selection. It falls short on locale behavior, which matters given the tool is locale-scoped.

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 single locale parameter has 0% schema description coverage, and the description never mentions locale or what the enum values do. The /zh/clients/ reference weakly implies a zh default, which is not enough to compensate for an entirely undocumented parameter.

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 (list) and resource (entries usable as free AI coding clients), plus a scope anchor ('same set as /zh/clients/'). That clearly separates it from the offers/recent_changes siblings, though it doesn't name a sibling directly.

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: an agent can infer 'call this to get the client list,' but there is no explicit when-to-use, when-not, or alternative tool named. The /zh/clients/ parity note hints at scope but not at routing.

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 only that the offers are 'free AI credit' listings; it says nothing about pagination or result size despite a `limit` parameter. With annotations carrying the heavy load, a 3 is appropriate.

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 text is short and front-loaded, but it is a bilingual duplicate that says exactly the same thing in Chinese and English, which is wasted space for a single-language agent. It is not bloated, but it is not maximally efficient either.

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 5 parameters, 0% schema description coverage, and no output schema, the description should explain at least the pagination/size behavior of `limit` and the purpose of `locale`. Neither appears, leaving real gaps for an agent that wants to page through or localize results.

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 must compensate, and it names three of five parameters (goal, region, model_tag) while omitting `limit` and `locale` entirely. The enum values live in the schema, so the description adds only marginal value over the structured fields.

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 (list/filter) and resource (free AI credit offers) plus the filter dimensions, so the agent can tell it is a filtered listing tool. It does not distinguish itself from siblings like search_offers or get_offer, which is the only thing missing from 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?

The description names filter dimensions but gives no when-to-use guidance, no exclusions, and never mentions the sibling tools search_offers or get_offer. An agent cannot tell from this text when to browse with list_offers versus searching with search_offers.

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, and closed-world behavior, so the safety profile is covered. The description adds the ordering and recency scope, but says nothing about how far back it reaches, default result size, or pagination — meaningful gaps for a list-style read.

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?

Very short and front-loaded, with the recency/ordering constraint stated immediately. The bilingual duplication is defensible for a locale-aware tool, though it repeats the same content rather than adding detail.

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 parameterized read tool with no output schema and 0% parameter coverage, the description leaves too much unstated: what a 'change record' contains, default limit, locale behavior, and time coverage. An agent can call it, but cannot predict the result 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?

Two parameters (limit, locale) with 0% schema description coverage, so the schema supplies no semantics and the description must compensate — but it never mentions either parameter. It does not clarify that limit caps results or how locale affects returned content.

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 clear verb+resource: retrieve recent data change records, with an ordering qualifier (newest first). It is understandable on its own, though it does not differentiate from sibling tools — which is acceptable here since the siblings (offers, coding clients) are unrelated domains.

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?

Only 'recent' and 'newest first' hint at when to use it. There is no guidance on what window counts as 'recent', no prerequisites, and no mention of alternatives or related tools for reaching older change history.

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, so the safety profile is covered. The description adds genuinely useful search semantics (case-insensitive, substring rather than prefix/exact), but says nothing about result caps, ordering, or how the locale parameter affects matching.

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 the operation and the searched fields. The zh/en duplication restates identical content, which is modest waste, but nothing else is padded.

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 read-only search with annotation coverage and no output schema, this is close to sufficient, but it omits the default limit, how locale influences results, and what the results contain, all of which an agent would need to call it confidently.

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 0% and only 3 parameters exist, so the description should carry more weight. It does explain the 'query' parameter well (case-insensitive substring over four named fields), but 'limit' and 'locale' receive no mention at all, leaving a third of the surface undocumented in both places.

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 (search/substring search) and the exact fields searched (name, vendor, models, quota text), which is more than a restatement of the name. It does not explicitly differentiate itself from list_offers or get_offer, but the fields searched make the intent concrete.

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 the usage context (free-text substring matching rather than enumeration), so an agent can infer this beats list_offers when it has a partial string. However, it never explicitly names an alternative or states when not to use this tool, leaving routing to inference.

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