WordRadar

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

With no annotations, the description carries the full burden, and it does disclose the two upstream sources and the cost of 2 credits, which is valuable context. However, it says nothing about return shape, latency, error conditions, or whether results are cached, so behavioral coverage remains partial.

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 a single compact clause with the purpose front-loaded and the cost appended, with no wasted words. But the brevity borders on under-specification given the tool's composite nature.

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 composite data tool with no annotations and no output schema, the description omits key details: input format for 'domain', what the 'ownership change conclusion' output contains, and how segmentation/gaps are represented. It is too sparse to call the tool 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 description coverage is 0% for the single required parameter, and the description does not clarify what form 'domain' should take (apex domain, URL, IDN, subdomain handling). With one parameter and no schema documentation, the description should compensate but does not.

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 (domain history) and the two data sources combined: RDAP registration events and Wayback segmentation/gap/ownership-change conclusions. The verb is implicit ('retrieve history'), but the composite nature distinguishes it from generic lookup tools. Slightly terse but clear.

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 guidance on when to use this versus a plain RDAP/Wayback lookup or any sibling tool, and no prerequisites or exclusions. The agent must infer the use case entirely from the name and resource description.

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?

With no annotations, the description carries the full burden of behavioral disclosure, and it does disclose that this is a cost-free read of the caller's own balance. However, it does not address whether authentication is required, how the balance is represented, or what the response looks like.

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?

This one short sentence contains no wasted words, and it leads with the essential information: what is fetched and whose balance it is. The parenthetical about cost is a compact addition.

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 no-parameter read that returns a single value and has no output schema, the description covers what an agent needs in order to decide to call it. The remaining gaps, namely the balance representation and the authentication model, are minor.

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 has zero parameters, so there is no parameter semantics to explain; a baseline of 4 applies. The description correctly implies that no input is 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 and resource: checking the caller's own credits balance. This is unambiguous and clearly distinct from every sibling tool (domain_timeline, ledger_pulse, stripe_leaderboard, etc.), none of which report account balance.

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 '(免费)' note implies that calling the tool is free, which encourages its use, but the description gives no explicit condition for using it and no alternative tool to use instead. Usage is implied rather than guided.

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?

No annotations exist, so the description carries the full burden. It discloses that the value reflects the latest ledger period and that the endpoint is free, which is useful context beyond structured fields, but it says nothing about freshness/window semantics, caching, or failure behavior for what is presumably a read-only metric.

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?

A single compact sentence with no waste, and the metric identity is front-loaded before the free-endpoint note. Slight terseness works against it, as the phrase reads more like a label than an 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?

With no output schema and no parameters, the description must convey the return value, and it does ('number of new words'). The remaining gap is the absent sibling routing and any note on what 'latest ledger period' means in time, which would help an agent decide between this and ledger_words.

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 and the schema is empty, so there is no parameter semantics to describe; the baseline of 4 applies. The description correctly implies a no-argument snapshot call.

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 metric (the count of first-appearing new words in the latest ledger period) and marks it as a free endpoint, so an agent knows exactly what value comes back. It doesn't explicitly differentiate from the sibling ledger_words, so the agent must infer the split (pulse = count, words = list) from the name alone.

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 only usage signal is '(免费端点)' — free endpoint — which hints at cost but not at when to pick this over ledger_words or trends_rising. There is no stated condition, exclusion, or prerequisite for calling it.

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?

With no annotations, the description carries the full behavioral burden: it does disclose a concrete billing trait ('1 credit' per call), which is genuinely useful cost context. However, it says nothing about result size/pagination (limit default 30), whether results are ranked, or what a ledger entry contains, so the behavioral picture remains partial for a tool that presumably returns metrics.

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 a single compact line with the resource and filter axes front-loaded, followed by the credit cost. Repeating the enum values is somewhat redundant against the schema, but it aids scanning and wastes little space.

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 annotations and no output schema for a 4-parameter filtered query, the description should signal what a ledger row returns (word metrics? credit ledger?) and whether results are ordered. It also omits any sibling differentiation, so an agent has to guess both the return shape and when this tool is the right choice.

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 only 25% (only the `site` parameter is documented), and the description partially compensates by listing the enum values for type and direction. But those values are already in the schema enums, so the added value is small, and the `limit` parameter has no explanation in either place (max? does it truncate?).

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 clear verb (词账本查询 / query the word ledger) and enumerates the filter dimensions (type, direction, site matching), which matches the four schema parameters. It does not differentiate from siblings such as ledger_pulse or trends_rising, so an agent cannot tell from the description alone why it would pick this ledger query over the pulse/rising feeds.

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 explains how to filter but never says when to use this tool versus ledger_pulse, trends_rising, or site_radar_latest. The only usage-like signal is the price line ('1 credit'), which is cost context rather than when-to-use guidance; no prerequisites or exclusions are given.

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?

With no annotations and no output schema, the description carries the full behavioral burden. It discloses the cost (1 credit) and the score range (0-100+), which is useful, but says nothing about the return structure, accuracy, read-only nature, side effects, 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?

The description is extremely compact and front-loads the core purpose, score range, input shape, and cost. Every fragment carries information, though the terseness borders on under-specification for an agent unfamiliar with 'proxy-kd'.

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 two parameters, 0% schema description coverage, no annotations, and no output schema, the description is not complete enough. It omits the meaning of the 'keyword' parameter, the response structure beyond a score range, and any limitations or error conditions.

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 supply parameter meaning. It helps by specifying the input shape for the required 'results' array as [{url,domain?}], indicating url is required per item and domain is optional. However, the optional 'keyword' parameter is completely unexplained in both schema and description.

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 and resource: scoring the composition of the SERP top ten as a proxy keyword-difficulty metric on a 0-100+ scale. The verb and subject are identifiable despite the terse Chinese phrasing and the 'proxy-kd' jargon. Sibling tools are unrelated enough that explicit differentiation is unnecessary.

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?

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The phrase 'SERP 前十构成打分' implies a keyword-difficulty scoring context, but the agent must infer that entirely.

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?

No annotations are provided, so the description carries the full burden, and it discloses only that the call costs 1 credit — genuinely useful context. It says nothing about return format, pagination, what happens with insufficient credits, or whether results are cached/live.

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?

A single front-loaded line with purpose first and the enum clarification and cost trailing; nothing is wasted. It is terse to the point of being cryptic for non-Chinese-reading agents, but structurally sound.

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 3-parameter tool with no annotations, no output schema and 0% schema coverage, the description does too little: two of three params are unexplained and return behavior is unknown. Only the credit cost and hosting enum are covered.

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 coverage, the description compensates for one of three parameters by explaining the hosting enum meanings (free = hosted subdomain, custom = own domain). It leaves limit and min_points entirely undocumented in both schema and description, so the gain is only partial.

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 and action: discovering new sites on HN, with the 'latest' name implying recency. It is clear enough on its own, though it doesn't explicitly say it returns the most recent new sites or how they're ranked.

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 indication of when to use this versus alternatives (e.g., domain_timeline or trends_rising) and no prerequisites. The parenthetical describes enum values, which is parameter help, not usage 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?

With no annotations, the description carries the full behavioral burden. It does add one genuinely useful operational fact — the '1 credit' cost — but says nothing about authentication, data freshness, what the leaderboard contains, or what a call returns.

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 fragments, front-loaded with the resource name and the cost signal, with no wasted text. It is efficient, though its brevity edges toward under-specification rather than ideal density.

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 tool with no annotations and no output schema, the description is too thin: the return value (columns, sort order, ranking basis), the month format, and the update cadence are all left unexplained. An agent can guess but cannot call this 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?

There is one parameter (month) with 0% schema description coverage; the pattern ^\d{6}$ implies a YYYYMM format but is undocumented. The description never mentions the month parameter at all, so it does not compensate for the coverage 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?

The description names a specific resource, the 'Stripe official referral monthly leaderboard', which is distinguishable from every sibling (timeline, usage, pulse, words, score, radar, trends). However it is a noun phrase with no verb and the parenthetical 'signal of new players who have completed the closed loop' is dense jargon that doesn't sharpen what the tool actually returns.

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 parenthetical hints at what the data represents (closed-loop players), but there is no explicit guidance on when to call this versus the many other analytics siblings, no prerequisites, and no exclusions. The agent must guess at the use case.

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?

With no annotations, the description carries the full burden, and it does disclose two real traits: results come from a collection cache (so may be stale rather than live), and each call costs 2 credits. It does not say what is returned, how many items, or how failures/cache misses behave.

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 clauses front-load the resource, then the priority rule and the credit cost — no waste. It is terse to the point of being cryptic, but nothing should be cut.

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 tool it covers purpose, data source, and cost, which is a reasonable minimum given there is no output schema to lean on. Still missing: region/language scope, expected result volume, and any indication of what the response looks like.

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 is a single keyword parameter with 0% schema description coverage, but the wording implies the keyword is the seed term from which rising queries are derived, which is useful framing. It adds no constraints (language, region, result count) beyond that implied meaning.

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 resource (Google Trends rising/breakout queries) and states its sourcing strategy (reads the collection cache, Breakout terms prioritized). Siblings like ledger_pulse and site_radar_latest are unrelated, so no differentiation is needed. The retrieval verb is only implied, but the resource is unmistakable.

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 no explicit when-to-use or when-not-to-use conditions, and names no alternative. The only usable signal is '读采集缓存' (reads the crawl cache), which hints at non-live data, but the agent must infer this rather than being told.

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