ThirdMade Shelf

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

With no annotations, the description must carry behavioral disclosure. It does add useful context: this is an 'Eval only' operation and explicitly states it does not solve captchas, plus a price note. However, it does not disclose the output shape, how the optional 'type' parameter affects the call, or any relevant failure/edge-case behavior, so transparency is incomplete.

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 two compact sentences, front-loaded with the core purpose and followed by a clear non-solving and price note. Every sentence earns its place and there is no redundant or fluff content.

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 has no output schema and no annotations, so the description is the only behavioral contract. It never explains what a successful call returns, how to actually supply a captcha for eval, or what the optional 'type' parameter does in the invocation flow. This leaves significant ambiguity for an agent trying 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?

The schema covers the single 'type' parameter with a description listing allowed values, so schema coverage is 100%. The tool description adds the concept of difficulty but does not clarify whether 'type' is an input to classify, a filter, or an output hint. Thus the description provides only marginal 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 states a specific verb and resource: 'Classifies captcha type (reCAPTCHA, hCaptcha, Turnstile) and difficulty.' It also adds a clear boundary with 'Eval only — we do NOT solve captchas,' distinguishing it from any solving-related alternatives. No sibling tool has overlap, so purpose ambiguity is low.

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 when to use the tool — when captcha type/difficulty classification is needed — and explicitly says not to use it for solving. However, it does not name alternative tools or give explicit 'when to use vs not use' guidance beyond the non-solving exclusion. This is adequate but not strong.

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 must carry behavioral information on its own. It usefully discloses the per-call price and the general data returned, implying a read-only query, but it does not specify what 'chain stats' includes, the response format, or any rate 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?

The definition is extremely short, front-loaded with the core output, and uses a compact pricing bracket. Every element earns its place with no redundancy.

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 read tool this is nearly adequate, but without annotations or an output schema the agent is left without a precise definition of 'chain stats' or the return payload. It also lacks usage context and the full set of supported chains.

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 documents the single chain parameter and its default value. The description adds no new parameter semantics and even omits the schema-listed 'arbitrum' option.

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?

Description states a clear, specific resource: it returns block height and chain stats for Base or Ethereum, expanding on the name chain_stats. It does not explicitly differentiate from sibling tools, and it omits the schema-supported 'arbitrum' chain, which slightly muddies scope.

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 is given on when to use this tool versus alternatives like gas_now or other chain-related tools. Usage context is only implied by the tool's output; no conditions, exclusions, or sibling comparisons are provided.

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. It transparently discloses the tool's behavior: it performs a live check and reports three specific facts (up status, TLS presence, title). It also adds the pricing detail. It does not specify mechanics like timeouts or error handling, but the core behavioral traits are clearly conveyed.

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 front-loaded sentence that states what the tool does, what it evaluates, and the intended audience, followed by a separate pricing note. There is no redundancy or filler; every part earns its place.

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, the description explains the key return data points ('is it up, does it have TLS, what's the title?'), which is the core output an agent needs. It also includes cost and use-context. It doesn't describe return formatting or failure modes, but for a one-parameter, low-complexity tool this is essentially 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?

The input schema has 100% description coverage for the single 'domain' parameter, so the description need not add much. It mentions 'a domain' in the text, but adds no format, example, or edge-case guidance beyond what the schema already provides. 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 clearly states a specific action ('Live-checks a domain') and the specific data points checked (up status, TLS, title). This is enough to distinguish it from sibling tools like similar_domains, page_meta, and scrape based on the unique health-check focus.

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 phrase 'Quick domain health check for agents evaluating links' gives clear context for when the tool should be used. It does not explicitly name alternatives or exclusions, but the intended use case is sufficiently scoped 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?

With no annotations, the description carries the behavioral burden. It adds useful traits: an external Ethereum RPC dependency and a metered cost ($0.001/call via x402). However, it does not state failure behavior, error handling, or return format.

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 purposeful sentence plus a compact pricing note conveys the full operation. It is front-loaded and has 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 lookup the definition is close to complete, but with no output schema it should state what a successful or failed resolution returns. The missing return/error detail prevents full completeness.

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 schema describes name as 'ENS name or address', and the description adds meaning by mapping that dual input to forward resolution versus reverse lookup. Since schema coverage is already 100%, this is extra value 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 states a clear action and resource: ENS name resolution plus reverse lookup, via public Ethereum RPC. This immediately separates it from sibling web/domain tools such as domain_facts and page_meta.

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 intended use is implied by the wording — resolve ENS names or reverse-resolve addresses — but the description gives no explicit when-to-use guidance, exclusions, or alternatives. This is sufficient in context but not proactive.

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 disclosure burden. It usefully discloses that Wikidata resolution is not yet available and includes pricing/paument context, but it does not describe the output format, language support, side effects, or the exact nature of extracted entities.

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 three short sentences, each contributing useful information: the core functionality, the current limitation/roadmap, and the cost. The purpose is front-loaded, and 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 tool, the description is largely sufficient and includes relevant context such as entity categories, no Wikidata resolution, and price. However, since there is no output schema, the main omission is an explicit statement of the return shape (e.g., a list of entity strings versus typed spans), which would make invocation expectations clearer.

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%, so the baseline is 3. The description's 'from text' adds minimal meaning beyond the schema's 'Input text'; it names the input but provides no additional constraints, format, or length guidance.

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 verb ('Extracts'), a direct object ('named entities'), enumerates the categories (people, companies, places), and the source ('from text'). This makes the tool's purpose unambiguous and distinguishes it from text siblings like sentiment, summarize, or pii_guard. The Wikidata roadmap also clarifies what the tool does not currently do.

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 use case—named-entity extraction from text—but it never explicitly states when to choose this tool over alternatives or when not to use it. No sibling comparison or exclusion criteria are provided.

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 provided, the description carries the behavioral disclosure burden. It does add useful context: data is fetched via public RPC, prices are in Gwei, and the tool is free. However, it does not mention failure modes, rate limits, or reliability issues that might come with public endpoints.

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, front-loaded sentence with no filler. The '[free]' and 'via public RPC' markers convey useful extra information without bloating the text.

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, read-only tool, the description covers most essentials: chains, unit, source, and cost. However, there is no output schema and no mention of the return shape, so an agent may not know exactly what to expect when parsing the response.

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 sole parameter 'chain' is fully documented in the schema with its allowed values and default. The description repeats the chain names and adds the unit 'Gwei', but it does not add meaningful parameter-level semantics beyond what the schema already provides.

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 the resource: current gas prices in Gwei for Base, Ethereum, or Arbitrum. It lacks an explicit verb like 'get' or 'fetch', and it does not contrast with sibling tools, but the purpose is unambiguous and specific enough.

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 no explicit guidance on when to use this tool versus alternatives. The '[free]' and 'public RPC' notes imply a lightweight option, but there is no when-to-use or when-not-to-use statement.

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?

There are no annotations, so the description carries the burden. It adds useful pricing and payment details via x402, and implies a read-only lookup, but it does not mention response format, rate limits, errors, or operational behavior beyond cost.

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 sentence delivers the core capability and pricing with no filler. Every word earns its place.

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 lookup with no output schema, the description covers input, output domains, and cost. It omits the exact return structure, but the tool is simple enough that an agent can invoke it and inspect the response.

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 schema already documents q as 'Location string' with 100% coverage, so the description adds little beyond reinforcing 'any location string'. It does not clarify accepted formats such as address, city, coordinates, or place name.

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 identifies what the tool does: geocode, timezone, and currency context for a location string. It is readily distinguishable from the sibling tools, though it uses a noun-phrase rather than an explicit verb and does not name an alternative.

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 phrase 'for any location string' provides a clear usage context: when an agent has a location string and needs geographic/time/currency context. It does not mention exclusions or alternatives, but the tool's niche is unique among the siblings.

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 behavioral burden. It explains the tool performs entailment scoring and includes a clear price/payment detail ($0.001/call USDC via x402). It does not specify the exact output format, but 'scoring' implies a result that evaluates claim support.

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 tightly-worded sentence with no redundancy. It front-loads the core behavior, follows with the intended use case, and appends the cost detail compactly.

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 has no output schema, and the description does not state the shape or range of the correspondence score. 'Entailment scoring' gives some hint, but an agent may not know whether the result is a boolean, a probability, or a score on a specific scale.

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%, so the parameter descriptions already document `context` and `claim`. The tool description adds little beyond paraphrasing the schema ('provided context' and 'this claim'), so the 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 clearly states the tool's purpose: 'Entailment scoring: does the provided context support this claim?' This identifies a specific action and resource. It doesn't explicitly distinguish from sibling tools like search_verify, but its meaning is 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?

'Research agents use this to self-check outputs' gives clear context for when to use the tool. It does not mention exclusions or alternatives, but the intended use case is explicit enough for an agent to decide 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?

With no annotations provided, the description carries the full burden and does well by disclosing async behavior, return payload shape, GPU backing, scale-to-zero, and pricing. It does not mention failure modes, prompt rejection, or typical latency, but the most operationally relevant behaviors are transparent.

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-loading the core function and output format, then adding async behavior and cost. Every sentence contributes meaningful operational information with no filler. It is an appropriately sized and well-structured description.

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 single-parameter tool with no output schema and no annotations, the description covers the essential call semantics: what is generated, the async status flow, and cost. It does not explain failure behavior or how to turn the poll URL into a final image, but the provided information is sufficient for correct invocation and follow-up.

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%, with the prompt parameter described as 'Image prompt'. The description adds context like text prompt, PNG output, and 512px resolution, but it does not specify prompt length, style constraints, or format details. This is adequate but not enhanced beyond the schema baseline.

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 that the tool generates one PNG from a text prompt, specifying the model and resolution. It also mentions the async return of a job ID and poll URL, which distinguishes it from the sibling image_status tool. This is a specific verb+resource statement with no 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?

The description gives clear context that this is an async operation returning a job ID and poll URL, so an agent knows not to expect an immediate image. It does not explicitly name image_status as the polling alternative, but the poll URL strongly implies the follow-up step. No exclusions or alternatives are stated, but the usage pattern is clear.

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 provided, the description carries the full burden and does reveal key behavior: it polls by job ID, and the output is a base64 PNG only when the job is COMPLETED. It does not describe non-completed statuses or failure modes, but for a simple polling tool this is reasonable disclosure.

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 two short, front-loaded sentences with no wasted words. The core action and the key output condition are immediately clear, and '[free]' is the only minor extra 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 one-parameter tool with no output schema, this description gives sufficient information to invoke the tool correctly: what to pass, what action it performs, and the expected result when complete. The main gap is the lack of detail about responses before completion, but this is not critical for the invocation decision.

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 the single 'job' parameter already has the description 'Job ID from image_gen'. The description adds little beyond saying 'by job ID', so it does not meaningfully exceed what the schema already provides.

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 the specific verb 'Poll' and the resource 'image-gen job status', and clarifies the tool returns base64 PNG when completed. This clearly distinguishes it from the sibling image_gen tool, which generates jobs rather than checking their status.

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 clearly implies the tool is used after obtaining a job ID from image_gen, and the schema reinforces this with 'Job ID from image_gen'. It does not explicitly name alternatives or state when not to use it, but the intended context is clear enough for correct selection.

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 burden of disclosing behavior. It does convey the main behaviors, intended pipeline usage, and pricing, but it does not describe output format, error behavior, or whether the tool has side effects. The lack of an output schema makes this gap more noticeable.

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 two sentences, front-loads the core functionality, and ends with a compact pricing note. Every sentence adds value: the first defines behavior, the second gives usage context, and the pricing is directly relevant to invocation decisions.

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-parameter tool with no annotations and no output schema, the description covers purpose, usage context, and cost. It lacks detail on return shape and error handling, but the tool's complexity is low enough that the description remains largely sufficient for an agent to select and invoke 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?

The schema has 100% description coverage for the single parameter, so the baseline is 3. The description adds context about what happens to the input, such as PII detection and schema stats, but it does not add new parameter-level syntax or format details 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 uses specific verbs and resources: validates JSON, detects PII inside values, and reports schema stats. This clearly distinguishes the tool from siblings like pii_guard and jsonify by combining validation, PII detection, and schema reporting into one purpose.

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 states a clear use context: pipeline agents should use this as a data-quality gate. It does not explicitly list exclusions or alternatives, but the context is specific enough to guide selection among sibling tools.

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 present, so the description carries the full burden of behavioral disclosure. It reveals a transformation behavior and pricing, but does not disclose output format details, failure modes for unparseable input, or any side-effect/safety characteristics. Some burden was met, but significant gaps remain.

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 front-loaded sentence stating the core transformation and input examples, followed by a terse pricing note. There is no filler, repetition, or unnecessary detail; every element earns its place.

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-parameter tool, the description covers the purpose, input formats, and outcome ('clean JSON'), and mentions cost. It doesn't describe the exact JSON shape or error behavior, but given the tool's low complexity and the presence of the main informational need, it is largely 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?

The schema only describes the text parameter generically as 'Input text', while the description adds meaningful semantics by enumerating accepted formats (key=value lines, CSV-ish, sentences). Even with 100% schema coverage, this elaboration improves an agent's ability to format input correctly.

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 identifies a specific action ('Coerces') and resource ('messy text...into clean JSON'), and lists concrete input formats (key=value lines, CSV-ish, sentences). It doesn't explicitly differentiate from sibling json_guard, but the transformation-focused wording makes the purpose reasonably distinct.

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 tool is for converting messy text into JSON, but it provides no explicit when-to-use guidance, no exclusion cases, and no mention of alternatives such as json_guard for validation. An agent must infer the appropriate context from the examples alone.

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 burden of behavior disclosure. It explains the core behavior well—extract outbound links and classify by domain—but does not disclose edge cases such as whether same-domain links are included, redirect handling, pagination, or response format. This is adequate but not richly transparent.

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 two tight sentences that front-load the primary action and output, followed by a price note. Every sentence earns its place; there is no repetition of the tool name or schema contents.

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 extraction tool with no output schema, the description gives enough conceptual detail about the returned data—outbound links, domain classification, dominant domains—to let an agent understand what it will get. It does not detail exact return formatting, but that is not strictly necessary for a simple tool of this kind.

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 the only parameter, url, is already described in the schema as 'Target webpage URL.' The description adds no additional parameter meaning or format guidance, so the baseline score 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?

Description uses a specific verb ('Extracts') and names a concrete resource ('outbound links from a page') with a clear output structure ('classified by domain'). It clearly differentiates from sibling tools like scrape, page_meta, and similar_domains by focusing specifically on link-level domain analysis.

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 when to use this tool: when you need to understand where a page links and which domains dominate. It provides clear context but does not explicitly name alternatives or state when not to use it, so it misses the higher bar of explicit exclusion 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 burden of behavioral disclosure. It states 'fetches and normalizes' but does not explain what normalization entails, error behavior, fallback if the file is missing, or the output format. The 'free' note is useful but not enough to compensate for the lack of operational detail.

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 sentence that front-loads the action and resource, then adds a purposeful contextual note about the standard and a pricing marker. Every element earns its place with no redundant phrasing.

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-parameter tool, the description is mostly sufficient, but the meaning of 'normalizes' is vague and there is no mention of the return value or handling of absent llms.txt files. Given no annotations or output schema, a slightly richer description would improve completeness.

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 schema already describes the sole parameter as 'Target webpage URL' with 100% coverage, so the baseline is 3. The description adds no further parameter-specific semantics; the phrase 'from any site' is more about tool scope than the url parameter itself.

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 identifies the action ('Fetches and normalizes') and the exact resource ('llms.txt / llms-full.txt'), distinguishing it from generic fetching tools like 'scrape'. The phrase 'from any site' adds scope, making the purpose immediately 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?

The description provides context ('the emerging standard for making websites readable to AI agents') implying when to use it, but it does not explicitly mention alternatives or when not to use this tool. An agent is left to infer that this is for retrieving llms.txt files rather than general web content.

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 present, so the description carries the full burden. It implies a read-only lookup and discloses pricing, but it does not explicitly state that it has no side effects, whether authentication is required, rate limits, or what the exact response format is. For a simple getter this is minimally adequate but not rich.

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 two substantive sentences plus a price note, with no wasted words. The core purpose is front-loaded, followed by the usage trigger and pricing.

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 two-parameter lookup, the description covers what the tool returns, the supported chains, and when to call it. The main gap is the lack of an explicit return-value shape, especially given that no output schema is provided.

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 documents address and chain. The description adds 'Base or Ethereum' and the tx-bot use case, but does not add parameter-level detail 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 identifies the tool as returning the current transaction nonce for a given address on Base or Ethereum, which is a specific query. It doesn't explicitly differentiate from sibling tools, but none of the listed siblings competes for this exact function, so the core purpose is 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?

States when to use it: 'Agent tx bots need this before sending transactions.' This gives a clear, actionable trigger. It doesn't name alternatives or exclusions, but no sibling tool offers a nonce lookup, so this context is sufficient.

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 burden. It adds pricing info ($0.001/call) and lists exactly what fields are extracted, which is useful. However, it does not disclose limitations like failure on non-HTML pages, redirect handling, or that 'any web page' may be over-claiming.

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 plus a pricing note. The core function is front-loaded, followed by use cases, then price—every part serves a purpose with 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 single-parameter tool with no output schema, the description covers purpose, expected return fields, use cases, and cost. It misses edge-case behavior (e.g., pages that block bots or non-HTML responses), but the essential facts for calling it correctly are present.

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%: the sole parameter 'url' already has a clear description 'Target webpage URL'. The tool description adds no new semantic detail beyond mentioning 'web page', so it neither helps nor hurts beyond the baseline.

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 'Extracts' with a clear resource list: OG tags, canonical URL, title, and meta description. This readily distinguishes it from siblings like scrape or link_graph, which handle broader or different content extraction.

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 explicit use cases: link previews, SEO audits, and content enrichment. It does not name alternative tools or state when not to use it, but the context is clear enough for an agent to select it for meta-data extraction.

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 provided, the description carries the transparency burden and mostly meets it: it discloses that the operation is detection (not redaction), reports character spans, and surfaces the $0.001/call cost. It could add limitations like false-positive behavior or input size bounds, but it does not hide any obvious side effects.

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 definition is two tight clauses and a parenthetical price; every element adds decision-relevant information. The main capability is front-loaded before the compliance use case and pricing.

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 detector with no output schema, the description is nearly complete: it states the input domain, the detected PII categories, the character-span output property, a target user, and cost. The main missing piece is a fuller specification of the response format when no PII is found.

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%: the sole parameter 'text' is already described as 'Input text'. The description reinforces that the text is arbitrary ('any text') but does not add parameter-specific constraints such as length limits or encoding, so the baseline of 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 the specific verb 'Detects' and the resource 'PII', names concrete entity types (emails, phones, SSNs, credit cards), and adds 'character spans' to define the output style. This distinguishes it from sibling guards such as prompt_guard and json_guard without needing to open the 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?

'Compliance agents use this before processing user data' is a clear when-to-use statement tied to a concrete workflow, so an agent can select it appropriately. It does not explicitly name alternatives or exclusions, but the stated context is sufficient for a single-purpose detection tool.

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. It adds useful context such as pricing and the guard-oriented use case, but it does not disclose the return format, score interpretation, or whether high risk is indicated by a threshold. This is a meaningful gap for an agent deciding how to act on the output.

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 filler. The core purpose is front-loaded, the use case is stated, and the pricing note adds actionable operational information without bloating the description.

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 single-parameter, no-output-schema guard tool, the description covers what it does, when to use it, and the cost. The main missing piece is the nature of the returned score, but overall it is adequately complete for the tool's simplicity.

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% with a clear 'Input text' description, so the baseline is 3. The description adds slight extra meaning by framing the text as 'LLM inputs' in the context of prompt-injection risk, but it does not go beyond that.

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 uses a specific verb ('Scores') and resource ('prompt-injection / jailbreak risk for LLM inputs'), making the tool's function immediately clear. It also distinguishes itself from related guards like pii_guard and json_guard by focusing specifically on injection/jailbreak risk.

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 identifies the target use case: agent builders sanitizing user inputs before calling an LLM. It does not state when not to use it or name alternatives, but the context provided is sufficient for a simple one-parameter tool.

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 disclosure burden. It clearly indicates a read-only parse operation and enumerates the output categories, which is sufficient for a simple fetch-and-parse tool; error handling and network behavior are not discussed but are minor gaps.

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 front-load the core action and include output details and a practical use case. The '[free]' marker is minor but not distracting.

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 with no output schema, the description adequately covers what it does, what it returns, and when to use it. It could include more detail about input format edge cases, but the current description is complete enough for correct invocation.

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 schema already covers the single url parameter at 100% with 'Target webpage URL'. The description adds that the tool resolves a domain's robots.txt from the URL, but it does not clarify whether a bare domain or full page URL is expected, so the added value is marginal.

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?

Describes a specific action and resource: 'Parses robots.txt for any domain' and lists concrete outputs (crawl rules, disallowed paths, sitemaps). This makes it easy to distinguish from sibling tools like scrape or page_meta.

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 when-to-use guidance: agents should check robots.txt permission before scraping a site. It does not name alternatives or explicit when-not-to-use cases, so it stops short of a full routing guideline.

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 present, so the description carries the full burden. It discloses that pages must be public, that output is normalized JSON, and the cost per call. It omits important caveats such as JS rendering, rate limits, and failure modes, so an agent may over-trust the claim 'any public web page'.

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 functional sentences plus a cost note, with the core purpose front-loaded. No wasted words; the 'workhorse' line earns its place by conveying usage priority.

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 single-parameter tool with no output schema, the description provides the essential call context: public-only scope, output fields, and price. It could be more complete with an example or failure-mode note, but nothing critical is missing for basic invocation.

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 schema already documents the only parameter ('url') with 100% coverage. The discription adds no syntax or format detail about the URL, but none is urgently needed for a single self-evident parameter; the baseline 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?

The opening sentence names the specific verb ('scrapes'), the resource ('any public web page'), and the exact output shape ('title, text, length'), which distinguishes it from sibling tools like page_meta or link_graph. 'The workhorse' reinforces its general-purpose role.

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 phrase 'agents use this for real data extraction' gives clear usage context and implies it is the default tool for this job. It does not explicitly state when NOT to use it or name an alternative, but it provides more guidance than most tool descriptions.

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. It reveals the core behavior (URL support check), the output (confidence score), and a pricing detail, but it does not specify the score scale, whether the URL is fetched live, or how failures are handled. This is adequate but not rich.

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 three short sentences, with the core function first, the audience second, and the pricing note last. Every sentence adds useful information and 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 two-parameter tool, the description is mostly sufficient, but since there is no output schema, it should clarify what the confidence score looks like (range, format, or interpretation). The pricing note is a nice operational detail, but the missing output format keeps this from being fully 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 100%, so the input schema already explains both parameters adequately. The description adds little beyond 'verify sources,' which reinforces rather than extends the schema. Baseline 3 is warranted.

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 verb and resource: 'Checks if a URL supports a claim' and says it returns a confidence score. This clearly separates it from sibling tools like hallucination_check (which checks model outputs) and scrape (which only extracts content).

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 explicitly states the intended context: 'Research agents use this to verify sources.' It doesn't name alternatives or give exclusion criteria, but the use case is clear enough for an agent to select it appropriately among the listed siblings.

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 descriptive burden. It discloses the core behavioral outcome—sentiment classification into three labels plus emotion keyword hits—and adds the price/cost model. It doesn't detail output formatting or failure semantics, but for a simple, stateless, non-mutating classifier this is reasonable coverage.

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: one clause for what the tool does and one bracketed clause for cost. The main behavior is front-loaded, and every word adds value. There is no fluff or repetition of the tool name.

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 with no output schema, this description gives the essential return types (sentiment label, emotion keyword hits) and pricing, which is enough for an agent to decide to call it. The main gap is that the exact structure of the response and the semantics of 'emotion keyword hits' are left implicit, but the low complexity keeps this acceptable.

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 input schema already covers the single 'text' parameter with 100% description coverage ('Input text'), so the baseline is 3. The description adds no format, length, or precondition details for the parameter, only tying it to the sentiment-classification task. It neither harms nor improves on 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 opens with a concrete verb phrase ('Sentiment classification') and specifies the exact output space (positive/negative/neutral) plus emotion keyword hits, which clearly identifies what the tool does. It does not explicitly contrast with a sibling tool, so it stops just short of the strongest sibling-differentiation bar.

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 usage: use this tool when you need sentiment classification of text. However, it provides no explicit when-to-use vs when-not-to-use guidance, no alternatives, and no edge-case context. The function is self-evident enough for an agent to infer usage, but no direct guidance is offered.

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 behavioral disclosure burden. It discloses the tool's core behavior ('Finds similar/competitor domains by name-similarity') and adds pricing information, but does not mention output format, result limits, 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 compact and front-loaded, with two sentences plus a pricing note. Every sentence adds useful information: core purpose, target use case, and cost.

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 single-parameter lookup tool, the description covers purpose, method, use case, and cost. It does not specify the exact response shape, but the tool's simple nature and complete schema coverage make the description largely sufficient.

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 the only parameter is described as 'Domain name' in the schema. The description does not add extra parameter details, but the schema already provides sufficient semantic clarity, 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 uses a specific verb ('Finds') and a specific resource ('similar/competitor domains') with a clear method ('by name-similarity'). It clearly distinguishes this tool from domain_facts and other sibling tools by focusing on lead-gen/prospecting.

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 explicitly identifies the intended use case: 'Lead-gen agents use this for prospecting.' This gives clear context for when to invoke it, though it does not explicitly mention exclusions or alternative tools.

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 provided, the description carries the full disclosure burden. It clearly reveals the extractive nature of the summary, the fixed output shape (3 sentences + top-5 keywords), and the price per call. It does not mention potential input length constraints or language behavior, but for a simple text-summarization tool this is reasonably transparent.

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 entire description is one compact sentence that front-loads the core function and output structure, followed by a bracketed pricing note. Every element earns its place with no wasted words.

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 single-parameter tool with no output schema, the description covers what the tool does, what it returns, and what it costs. The main gaps are usage guidance and edge-case behavior, but the tool is simple enough that the current description is mostly adequate.

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 'text' parameter, so the schema already documents it as 'Input text'. The description adds 'any text' context but no additional parameter-level detail such as formatting, size limits, or language expectations. 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 specifies precisely what the tool produces: an extractive summary of exactly 3 sentences plus the top-5 keywords from any input text. This clearly identifies the resource (text) and the operation (summarization), and differentiates it from sibling tools like sentiment or hallucination_check.

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 states it works on 'any text' but gives no guidance on when to choose this over alternatives, nor any exclusions such as language, length limits, or text types. There are no sibling comparison or selection criteria provided.

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 of behavioral disclosure. It does reveal the price and that findings are confidence-labeled, but it does not state whether the tool is read-only, how output is returned, what the verify-loop lane means, or any limitations. The unexplained 'BUILD' and 'full sweeps' references add confusion rather than transparency.

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 first sentence is relatively compact, but the second sentence and bracketed price are promotional and not useful for tool selection or invocation. 'The flagship teaser — full sweeps at $0.50-5 ship when this crosses BUILD' should not be in a tool definition. The description would be more effective if reduced to one functional sentence.

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?

Even with only one parameter and no output schema, the description leaves the agent without a clear contract. It never defines 'sweep', 'verify-loop lane', or 'BUILD', and it does not explain what a result looks like. The price is the only concrete operational detail, but the overall definition is too vague to fully support correct invocation.

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%: the only parameter, topic, is described as 'Research topic.' The description adds that findings are 'on any topic,' implying no restrictions on the topic value, but it provides no format, examples, or additional constraints. The baseline 3 applies because the schema already documents the single parameter adequately.

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 says the tool produces 'confidence-labeled findings on any topic,' which gives a vague sense of a research/verification tool, but it never names the action (search, analyze, verify) or defines what a 'sweep' is. The 'flagship teaser' phrasing is promotional, not functional, and does not differentiate it from research siblings like search_verify, scrape, or sentiment.

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 is given about when to use this tool versus its alternatives. Sibling tools are not mentioned, and 'any topic' implies broad applicability without any criteria for when sweep is preferred over other research/verification tools. An agent must guess when to invoke 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?

No annotations are provided, so the description carries the burden. It discloses that the tool analyzes honeypot signals, proxy detection, and LP data, and mentions a price per call. It does not describe side effects, output shape, or error/edge-case behavior, but the read-only heuristic nature is reasonably inferred.

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 and front-loaded: what the heuristics cover, the use case, and pricing. Every sentence contributes distinct information with 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?

With no output schema and no annotations, the description only partially fills the gap. It names analysis categories but not the response format or how the signals are returned, so an agent may need to probe the output. The required parameter is in the schema, so invocation is still feasible.

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%, so the schema already documents the two parameters and their meanings. The description adds no extra parameter-level detail beyond the schema, which is acceptable but not enhancing.

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 identifies the tool's domain and purpose: token security heuristics covering honeypot signals, proxy detection, and LP data, for avoiding rugs. It lacks an explicit verb like 'checks' or 'evaluates', but the intent is unmistakable and distinct from all 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?

States that trading agents use this to avoid rugs, which implies the intended context. However, it does not explicitly state when to prefer this over alternatives or provide any exclusions or prerequisites.

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