Pyfile LLM & Data API

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 the output units (wei and gwei) and that the value is 'current,' implying a live read. However, it does not describe whether this is a read-only operation, how the value is obtained, or any freshness/error behavior. For a simple query tool this is acceptable, though slightly sparse.

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 short, well-structured sentence that conveys the resource, the value, and the units. Every word contributes meaning, and there is no filler or 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 simple, parameterless read tool, the description is largely sufficient: it names the network, the gas price, and both output units. Since there is no output schema, explicitly stating that the result includes both wei and gwei values is helpful. A minor gap is that it doesn't specify the result shape (e.g., separate fields vs. a formatted string), but this is unlikely to confuse an agent.

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 and the schema is empty (100% coverage), so there is nothing to clarify. Per the baseline for 0-parameter tools, the description adds sufficient meaning by stating what is returned.

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 query ('gas price') on a specific resource ('Base L2') and specifies the units returned (wei and gwei). It clearly differentiates from the sibling tools, which are about chat, crypto price, DNS, and Wikipedia – none of which overlap with an L2 gas price lookup.

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 exact use case: retrieving the current Base L2 gas price. It doesn't explicitly name alternatives or exclusions, but the tool is so specialized and siblings are unrelated that the context is fully clear. No additional exclusions are needed.

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 discloses that the tool fetches live market data, synthesizes it via an LLM, returns a structured narrative brief, and requires a Bearer key. It does not cover failure behavior or rate limits, but covers the most decision-relevant behaviors.

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 tight sentences front-load the definition and output, then state the auth requirement. No filler or repetition.

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 no output schema, the description explains the return shape well enough to know what to expect. It is complete for invocation—optional params, topic mapping, and auth are covered—but minor gaps remain around focus semantics and explicit sibling routing.

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 adds value by mapping the topic enum to concrete facts ('crypto' to crypto prices, 'defi' to DeFi yields, 'network' to Base block height), which is not stated in the schema. The focus parameter remains only lightly explained.

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 'Composite analyst brief' and states a specific behavior: it gathers live market facts (crypto prices, DeFi yields, Base block height) and returns an LLM-written brief. This makes the resource and output concrete and differentiates it from the single-fact sibling tools like crypto_price and base_gas.

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 'composite analyst brief' framing and mention of live market facts gives a clear context: use it when a synthesized, multi-source market brief is needed rather than a single fact. It also notes the Bearer-key prerequisite, though it does not explicitly name sibling alternatives or give when-not-to-use guidance.

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

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

With no annotations, the description carries the behavioral disclosure burden. It does state the output type ('text completion'), which is a useful behavioral trait. However, it omits other behavioral context such as latency, cost, determinism, or that the model parameter affects results. The description is not misleading, but it is minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with no filler. The core action is front-loaded ('Ask an LLM a question') and the return type follows immediately. 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 simple two-parameter tool with no output schema, the description covers the basic contract. However, with no annotations and no guidance on model selection or prompt expectations, the agent has to infer some behavior. It is adequate but not rich.

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 50%: only 'prompt' is described ('The user prompt'), while 'model' has an enum but no explanation. The description itself adds no parameter semantics—it does not clarify what 'model' means, what the options represent, or what the default does. Since coverage is low, the description needed to compensate but did 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?

The description uses a specific verb ('Ask') with a clear resource ('an LLM'), and explicitly states the result ('Returns a text completion'). This clearly distinguishes it from the sibling tools, which are all domain-specific lookup utilities like crypto_price and dns_lookup.

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 explicit when-to-use guidance, exclusions, or alternative routing is provided. The sibling tool names imply these are non-LLM tools, and the phrasing 'Ask an LLM a question' gives an implicit usage context, but the description never states when this tool should be chosen over alternatives.

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?

The description clearly frames the operation as a read-only price lookup and identifies CoinGecko as the data source, so an agent can infer it is safe and non-mutating. It does not disclose limitations such as invalid-ID behavior, rate limits, or response format, and no annotations are available to cover those 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?

The description is a single front-loaded clause that leads with the result ('current USD prices') and then adds the only necessary input-format detail. Every part earns its place, and there is no repetition or 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 simple tool with one optional parameter, a default value, no nested objects, and no output schema, the description plus schema provides enough to invoke the tool correctly. The main omission is explicit output-shape detail, but 'current USD prices' adequately signals the expected return for this straightforward lookup.

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 fully describes the single parameter, including type, default, and comma-separated CoinGecko ID format, so the baseline is 3. The description only reinforces that one or more IDs are accepted without adding meaningful new semantics 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 names the resource (coins) and the value delivered (current USD prices), and the CoinGecko-id note makes the input domain concrete. It lacks an explicit verb such as 'get' or 'return' and does not explicitly contrast with sibling tools, but the intent 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?

The description implies use for current USD cryptocurrency price lookups and makes the input format clear. It provides no explicit when-to-use/when-not-to-use guidance or alternatives, though the sibling tools are distinct enough that mis-selection is unlikely.

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. It clearly communicates a read-only network lookup behavior, but does not mention possible failures, timeouts, or what the returned records look like. This is acceptable for a simple lookup 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?

A single sentence with no filler, front-loading the action and resource. It is appropriately sized for a two-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple and the schema covers both parameters, but the description omits CNAME, does not mention the default type of A, and provides no information about return values or error behavior. Adequate for straightforward usage, but with clear gaps.

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 50%, and the description partially compensates by listing acceptable record types. However, it omits CNAME even though the schema permits it, and it adds no explanation of how the name parameter is used beyond what the schema already states.

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 ('Resolve') and resource ('a DNS record'), and lists the main record types. It does not explicitly differentiate from sibling tools, but the sibling tools are clearly unrelated domains, so there is no real 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?

Usage is implied by the verb and resource: use when a DNS record for a domain is needed. There is no explicit guidance on when to prefer this tool over alternatives, but no competing lookup sibling exists.

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 and does reasonable work: it discloses the auth requirement ('Requires a Bearer key'), the exact upstream sources queried, and that results come back with an aggregate 'risk_summary'. It does not cover rate limits, error behavior, or what happens when neither optional parameter is supplied.

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 front-loaded sentences with no filler. The data-source enumeration is dense but each clause carries information; the auth note is correctly placed last as a secondary constraint.

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?

Key omissions remain for a 2-optional-parameter tool with no output schema: the description hints at a 'risk_summary' but says nothing about the shape of the response, and it does not state whether name, domain, or both are needed for a meaningful call.

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 both parameters, so the schema already documents 'name' and 'domain' with examples. The description contributes no additional parameter meaning, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('enrich'/'One-call domain/company enrichment') and enumerates exactly what data is gathered: DNS A records, CT names, GLEIF LEI, OFAC SDN, and a web snapshot. This implicitly distinguishes it from single-source siblings like dns_lookup and ofac_sanctions_search, though it never names them explicitly.

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 'One-call' implies the tool should be preferred when the caller wants everything at once instead of calling the individual sibling tools, but this is left to inference. No explicit when-to-use, when-not-to-use, or alternatives are stated.

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 the commit-reveal protocol and the reproducibility requirement, which is real value. It still omits whether the call is read-only, whether the same inputs deterministically reproduce the output, and whether any server-side state (round) is created, which matters for a randomness tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with zero padding; the mechanism is front-loaded and the usage and follow-up guidance follow in priority order.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so the description correctly states the return is a random integer, and it explains the protocol needed to verify it. It is close to complete for a 5-optional-param tool, with only deterministic/purity behavior left unstated.

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 min, max, nonce, round and client_seed are already documented individually. The description adds the framing that the seed is committed and must later be revealed, which only marginally enriches client_seed 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?

States a specific verb+resource ('provably-fair random integer from a committed seed') with the commit-reveal mechanism named, so the operation is unambiguous. It does not, however, distinguish itself from the sibling fair_pick, which an agent would likely confuse it with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases ('verifiable winner selection, raffles and allocations') and a post-call workflow ('Reveal the seed afterwards so anyone can reproduce the result'). It stops short of exclusions or naming alternatives such as fair_pick, so the when-not side is left to inference.

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

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

No annotations exist, so the description carries the full burden. 'Provably-fair' is a strong claim that is never unpacked: the description doesn't explain how the fairness is achieved or verified (nonce/round/client_seed roles), whether the result is reproducible, or whether any server-side seed or auth is required. For a tool whose entire value proposition is verifiability, this is a substantial gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the core action and immediately followed by the scoping condition. No filler or 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?

With 4 parameters, no annotations, and no output schema, the description should explain the return shape and the verification/provably-fair mechanism. It omits both, leaving an agent unable to tell how to verify the result or what the tool returns.

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 candidates, nonce, round, and client_seed, making 3 the baseline. The description only loosely maps to the candidates parameter and adds no semantics about seed combination or nonce/round defaults beyond what the schema states.

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 action (provably-fair selection of one winner) and the resource (a list of candidates), plus the use case (single-prize bounties). It implicitly contrasts with fair_draw via 'one winner / single prize', but never names the sibling to make the distinction explicit.

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?

'For bounties with a single prize' gives a usage context, but there is no explicit when-not-to-use guidance and no pointer to fair_draw for multi-winner cases. The agent must infer that fair_draw is the alternative for multi-prize scenarios.

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 behavioral burden. 'Open data' hints that no auth is required and the tool is read-only, but nothing states rate limits, whether the default dataset is returned when kind is omitted, or what a call returns. For a zero-annotation tool this is a thin 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?

A single front-loaded fragment with zero padding, listing the highest-value information first. It is dense but lacks any sentence structure or scoping detail, keeping it just below the top score.

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, the description should disclose the return format and the effect of defaulting kind to drug/event, but it does neither. The dataset coverage is complete, yet the agent is left guessing about response shape and result-size behavior.

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 baseline is 3. The description does add some meaning by decoding the terse enum values ('drug/event' = adverse events, '*_enforcement' = recalls, 'drug/label' = labels), but it says nothing about q semantics or the limit parameter, so it does not clearly exceed 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 names the data source (US FDA open data) and enumerates the specific dataset domains it covers — adverse events, recalls, labels — which tells an agent exactly what resource it reaches. It omits an explicit verb (lookup/query), but the name plus the dataset enumeration makes the operation unambiguous and none of the siblings (dns_lookup, crypto_price, wikipedia_summary) overlap.

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 statement of when to use this tool, no prerequisites, and no guidance against alternatives such as web_read or wikipedia_summary for health/recall questions. The only implicit cue is the data-domain list, which the agent must infer from.

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 usefully discloses the auth requirement ('Requires a Bearer key') and that this fans out to three external registries, but says nothing about latency, rate limits, or partial-failure behavior when one of the three sources is unavailable.

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, zero filler, and the substance (what it aggregates) is front-loaded ahead of the prerequisite. Nothing is repeated from the schema or title.

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 should convey the shape of the composite result, but it does not indicate whether it returns raw sanctions hits, an LEI, EDGAR filings, or a merged verdict. For a three-source aggregation tool this leaves a meaningful gap, though what to pass in is fully 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?

There is a single parameter with 100% schema description coverage, so the schema already states 'Company name to screen.' The description adds no syntax, formatting, or disambiguation guidance (e.g., legal-entity vs. trade name), making the baseline 3 correct.

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 concrete verb-and-resource ('counterparty due-diligence') and enumerates the three data sources it aggregates (OFAC SDN, GLEIF LEI, SEC EDGAR), so an agent knows exactly what it returns. It stops short of explicitly differentiating itself from the sibling ofac_sanctions_search, which covers one of the same sources.

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 word 'Composite' and 'in one call' imply this is the broad option to reach for instead of chaining narrower tools, which is weak guidance toward ofac_sanctions_search. There is no explicit when-to-use, when-not, or named alternative, so selection logic is left to inference.

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

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

No annotations are provided, so the description carries the full disclosure burden. It reveals the authoritative source (US Treasury OFAC SDN) but says nothing about match semantics (exact vs fuzzy), result format, rate limits, or auth requirements. For a screening tool whose match behavior determines compliance decisions, this is a notable gap.

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 tight sentence with the verb and data source front-loaded and zero filler. Every clause 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 two-parameter lookup with no annotations or output schema, the description conveys the essential source and purpose. It omits anything about result interpretation or matching behavior, which an agent screening entities would benefit from knowing.

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 both q and limit documented in the schema, so the baseline is 3. The description's 'by entity name' corroborates the q parameter but adds no syntax, format, or matching hints beyond what the schema already says.

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: search the US Treasury OFAC SDN sanctions list, scoped to entity name. It names the exact data source, which is meaningful. It does not explicitly differentiate from the sibling kyb_screening, which likely overlaps in intent.

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 '(KYB/AML screening)' implies the compliance context in which this tool is used, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g. kyb_screening). Usage is inferable rather than stated.

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 usefully discloses the output shape (clean Markdown) and a real constraint ('public' pages only, implying no auth/paywalled content) plus that nav/ads are stripped. It does not cover JS-rendered pages, timeouts, redirects, or failure behavior for non-HTML content.

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 that packs verb, resource, output format, and intended use 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 simple 2-parameter tool with no output schema, the description adequately explains what is returned (clean Markdown). It is nearly complete, with only edge-case behavior (non-HTML pages, blocked or JS-heavy sites, redirects) left unaddressed.

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 both parameters are already documented in the schema and the baseline is 3. The description adds only the 'public' constraint on the url parameter and repeats no format or syntax details beyond what the schema 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 names a specific verb (Fetch), resource (public web page), and output transformation (main readable content as clean Markdown with nav/ads stripped). This is far more specific than a tautology. It doesn't explicitly differentiate from siblings like wikipedia_summary, but the siblings are largely non-overlapping so the risk of confusion 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 phrase 'for RAG and agent pipelines' hints at the intended context, and 'any public web page' implicitly scopes eligible targets. However, there is no explicit when-to-use/when-not guidance and no named alternative (e.g. use wikipedia_summary for encyclopedia topics), so usage must be inferred.

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 of behavioral disclosure. It states the output is a short summary, but does not describe missing-article handling, redirects, language behavior, or failure modes.

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 concise sentence with no filler. Both the purpose and the required input are immediately clear.

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 lookup tool, the description is mostly adequate, but the absence of an output schema or any mention of response format and error behavior leaves moderate gaps.

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 100% of parameters and describes 'title' with an example, so the baseline is 3. The description adds no further meaning beyond the parameter 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 the resource (Wikipedia article), the input (title), and the output (a short summary). It distinguishes the tool from unrelated siblings, though it lacks an explicit verb like 'retrieves' or '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 description conveys when to use the tool (when a Wikipedia article summary is needed by title), but there is no explicit guidance about alternatives, edge cases, or when not to use it.

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