Genexxis agent data APIs

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

No annotations are provided, so the description carries the burden. It usefully discloses pricing ($0.002 USDC via x402 on Base), sorting by severity, and returned fields, but omits update cadence, rate limits, error behavior, and auth details beyond payment.

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, front-loaded with the main resource and included program types. The pricing note is brief and useful. No wasted 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, no-output-schema, no-annotation tool, the description conveys purpose, returned fields, and payment model. It still leaves out output shape, data freshness, and response/error details, so it is adequate but not 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 optional type filter is fully documented in the schema. The description lists the program types but never explicitly says they can be used as a filter, so it adds little 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 resource (current FAA delay programs nationwide) and enumerates included program types, returned fields, and sorting. It is distinguishable by nationwide scope, but it never names or contrasts the sibling tools, so it falls short of a 5.

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

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

Provides no when/when-not guidance and does not mention alternatives such as airports_status or airports_weather. The nationwide scope implies a use case, but there is no explicit routing 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?

No annotations exist, so the description carries the full burden — and it does disclose the most agent-critical trait: this is a paid call ($0.003 USDC, x402 on Base), which affects whether an agent should invoke it at all. It also discloses the exact return shape including the disruption-level scale. Gaps: no mention of what happens on invalid/non-US codes, error behavior, or 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?

One dense sentence with purpose first, enumerations in the middle, and the price note last — front-loaded and waste-free. Slightly long, but every clause (programs, weather, disruption level, price) carries information an agent needs.

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

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

There is no output schema, so the description must describe returns — and it does, listing the exact data sources, the delay reason/minutes detail, and the four-level disruption scale. Combined with the explicit payment model, an agent knows both what it pays and what it gets. Nothing material is missing for a one-parameter read tool.

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?

Only one parameter at 100% schema coverage, so the schema already explains that 'airport' accepts IATA (JFK) or ICAO (KJFK) with examples. The description reinforces the single-airport scope and the FAA framing (which explains the US-only constraint). No syntax is added beyond the schema, but none is needed.

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

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

States a specific verb+resource ('Disruption check for one airport') and enumerates exactly what it returns: FAA ground stops/GDPs, delays, closures, decoded METAR, 12h TAF, and a none/minor/moderate/severe level. This is clearly the composite view of the siblings airports_delays and airports_weather, but the description never names those siblings, so differentiation is inferential rather than 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?

Usage is only implied: the bundled FAA + METAR + TAF content suggests this is the 'one-call overview' versus the narrower siblings, but the description never says 'use this instead of airports_delays/airports_weather when you want a combined view.' No exclusions, no statement of when a granular sibling would be better.

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

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

With no annotations, the description carries the full burden, and it does add genuinely useful context beyond structured data: the data window (TAF next 12 h) and, notably, the paid model ($0.002 USDC per call via x402 on Base). It omits rate limits, error behavior, and freshness/latency expectations, so the behavioral profile is only partly covered.

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 dense sentences front-load the data scope and end with the pricing constraint, with no filler. The field list is slightly enumerative but each item conveys actual return 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?

For a single-parameter, no-output-schema tool, the description usefully enumerates the decoded fields (wind, visibility, ceiling, flight category, temperature, weather, raw reports) which substitutes for a return schema, and it discloses the payment requirement. Only ancillary operational details are missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single 'ids' parameter (comma-separated, max 10, 3-letter US codes). The description adds 'worldwide' ICAO coverage but nothing about format or edge cases beyond the schema, making 3 a fair 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 resource and scope: decoded METAR/TAF weather for up to 10 ICAO stations, with the returned fields listed. It is clearly a weather tool rather than a status or delay tool, but the description never names the airports_status / airports_delays siblings or says how it differs from them.

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

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

There is no explicit when-to-use, when-not-to-use, or alternative-tool guidance. An agent must infer that this is the weather option versus airports_status and airports_delays from the name 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 full behavioral burden. It usefully discloses that the tool returns rate, date and source, and that calls cost $0.002 USDC via x402 on Base, but it does not explain payment prerequisites, error behavior, rate limits, or freshness guarantees beyond the source reference.

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, stating the conversion purpose first and then the source and pricing details. Every sentence adds useful information without 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?

For a paid FX conversion tool with no output schema, the description covers the rate sources, included output fields, and cost. It remains incomplete on payment mechanics for x402, possible conversion limitations, and failure modes, which matter for an agent invoking a paid endpoint.

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 documents the three parameters, including ISO 4217 codes and the default amount of 1. The description adds no parameter-level detail beyond that, 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 specifies a clear verb and resource: converting an amount between roughly 50 currencies using official reference rates. It adds useful scope details such as Bank Al-Maghrib for MAD pairs, ECB otherwise, and EUR cross-rates, but it does not explicitly differentiate this tool from sibling FX tools like macro_fx.

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 'Convert an amount between ~50 currencies,' and the rate-source context helps an agent understand the data basis. However, there is no explicit when-to-use guidance, no exclusions, and no named alternatives such as macro_fx for related FX data.

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

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

With no annotations, the description carries the full burden and does disclose the key non-obvious trait: this is a paid call at $0.003 USDC via x402 on Base, which an agent must know before invoking. It also clarifies that the FX rate is 'current official' with a date and source, implying freshness behavior. It stops short of stating latency, caching, or what happens on unpaid requests.

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, payload enumerated up front and pricing/access detail relegated to the end — correctly front-loaded. The enumeration is dense but each item tells the agent something it gets, so little is wasted.

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

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

There is no output schema, so the description must describe return values, and it does so concretely (rates, differential in pp, carry side, FX rate with date/source). The remaining gap is operational: no mention of error behavior, stale-data handling, or how the payment step is triggered.

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 both parameters carry their own descriptions (ISO 4217 or ISO2 country), so the schema does the heavy lifting. The description only refers to 'one currency pair' without adding format, ambiguity, or country-vs-currency resolution guidance, which is the baseline-3 case.

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

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

The description names a specific resource ('Carry view of one currency pair') and enumerates exactly what it returns: both policy rates with central bank and last change, the differential in pp, which side earns carry, and the current official FX rate with date and source. That is enough to distinguish it from ranking/convert siblings, though no sibling is named 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?

Usage is implied rather than stated: an agent can infer this is the single-pair drill-down versus carry_ranking (multi-pair) and carry_convert (conversion), but the description never says when to pick this one or names an alternative. No prerequisites or exclusions are given beyond the price.

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 and does a good job: it discloses the ranking basis, sorting order, FX-change condition, central-bank-rate-change flag, and the $0.01 USDC x402 payment model on Base. It could still say more about freshness or error behavior, but it provides substantial context beyond the schema.

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 dense but front-loaded, starting with the core ranking purpose before listing outputs and price. Every clause contributes useful information, though the single long sentence could be split for readability.

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

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

There is no output schema or annotations, so the description has to explain the return content, and it does: rate differential, FX change, and rate-change flag. It is complete enough for a two-parameter ranking tool, with only minor gaps around exact response shape or pagination 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 both parameters are already well documented. The description reinforces the funding-currency concept and the sorted-descending ranking behavior, but it does not add parameter-specific 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 a specific resource and operation: a carry ranking of about 38 currencies versus a funding currency. It distinguishes itself from a pair-level tool by emphasizing a cross-currency ranking, though it does not explicitly name sibling alternatives such as carry_pair or macro_policy_rates.

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 tool name and the description of the ranking output, but there is no explicit guidance on when to choose this over carry_pair, carry_convert, or macro_policy_rates. The description explains what the tool returns more than when an agent should 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?

With no annotations, the description carries the burden and does well: it discloses the 20-call cap, that each call individually returns success/decoded/raw hex or its revert reason (implying partial-failure tolerance rather than all-or-nothing), and the pricing/auth model ($0.004 USDC per call via x402 on Base). What's missing is whether mixed-chain calls are rejected, and any rate-limit or wallet-setup detail for x402.

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, front-loaded with capability and scope, then return semantics, then pricing. No filler; each clause carries information an agent needs before calling.

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 paid batch RPC tool with no output schema and no annotations, the description covers the essentials: batch limit, per-call result shape including revert handling, chain constraint, and cost model. Only edge behavior (mixed-chain rejection, whole-request failure modes) is 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 coverage is 100%, so the baseline is 3. The description adds only the repeated 20-item cap and the 'one chain' constraint (all calls share the chain parameter); it adds no meaning about args encoding, function signature format, or blockTag behavior 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?

States a specific verb and resource ('Up to 20 contract reads on one chain in a single Multicall3 request') and clarifies the batching mechanism, which cleanly separates it from the single-read sibling chainread_read_contract. It doesn't explicitly name the sibling, but the batching scope makes the distinction obvious.

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 'up to 20 reads on one chain' framing implies the batch use case (and implicitly that single reads belong elsewhere), but there is no explicit when-to-use/when-not guidance and no named alternative such as chainread_read_contract. Adequate but inferential.

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

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

With no annotations, the description carries the full burden and does well: it discloses decoded/raw return fields, block number and chain id, revert handling via 422, and per-call pricing via x402. It could be stronger by clarifying whether reverts are still charged or detailing rate limits/auth flow.

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 packs purpose, supported chains, return shape, error behavior, and pricing without unnecessary 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?

Given the full schema coverage and no output schema, the description supplies the missing return-value details, revert behavior, supported chains, and payment requirement. An agent has enough information to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all five parameters thoroughly. The description adds little parameter-specific meaning beyond reiterating human-readable signatures and supported chains.

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 verb and resource: call any view/pure function on six named chains. It distinguishes the tool from token- or resolve-specific siblings by scope, but does not explicitly differentiate it from chainread_multicall, so it falls short of a full 5.

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

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

There is no explicit when-to-use guidance, no when-not-to-use guidance, and no named alternatives such as chainread_multicall. The intended use is only implied by 'Call any view/pure function' and the supported chain list.

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 and handles pricing explicitly ($0.001 USDC per call, x402 on Base), which is critical for an agent deciding whether to invoke. It also clarifies that reverse results are forward-verified, implying a reliability caveat. Missing: what happens if both name and address are omitted (schema requires neither), and failure/timeout 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?

Two front-loaded clauses covering capability, format examples, and price with no filler. It is dense but every element (records list, forward-verified, pricing) earns its place; it could be split for readability but nothing is wasted.

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 2-param tool with no output schema and no annotations, the description covers the return shape (avatar, url, twitter, github, description), the reverse-lookup guarantee, and the payment model. Only the neither-param case and error behavior are unaddressed, which is a minor gap.

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 both parameters are already documented with examples (vitalik.eth, alice.base.eth). The description adds modest value by enumerating the record types returned, but adds no syntax, format, or mutual-exclusion guidance for the two optional params. 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?

States a specific verb (resolve), the exact resources (ENS / Basename names and addresses), and both directions of lookup. It is unmistakably distinct from siblings like chainread_token, chainread_read_contract, and chainread_multicall, which imply token/contract reads rather than name resolution.

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?

Clear about which input selects which behavior — pass a name for forward lookup with records, pass an address for reverse primary-name lookup. No explicit exclusion or sibling routing is given, but the tool's domain is narrow enough that the context is unambiguous.

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 at all, the description carries the full burden and does the most important part: it discloses the paid x402 model ($0.001 USDC per call, settled on Base), which an agent must know before invoking. It also lists the concrete data returned. It does not cover failure behavior (e.g. address that is not a contract) or rate limits, so it stops short of a 5.

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, both front-loaded with high-value information: what comes back first, the payment terms second. No filler, no restatement 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 2-parameter, 100%-documented, low-complexity read tool with no output schema, the description supplies the return-field list and the payment prerequisite, which is what an agent needs to decide and call. What is missing (output shape/structure, non-contract-address handling) is modest given 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 description coverage is 100% and the chain enum values (ethereum default, base, arbitrum, optimism, polygon, bsc) are fully listed in the schema, so the description is not required to compensate. 'For any contract' loosely reinforces that address is a contract address, but adds nothing about chain defaults or formatting 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?

Names a specific resource (token metadata for a contract address) and enumerates exactly what is extracted: ERC-20 name/symbol/decimals/total supply plus ERC-721/ERC-1155 detection via ERC-165. It implicitly separates itself from the generic chainread_read_contract by being token-standard aware, but it never names that sibling, so the differentiation is left to inference.

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 enumerated return fields imply when to reach for it (you want standard token metadata rather than arbitrary contract calls), but there is no explicit when-to-use/when-not statement and no alternative such as chainread_read_contract or chainread_multicall is named. Usage is deducible 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 must carry the behavioral burden. It usefully discloses the payment model ('$0.005 USDC per call, paid with x402 on Base') and lists the lint checks, but it does not state whether the content is stored or externalized, the response format, or that the operation is read-only.

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 front-loaded with the action, followed by a compact comma-separated checklist, then the price. It is dense but free of filler and appropriately sized for the information conveyed.

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

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

For a tool with no annotations and no output schema, the description explains what is checked and the cost, but it leaves the return shape unspecified—for example, whether findings include severity, line numbers, or remediation. The single parameter is fully documented, so the gap is moderate rather than severe.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single required parameter 'content' is fully documented in the schema (100% coverage), including the 100 KB limit. The description adds no additional parameter semantics, so 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 description uses a specific verb and resource: 'Lint a docker-compose file.' It enumerates the exact checks performed, and the file-type specificity distinguishes it from siblings such as ciaudit_dockerfile and ciaudit_github_actions.

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 file type implies when to use it, but there is no explicit when-to-use/when-not guidance or routing to alternatives. An agent comparing this against ciaudit_dockerfile or ciaudit_github_actions receives no direct help.

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 and does well: it discloses the return shape (findings with line, why, fix, and a score) and the payment mechanic ($0.005 USDC per call via x402 on Base), which is real operational context an agent needs before invoking. It stops short of stating failure modes or whether the call is idempotent/read-only, but lint semantics make that largely self-evident.

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

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

One dense, front-loaded sentence listing the checks, followed by a short returns/pricing note. The rule enumeration is long but each item is a concrete check, so it earns its space; the pricing clause is separable metadata rather than description 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?

For a single-input lint tool with no output schema and no annotations, the description supplies what is missing: the checks performed, the structure of findings, the score, the input size limit (in schema) and the cost model. Nothing an agent needs in order to call it correctly is absent.

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?

Only one parameter and schema description coverage is 100%, so the schema already documents 'content' as the full file text with a 100 KB cap. The description adds nothing about the parameter, which is acceptable at this coverage level but unremarkable.

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?

Names a specific verb (lint) and resource (Dockerfile) and enumerates the exact rule families checked, so the agent can tell it apart from ciaudit_compose and ciaudit_github_actions at a glance.

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

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

Usage is only implied by the resource name 'Dockerfile'; there is no explicit statement of when to prefer this over ciaudit_compose or what input qualifies beyond the schema's 100 KB note. An agent can infer the context, but nothing routes it deliberately.

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. It usefully discloses the commercial/transport model ('$0.005 USDC per call, paid with x402 on Base'), which an agent cannot get from structured fields, but says nothing about what the response contains (findings shape, severity, line references) or any failure behavior for oversized input.

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

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

Front-loaded with the action and resource, followed by a compact enumeration of checks and the price sentence. The check list is long but each entry defines scope, so it earns its place; only minor tightening is possible.

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 should say more about the result an agent will receive (e.g. list of findings with rule names/locations). Purpose, input, and pricing are complete, but the return contract is left entirely implicit for a paid 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?

A single parameter 'content' is already documented at 100% schema coverage including the 100 KB cap, so the schema does the work. The description adds no further semantics about the parameter, meaning baseline 3 applies rather than a higher score.

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 ('Lint a GitHub Actions workflow') and then enumerates the exact defect classes it detects, so an agent knows precisely what the tool does and how it differs from ciaudit_compose and ciaudit_dockerfile, which target other artifact types.

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: the tool lints a single workflow file and there is no note on when to prefer it over sibling audits or when it is inappropriate (e.g. non-GitHub CI configs). It also never states that it only analyzes and never modifies, which an agent must infer from 'Lint'.

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 burden. It usefully discloses that each call costs $0.005 USDC via x402 on Base, which is important invocation context, but it does not describe return format, error behavior, rate limits, or payment failure handling.

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 with the core purpose front-loaded and pricing information clearly appended. Every sentence earns its place with no redundant wording.

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 annotations and no output schema, the description should carry more of the behavioral and return-value burden. It covers purpose and payment cost, but omits output shape, limit semantics, and payment/error mechanics, leaving moderate gaps 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?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful semantics for vcpu and memory as minimum thresholds and clarifies region is optional, but it ignores the limit parameter and does not fully explain the type values beyond on-demand or spot.

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 resource and scope: cheapest AWS EC2 Linux instances meeting minimum vCPU and memory, with on-demand or spot pricing and optional region. It is clear enough to distinguish from a generic pricing tool, but it does not explicitly differentiate itself from siblings like cloudprice_gpu or cloudprice_instance.

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

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

It gives clear usage context: use when you need the cheapest EC2 Linux instance satisfying at least the given vCPU and memory, with optional region and pricing type. It does not state when not to use it or name alternative sibling tools, so it falls short of full when/when-not 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, and it does disclose the most consequential trait: a paid call at $0.01 USDC via x402 on Base. It also tells the agent what the response contains (cheapest price, per-provider summary, 30-day history). Gaps remain around wallet/auth requirements for x402 settlement and failure behavior when no price is found.

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, with the capability statement and coverage scope front-loaded before the price disclosure. The first sentence is a long run-on carrying many facts, but 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?

No output schema exists, and the description compensates by stating what comes back: the cheapest GPU-hour plus a per-provider summary and 30-day history. Combined with full schema coverage, an agent has enough to invoke correctly; only the payment/auth mechanics and no-result behavior are 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 coverage is 100%, so a 3 is the baseline, but the description adds real value by fully enumerating the valid `model` values (H100, H200, B200, A100, MI300X, L40S, L4, A10G, A10, V100, T4) rather than the schema's three examples. It also reinforces the on-demand/spot dimension of `type`. The `limit` parameter is not mentioned.

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?

Specific verb+resource ("cheapest cloud GPU-hour for a GPU model") with an explicit enumeration of supported models, providers (AWS/Azure), and pricing modes (on-demand/spot). It doesn't explicitly contrast itself with sibling tools cloudprice_compute and cloudprice_instance, which is the one thing an agent choosing between the three would want.

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 model list and the on-demand/spot framing, but there is no explicit statement of when to pick this versus cloudprice_compute or cloudprice_instance. No prerequisites, no exclusions, no guidance on when the 30-day history or per-provider summary is the reason to call it.

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

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

With no annotations, the description carries the full burden and does add real behavioral detail: a per-call price of $0.005 USDC paid via x402 on Base, and results ordered cheapest region first. It omits what happens on payment failure, rate limits, or error modes, so it is not fully complete for a paid endpoint.

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 with zero waste: the core result and ordering come first, followed by the payment model. 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 read tool with no output schema, the description adequately conveys the return shape (all regions, cheapest first) and the billing model. Payment-failure and error behavior are the only meaningful omissions.

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 both parameters are already documented in the schema, and the description adds only mild context ('on-demand and spot' hints at the priceType values, 'in every region' clarifies the type's scope). Baseline 3 is appropriate since the schema does the heavy lifting.

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: hourly price of one cloud instance type across every region, covering AWS EC2 and Azure VM SKUs, on-demand and spot, sorted cheapest-first. An agent can immediately tell this is a per-instance-type price lookup, though it never names how it differs from siblings like cloudprice_compute or cloudprice_gpu.

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

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

Usage is only implied via the phrase 'one cloud instance type' and the on-demand/spot scope; there is no explicit statement of when to choose this over cloudprice_compute, cloudprice_gpu, or list_services. No prerequisites or exclusions are given, leaving sibling selection 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?

With no annotations, the description carries the full burden and does well: it discloses the 72h window, that all words must match (AND semantics), ticker-to-tag mapping, sort order, and that results include a corroboration count. It also surfaces a critical constraint — payment of $0.002 USDC per call via x402 on Base — that an agent must know before invoking; only error/empty-result behavior is unstated.

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

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

Front-loaded with the resource and scope, then pricing, with essentially no filler. The first sentence is long and clause-stacked, but every clause adds operational information.

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

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

No output schema exists, yet the description discloses key return traits (newest-first ordering, corroboration count), and it covers cost/auth requirements. Only edge-case behavior (zero matches, rate limits) is absent, which is minor for a paid search endpoint.

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 earns more by explaining matching semantics ('all words must match') and the ticker/asset-name-to-tag mapping, which the q schema description does not state. The 'last 72 h' phrase also clarifies the hours parameter's default intent.

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

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

States a specific resource ('crypto headlines'), a scope ('last 72 h'), and a filter mode ('matching keywords or tickers'), which implicitly distinguishes it from cryptonews_top. It stops short of naming the sibling so an agent must still infer which of the two to pick.

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 'matching keywords or tickers' — call it when you have search terms. There is no explicit when-not guidance or reference to cryptonews_top, so the routing decision between the two crypto tools 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?

With no annotations, the description carries the full burden and does well: it discloses the ranking/dedup algorithm, the exact output fields (headline, outlets, links, firstSeen, tags), and — critically — the payment mechanism and cost ($0.002 USDC via x402 on Base), which affects invocation planning. It stops short of rate limits, failure modes, or what happens when fewer than N hours of news exist.

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 dense sentences with the core purpose and ranking logic front-loaded; no filler. The output-field list and pricing are slightly compressed into the tail, but every clause carries information an agent needs.

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 and no annotations, yet the description covers purpose, ranking semantics, return fields, and the payment requirement — enough to call it correctly. Minor omissions are behavioral edges (empty/short windows, pagination behavior beyond limit).

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 ranges and defaults already documented for hours (1-72, default 24) and limit (1-50, default 20). The description only echoes 'last N hours' and adds no format, interaction, or edge-case guidance beyond the schema, so 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?

States a specific resource and scope: 'Top crypto stories of the last N hours,' plus the exact ranking logic (distinct-outlet count, then recency) and the source pool (13 outlets). It is clear what the tool returns, but it never names or contrasts with the sibling cryptonews_search, leaving the agent to infer 'top/ranked' vs 'search'.

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

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

Usage is only implied by the phrase 'Top crypto stories' and the deduplication/ranking rationale. There is no explicit when-to-use, when-not-to-use, or routing to cryptonews_search for keyword/topic lookups, which is the obvious alternative.

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 well by disclosing the x402 payment requirement, price, and the returned intelligence fields. It stops short of explicitly stating read-only behavior, side effects, rate limits, or error 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 purpose is front-loaded in a single dense sentence, and the price note is separate and direct. The output-field list is long but earns its place because there is no output schema.

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?

Because there is no output schema, it is useful that the description lists returned fields such as CVSS, CWE, KEV status, EPSS, and patch priority. It also explains payment, though it could say more about failure modes or 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 description coverage is 100%, so all five parameters are already documented in the schema. The description mentions severity, days, maxItems, and id/keyword lookup, but adds no meaningful syntax or semantic 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 states a specific verb and resource: CVE lookup by exact id or NVD keyword search. It also exposes the main filters, but it does not differentiate this general lookup from siblings such as cve_kev or cve_packages.

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

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

It identifies id-based and keyword-based modes, but gives no explicit guidance on when to use this tool versus cve_kev or cve_packages. There are no exclusions or routing hints for overlapping CVE-related 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 full burden; it does usefully disclose the paid-call requirement ($0.002 USDC via x402 on Base) and that results are enriched with EPSS scores. However it says nothing about rate limits, error behavior, or how the paid flow must be handled before calling.

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, front-loaded with the resource and filters, then the cost. Little waste, though the pricing sentence could be separated more clearly as preconditions.

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 compensates reasonably by naming returned fields (due dates, required action, EPSS score). Remaining gaps are the paid-call mechanics and result-set limits, the latter partly covered by maxItems in the schema.

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 baseline is 3; the schema already documents defaults and max values for days, maxItems, vendor, product and ransomware. The description adds no syntax detail (e.g. exact matching versus contains, ransomware value format) 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 resource (CISA Known Exploited Vulnerabilities) plus its filter axes, which an agent can distinguish from generic CVE lookup siblings like cve_cve. It never names a sibling tool explicitly, so the differentiation is inferred rather than stated.

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 lists filter options but gives no guidance on when to pick this over cve_cve or cve_packages, no prerequisites, and no exclusions. An agent must guess the boundary between the three cve_* 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, the description carries real weight: it discloses the paid x402-on-Base model at $0.003 USDC per call, the 50-item cap, and the upstream sources (OSV.dev, CISA KEV, EPSS). It omits failure behavior and whether partial results are returned when some packages fail to resolve.

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

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

One dense sentence covering action, source, return shape, and pricing, with no filler. The scope ('up to 50 packages') and cost are front-loaded where the agent will see them.

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

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

There is no output schema, so the enumeration of return fields is necessary and well done. Pricing, batching limits, and enrichment sources are all covered; only error/partial-failure semantics are missing, which is a minor gap for a read-style scan tool.

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 single 'packages' parameter is already documented. The description confirms the three fields (ecosystem, name, version) and that version is optional, but adds nothing beyond the schema about the string encoding of the list or the consequences of exceeding 50 items.

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 ('Scan up to 50 open-source packages ... against OSV.dev') and enumerates the returned vulnerability fields. This clearly distinguishes it from the single-lookup siblings cve_cve and cve_kev, which return one advisory rather than a batch scan.

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 batch-dependency-audit use case is implied by 'up to 50 packages', but the description never names cve_cve or cve_kev as alternatives or states when to prefer this tool over them. An agent must infer the routing from the plural 'packages' framing.

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

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

With no annotations, the description carries the full burden and does disclose the key non-obvious behavioral fact: this is a paid call at $0.003 USDC per call settled via x402 on Base, which an agent must know before invoking. It also implies the data granularity (1d/7d change, 90 days of history), but says nothing about rate limits, error behavior, or data freshness.

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 core capability is front-loaded in one dense sentence that enumerates the modes in the same order an agent would reason about them, followed by a short payment notice. It is information-dense rather than padded, though the single mega-sentence packs three modes together and would read faster as a short list.

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 5-parameter tool with no output schema and no annotations, the description covers the critical unknowns: what data comes back per mode and that the call costs money. Gaps remain around result limits (maxItems default 10 is only in the schema), pagination, and the absence of an output schema means return shape for the chains mode is only loosely implied.

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 a 3 is the baseline; the description earns above that by explaining what each mode actually returns (protocols/chains/protocol) and by surfacing the fields available in protocol output (TVL, 1d/7d change, market cap, chains), giving the 'mode' and 'category' parameters real semantic weight. It does not clarify 'maxItems' or the name/slug format 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 names a specific resource (DeFi TVL from DefiLlama) and enumerates the three distinct output shapes it can return: filtered protocol lists, chain TVL rankings with protocol counts, and single-protocol per-chain splits with 90-day history. This clearly separates it from siblings like defi_pool, defi_summary, and defi_yields 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?

The three modes are described with enough detail that an agent can infer which mode fits which question (ranked/filtered lists vs. chain-level rankings vs. deep-dive on one protocol). However, no sibling alternatives are named and no explicit exclusions ('use X instead when...') are given, so routing across the several other defi_* tools 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?

With no annotations provided, the description carries the behavioral burden and does well: it specifies the data returned, the 30-day history window, min/max/avg aggregates, and the paid x402/Base cost. It does not explicitly state read-only behavior or error conditions, but the retrieval nature and pricing are clearly disclosed.

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. The core resource and returned fields are front-loaded, and the pricing/payment sentence is also operationally relevant.

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 data-retrieval tool with no output schema, the description is complete enough: it explains what the tool returns, the historical range and aggregates, and the payment model. An agent has enough information to invoke it correctly given the id.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the single parameter is fully documented in the schema as a DefiLlama pool UUID from /yields. The description adds no material meaning beyond the schema, 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 names a specific resource and scope: one DeFi pool identified by DefiLlama id. It also enumerates the returned data (APY split, TVL, risk flags, 30-day APY/TVL history), making it distinguishable from list-oriented siblings such as defi_yields.

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: use this when you have a DefiLlama pool id and need detailed data for that single pool. The id source is hinted via 'from /yields', but the description does not explicitly say when to use this versus defi_yields or defi_summary, nor does it state exclusions.

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 and does add real behavioral context: cost of $0.002 USDC per call via x402 on Base and the omit-chain default of top 25 chains. It omits error handling for an invalid chain name and the size/latency of a 25-chain response, which for a paid call an agent would want to know.

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-loaded with the output content and ending with the payment constraint. No padding or redundancy across clauses.

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 usefully previews the return payload (median APY, top 5 projects, pool counts) and discloses pricing and the default scope, which is most of what a caller needs. Missing only failure behavior and rough response shape for the 25-chain case.

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 baseline is 3. The description restates the omit-for-top-25 default rather than adding new semantics such as accepted chain-name format or casing, so it adds nothing 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 a specific resource and scope: a per-chain DeFi yield overview with TVL-weighted median stablecoin APY plus top-5 projects by TVL and pool counts. That distinguishes it in kind from the pool-level siblings (defi_pool, defi_yields), though it never names an alternative outright.

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

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

It states the two usage modes — pass one chain, or omit to get the top 25 — which implicitly tells the agent how to call it. But there is no explicit when-to-use guidance versus defi_yields/defi_pool, and no exclusion or prerequisite beyond the payment note.

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

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

With no annotations, the description carries the full burden and does disclose the most decision-relevant trait: this is a paid call at $0.003 USDC via x402 on Base, which affects whether the agent should invoke it. It also enumerates the risk flags returned (APY spike, reward-heavy, IL, low TVL, multi-asset exposure), which tells the agent what judgment the tool performs. It omits rate limits, failure modes, and whether ranking is by risk-adjusted APY or raw APY.

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 dense sentences: capability plus filters first, output contents second, commercial terms last. No filler, nothing repeated, and the front-loaded clause tells the agent immediately what it ranks.

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 must convey both behavior and return shape, and it does explain the returned APY and risk flags plus the payment requirement. The gaps are modest: it does not state sort order, whether results are paginated beyond 'limit', or what an empty result means.

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 every parameter is already documented with examples and defaults (USDC/ETH, Ethereum/Base, 1-50 default 20, TVL default 1000000). The description restates the same filters without adding format, precedence, or combination rules. Baseline 3 is appropriate since the schema does the work.

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

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

States a specific resource and action: ranked DeFi yields with named filters (asset, chain, min TVL, stablecoin-only, single-sided) and a described output shape (risk-adjusted APY plus risk flags). That is well beyond a tautology. It does not name or differentiate itself from the adjacent defi_pool / defi_summary siblings, which keeps it at 4.

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 filters imply when the tool is useful (screening pools by safety/liquidity traits), but there is no explicit when-to-use or when-not-to-use statement. Crucially, it never says how it differs from defi_pool or defi_summary, so an agent facing three DeFi tools must guess. Usage is only inferred from the description.

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

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

With no annotations, the description carries the full behavioral burden and covers the costly/operational facts well: pricing at $0.002 USDC per call, payment via x402 on Base, the FX rate source, and that the rate date is returned. It omits error/rate-limit behavior and how the premium is expressed (absolute vs percentage), which keeps it short of a 5.

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: the operation and sources first, the default scope second, cost and payment last. Every clause earns its place 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-optional-parameter read tool with no output schema, the description supplies the essential context: what is computed, from which sources, the default universe, and the cost. The remaining gap is the return shape — notably whether the premium is a percentage or a spread — which an agent would need to interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the single symbols parameter (comma-separated, max 30, example tickers, omit-for-default) is already fully documented. The description restates the omit-for-default behavior but adds no new syntax or format detail, so 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 description names a specific metric (Korea/kimchi premium per coin) and the exact data sources compared: Upbit and Bithumb KRW prices versus Binance USDT, converted via the ECB USD/KRW reference rate. This is unambiguous and clearly distinct from siblings like depth_slippage or macro_fx.

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

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

It gives clear operating context: pass symbols to scope the query, omit them to get the top 20 Upbit markets by 24h KRW volume. However, it never states when to prefer this over adjacent siblings (depth_slippage, macro_fx, carry_pair) or any exclusions.

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 and does well: it discloses that the order is simulated (no real execution), that five venues are covered, and that access is metered at $0.003 USDC per call via x402 on Base — genuine auth/cost context an agent cannot get elsewhere. It stops short of failure modes or latency, keeping it below 5.

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 dense sentence with the operation front-loaded and the return fields enumerated after — nothing redundant. The trailing pricing clause is a separate concern appended to the same sentence, slightly hurting structure but not clarity.

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

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

There is no output schema, so the description must describe return values and it does so explicitly, including the depth bands (0.5%/1%/2%) and the fill-status flag. With payment terms and simulation semantics also covered, an agent has everything needed to decide and 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% with all three required params documented in-schema (usd range 10–50,000,000, side buy/sell, symbol ticker). The description adds no syntax or format detail beyond that, so the baseline 3 is 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?

States a specific verb and resource ('Simulated market order on five spot venues for a USD amount') and enumerates the exact outputs (average fill, slippage bps vs mid, worst price, fill status, best venue, depth bands). This clearly separates it from the depth_kimchi sibling, which is a single-venue comparison tool.

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 (checking execution cost and liquidity for a given order size) and gives size bounds via the schema, but never states when to prefer this over depth_kimchi or other market-data siblings, nor any exclusions. Usage is inferable but not spelled out.

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

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

With no annotations, the description carries the full burden and does well: it discloses the paid model ('$0.01 USDC per call, paid with x402 on Base') and the shape of what comes back (score, subscores, top 5 fixes). It does not state that the operation is read-only/non-mutating or mention rate limits or failure behavior, so it falls short of a 5.

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 dense sentence that front-loads the action and the coverage list; every clause (checks, score, fixes, price) carries information. It is a long run-on enumeration rather than cleanly broken structure, but nothing is padded.

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

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

No output schema exists, and the description compensates by naming the key return fields (0-100 overall score, subscores, top 5 fixes) and the payment requirement. For a one-parameter read tool this is close to sufficient; only error/empty-result behavior and latency expectations are 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?

One parameter with 100% schema description coverage ('Domain, e.g. example.com'), so the schema already does the work and the baseline is 3. The description adds only the nuance that header checks run against https://<domain>, i.e. the scheme is assumed rather than supplied, which is a minor clarification.

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?

Opens with a specific verb+resource ('Full domain security audit') and enumerates exactly what is inspected: headers on https://<domain>, TLS, email, DNS records, RDAP, security.txt, robots.txt, plus a 0-100 score. The word 'Full' implicitly contrasts with the narrower siblings domainaudit_headers, domainaudit_tls and domainaudit_email, though the differentiation is never stated 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?

Usage is only implied: an agent can infer this is the aggregate option versus the single-purpose domainaudit_* tools. There is no explicit 'use this when you want everything at once, use domainaudit_headers for just headers' routing, and no prerequisites or exclusions.

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 load and does well: it discloses the payment rail and cost (x402 on Base, $0.02/call), the request body format, and even a negative capability ('contrast not measured') that prevents false expectations. It stops short of stating wallet/auth prerequisites or response time for a multi-check operation.

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

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

Front-loaded with the purpose, then the check inventory, then cost — a sensible order for an agent. The long enumeration in the first sentence is dense but every item is a distinct capability; nothing is padding.

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 complex multi-check tool with no output schema and no annotations, the description supplies output shape (section scores, overall 0-100, top 10 fixes), cost, and input format. Remaining gaps are latency, rate limits, and payment prerequisites, which matter but are secondary to 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% and the single 'url' parameter is already documented as accepting a URL or plain hostname, so the schema does the heavy lifting. The description's 'Body {"url":"https://..."}' restates the schema example without adding accepted hostname forms or validation behavior beyond it.

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 ('One-call website report for a URL') and then enumerates exactly which checks the bundle covers: on-page SEO, accessibility items, security headers, TLS, email anti-spoofing, RDAP. The enumeration inherently separates it from the narrow siblings like domainaudit_seo or domainaudit_tls without needing to name them.

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

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

Usage is only implied: the phrase 'One-call website report' plus the price hint at when the bundle is preferable to single checks, but there is no explicit 'use this when you need everything; use domainaudit_headers for headers only' guidance. The $0.02 price is the only decision aid offered, and no sibling prices are referenced for comparison.

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 does disclose meaningful behavior beyond structure: a per-call price of $0.003 USDC paid via x402 on Base, which signals a paid, likely live network operation. However it says nothing about rate limits, failure modes, permission/auth requirements for the x402 payment, or response 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?

Two dense sentences with zero waste; the returned-data inventory is front-loaded, and the pricing caveat is a compact trailing sentence. 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 one-parameter lookup with no output schema and no annotations, the description enumerates exactly which data categories come back, which is the key thing an agent needs. It stops short of describing return shape, error behavior, or payment mechanics for x402, but is otherwise sufficient to call 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 single 'domain' parameter is fully documented in the schema (100% coverage) with an example, so the schema does the heavy lifting. The description adds no extra meaning about accepted formats, IDN support, or TLD restrictions, making the baseline 3 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 states exactly what the tool returns: RDAP registration data (registrar, created, updated, expires, status codes, nameservers, DNSSEC) plus DNS A/AAAA/MX/NS/TXT/CAA records. The verb+resource are unambiguous, though it does not explicitly contrast itself with siblings like domainaudit_whois_bulk or domainaudit_audit, leaving an agent to infer the difference.

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

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

Usage is only implied — an agent can deduce this is a one-domain, all-in-one lookup, but there is no explicit when-to-use statement, no conditions, and no named alternative (e.g., when to prefer domainaudit_whois_bulk for many domains). The per-call price does hint at cost consideration per invocation.

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 disclosure burden and does meaningful work: it reveals the cost and payment mechanism (x402 on Base), and it describes the shape of the result — per-record findings plus 'flags and a verdict' — which tells the agent a synthesized judgment is returned, not raw DNS dumps. It stops short of stating that this is a purely read-only public-DNS lookup with no side effects, and gives no latency, rate-limit, or failure-mode information for a paid call.

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

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

One dense sentence front-loads the resource and the checklist of checks, then a second short sentence delivers the price and payment rail. Every clause is a scannable fact — no filler, no restatement 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?

There is no output schema, so the description must convey return content, and it does so reasonably: check inventory plus flags and verdict. Cost and payment method are covered, which matters for a paid tool. What is left out — exact output field names, response format (JSON), and whether checks failing to resolve are reported as errors or as findings — is minor for a single-parameter read tool.

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?

Only one parameter, and the schema already documents it fully ('Email domain, e.g. example.com'), giving 100% coverage. The description adds no syntax, normalization, or IDN/subdomain handling detail beyond that, so the schema does all the work and 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?

Names a specific resource (a domain's email spoofing protection) and enumerates exactly which records are inspected — MX, SPF with recursive lookup count and all qualifier, DMARC tags, MTA-STS, TLS-RPT, BIMI, DKIM selectors — plus a verdict. That enumeration implicitly separates it from domainaudit_tls and domainaudit_headers, but no sibling is ever named explicitly, so differentiation is inferred rather than stated.

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 subject matter: run this when you need to assess a domain's email anti-spoofing posture. There is no explicit when-to-use/when-not-to-use guidance and no statement about when a caller should prefer domainaudit_audit (the broad audit) over this narrower email-specific one. The one genuine usage constraint given is commercial: $0.003 USDC per call paid via x402 on Base.

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 $0.003 USDC x402 payment requirement and the per-check value/fix output. However, it does not state whether the tool is read-only or side-effect-free, whether it makes live network requests, what rate limits apply, or how errors surface.

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 purpose and the concrete checks are front-loaded in a single dense sentence, followed by the pricing detail. Every element earns its place, and there is no boilerplate 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?

For a one-parameter tool with no output schema, the description explains the grading scale, the checks performed, and that each check returns a value and concrete fix. It could go further on response format and error behavior, but the core is complete enough for an agent 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?

There is a single required 'url' parameter and schema description coverage is 100%, so the schema already documents acceptable formats such as https:// or plain hostname. The description adds no parameter-level semantics beyond the overall scope, which matches the baseline of 3 when the schema does the documentation work.

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, 'Grade', a clear resource, 'web page', and enumerates the exact HTTP security header checks, from HSTS to http->https redirect. This makes its purpose clear and implicitly distinct from domainaudit_tls and domainaudit_email, but it never explicitly names a sibling or states how it differs from domainaudit_audit.

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 says what the tool does but gives no when-to-use guidance, no exclusions, and no alternatives among sibling domain audit tools. Apart from the price, the agent must infer the selection context entirely.

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

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

With no annotations, the description carries the burden well: it discloses the full set of checks, the pass/warn/fail scoring output, and the $0.005 USDC x402 payment requirement. It does not mention rate limits or failure behavior, but the cost and output model are valuable behavioral context.

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 purpose is front-loaded, but the description is a long run-on sentence with a dense semicolon-separated checklist. The content is useful, but it would be easier to parse if the checks were grouped or formatted.

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 audit tool with no output schema and no annotations, the description is fairly complete: it explains the checks performed, the scoring model, and the payment method. It still leaves the exact response shape and error behavior unspecified.

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 url parameter is already documented as a page URL or plain hostname. The description only reinforces that this is a single-URL audit and adds no additional parameter 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 states a specific verb and resource: an on-page SEO audit of one URL. It then enumerates the exact checks included, making the scope distinct from broader domainaudit siblings such as domainaudit_audit or domainaudit_headers.

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 it by saying it audits one URL for on-page SEO, but it never explicitly names alternatives or says when not to use it. An agent can infer the intended use, but routing guidance is left implicit.

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

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

With no annotations, the description carries the full burden and does so well: it discloses the fixed port, the exact class of data returned, flagging thresholds (expiry < 21 days, TLS < 1.2), and the paid-access model including price, currency, and payment rail (x402 on Base). It omits timeout behavior, rate limits, and whether failures are reported in-band, which keeps it short of a 5.

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

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

Front-loaded with the core action, followed by a dense but purposeful enumeration of returned attributes and a separate short line for pricing. Every clause describes real output or cost information; it is long only because no output schema exists to carry that 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?

With no output schema and no annotations, the description must explain both results and operating constraints, and it does: it lists the certificate/chain fields and flag thresholds and states the payment requirement. Remaining gaps are minor operational details (timeout, error signaling, whether an IP literal is accepted).

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 there is only one parameter ('host', documented as 'Hostname, e.g. example.com'). The description does not add syntax or constraint detail (e.g., no IPs, no scheme/port prefix) beyond the schema, so the baseline of 3 applies.

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

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

States a specific verb and resource ('TLS check on port 443') and enumerates exactly what is inspected, so an agent knows this is the certificate/TLS-handshake tool. It does not explicitly separate itself from a sibling like domainaudit_audit, which plausibly overlaps in scope, so differentiation is implied rather than stated.

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 narrow TLS scope and the fixed port 443, but there is no explicit 'use this when...' guidance, no statement of when domainaudit_audit or domainaudit_headers would be the better choice, and no prerequisites (e.g., reachable host, non-IP input).

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

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

With no annotations, the description carries the full burden and does well on the operationally critical parts: explicit price ($0.01 USDC per call), payment rail (x402 on Base) and parallel lookup. It does not say what happens on partial failure (a bad domain among ten), whether the call is still charged, or any rate limits, which keeps it below 5.

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 dense sentences with zero filler: capability and return fields first, cost and payment mechanism second. Nothing is repeated from the schema beyond the useful 'max 10' limit.

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

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

No output schema exists, so listing the returned fields (registrar, created, updated, expires, days left, status, nameservers, DNSSEC) is genuinely necessary and present, as is the pricing. Only error/partial-failure semantics are missing for a paid batch 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 coverage is 100% and the single 'domains' parameter already documents the comma-separated, max-10 format. The description restates the limit and adds the result field list rather than parameter-level detail, so 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?

Names a specific data source (RDAP registration data), a concrete scope (up to 10 domains in one call) and enumerates the fields returned. The 'bulk' framing implicitly separates it from the single-domain siblings like domainaudit_domain_info, though no sibling is named 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 'up to 10 domains in one call' wording implies the batch case versus a single-domain lookup, but there is no explicit when-to-use rule, no stated alternative (e.g. domainaudit_domain_info for one domain), and no exclusions.

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?

Without annotations, the description carries the full behavioral burden and discloses key traits: returned fields, 'newest first' ordering, optional filters, and the payment model ($0.003 USDC per call via x402 on Base). It does not cover error handling or rate limits, but it provides substantial operational context beyond the schema.

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 front-loaded with the core purpose and packs useful details (returned fields, example request, pricing) into two sentences. It is dense but every element serves a clear function, with no redundant 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?

The description lists the returned fields and pricing, which compensates for the lack of an output schema, but it omits a crucial requirement: although only 'concept' is required, the user must supply either 'ticker' or 'cik' to identify the company. This gap could cause an agent to invoke the tool without a company identifier.

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 all six parameters, so the baseline is 3. The description adds an example body and mentions optional form and unit filters, but it does not deepen semantic understanding beyond what the schema already documents.

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 it returns values of one XBRL concept for a US company, listing the specific fields returned. It does not explicitly differentiate itself from sibling tools like edgar_facts_query or edgar_financials, leaving some ambiguity about which EDGAR tool to choose.

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 'Values of one XBRL concept' and the example body, but there is no explicit guidance on when to use this tool versus alternatives such as edgar_facts_query. Optional filters (form, unit) and the limit are mentioned but no exclusions or prerequisites 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?

No annotations, so the description carries the full load; it does add valuable non-schema behavior: newest-first ordering and a per-call price of $0.003 USDC via x402 on Base, which signals a paid metered endpoint. It says nothing about pagination limits beyond the schema, error/empty behavior, or what 'as filed' implies about restatements.

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 dense sentence that front-loads what is returned and closes with the payment model. The parenthetical field enumeration is long but earns its place since there is no output schema.

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?

Listing the returned fields compensates for the missing output schema, and the price disclosure covers the payment dimension. However, with six parameters and no annotations, the description still omits guidance on ticker vs CIK selection, limit behavior, and when this endpoint is the right choice.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so every parameter is already documented, including concept examples and unit filters. The description only echoes the form/unit filters and the output field list, adding no semantics beyond the schema; 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?

States a specific resource and verb: values of one XBRL concept as filed, with the exact returned fields enumerated. The opening 'Same as POST /facts with query strings' is a REST-ism that doesn't map cleanly to a sibling tool name, so differentiation from edgar_facts/edgar_financials is left partly implicit.

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?

Mentions optional form and unit filters but never says when to choose this over edgar_facts, edgar_financials, or edgar_filings. An agent gets no decision rule beyond the fact that a concept is required.

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 of behavioral disclosure. It usefully discloses the cost ($0.003 USDC per call) and payment mechanism (x402 on Base), which is valuable non-schema context, but it does not address rate limits, error behavior, or confirm read-only semantics explicitly.

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 followed by a short pricing note, with no wasted text. The key return fields and access method are presented efficiently.

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 the tool has no output schema, the description appropriately lists the main return fields, including form, dates, accession, document URL, 8-K item codes, company name, SIC industry, and fiscal year end. It is complete enough for an agent to understand the payload, though it could mention filtering by form type more explicitly.

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 all four parameters well. The description adds only that lookup can be done by ticker or CIK, which aligns with the schema but does not provide additional semantics for forms or limit beyond what is already structured.

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 as recent SEC filings and lists the concrete contents returned, including form type, dates, accession, document URL, and 8-K item codes. It is specific enough for an agent to recognize the tool's domain, but it does not explicitly distinguish itself from sibling tools such as edgar_financials or edgar_insiders.

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 says filings can be fetched by ticker or CIK, but it gives no explicit guidance on when to use this tool versus related sibling tools like edgar_financials or edgar_insiders. There are no stated exclusions, prerequisites, or alternative-selection rules.

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

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

With no annotations, the description carries the full burden, and it usefully discloses the cost and payment mechanism ($0.005 USDC per call, paid with x402 on Base) plus the data coverage window. It does not disclose latency/freshness or output structure, which are secondary for a read-only retrieval tool but still 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 definition is two tightly written sentences that front-load the data scope and metric list, then separately state the pricing. Every sentence earns its place, with no redundant or filler 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?

Given no output schema and no annotations, the description does a good job listing what data is returned and its time coverage. However, it does not clarify that at least one of ticker or cik must be supplied despite required parameters being zero, nor does it describe the return structure, leaving a meaningful gap 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?

Schema description coverage is 100%, and the schema already explains that ticker and cik are alternatives. The description adds no additional parameter semantics beyond what the schema provides, 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 resource (standardized financials from SEC XBRL), a precise time scope (last 5 fiscal years and 4 quarters), and enumerates the metrics returned. It implicitly distinguishes itself from edgar_filings and edgar_insiders by focusing on standardized financial statements rather than filings or insider data.

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 when-to-use or when-not-to-use guidance, and it does not mention the sibling EDGAR tools as alternatives. Usage is only implied by the data type, leaving the agent to infer that this tool should be chosen over edgar_filings when financial statement metrics 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 and does disclose key operational traits: a $0.005 USDC per-call price paid via x402 on Base, and a 25-filing parse cap. It does not cover error behavior, response ordering, or authentication beyond payment, but the critical cost and limit constraints are present.

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, front-loaded with the data scope and field list, followed by the payment requirement. It is dense but every clause earns its place by specifying the returned fields, transaction-code meanings, filing cap, and price.

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

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

There is no output schema, so the description must define the return shape; it does so thoroughly by listing owner, role, date, transaction codes, shares, price, shares owned after, and net open-market buys/sells in shares and USD. Combined with the cap and payment details, it is complete enough for an agent to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description maps 'last N days' to the days parameter and explains transaction codes, but adds no syntax or edge-case guidance for cik, ticker, or days 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?

States a specific verb and resource: insider transactions from Form 4 filings. It scopes the data to the last N days and up to 25 parsed filings, and lists the returned fields, making it clearly distinct from generic siblings like edgar_filings and edgar_financials.

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 explicit when-to-use or when-not guidance, and no named alternatives among edgar_filings or edgar_financials. Usage is implied by the insider-specific resource and the 25-filing cap, but the agent must infer that this is the right tool for insider Form 4 data rather than general filings.

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 load and does reasonably well: it discloses the payment model (paid per call, x402 on Base), the sort order, the tier set, and that prices are current. It omits what happens on missing/unpaid payment and any rate limits or data freshness caveats.

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: the core output description is front-loaded and the pricing note is appended last. Every clause carries information (chains, sort order, tiers, price, payment rail) 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 still conveys the return shape (USD cost per chain, sorted, three speed tiers) and the payment requirement. Only edge details such as failure behavior or data-freshness policy are missing, which is a minor gap for a read-only single-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the schema already documents the gas parameter's range (21000-30000000) and default (65000). The description only restates 'given gas units', adding no unit conventions or guidance on choosing values, so the baseline holds.

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: computes USD cost of a transaction across six named chains, sorted cheapest first, with slow/standard/fast tiers. This is clearly distinguishable from a single-chain gas lookup like gas_now without needing 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?

The description implies usage ('given gas units', the chain list, the tier breakdown) but never states when to pick this over sibling gas_now or other cost tools, nor any prerequisites. The one explicit contextual fact is the per-call price of $0.002 USDC paid via x402 on Base.

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, but the description carries real behavioral weight: it discloses the payment model ($0.001 USDC per call via x402 on Base), which an agent must know before invoking, and it clarifies that rollup quotes fold in the L1 data fee. It does not discuss failure modes for unsupported chains or data freshness, but core operational context is present.

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: the first front-loads output contents, the second front-loads pricing. Every clause (gwei units, rollup L1 fee inclusion, payment rail) supplies information an agent needs before calling.

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 read tool with no output schema and no annotations, the description covers what is returned and what it costs, which is the critical missing structure. Minor gaps remain around unsupported-chain behavior and whether values are a live snapshot or cached, but nothing essential to a correct call is absent.

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 chain parameter's schema description already lists the six accepted values (ethereum, base, arbitrum, optimism, polygon, bsc). The description adds only the framing of 'one EVM chain' and does not enrich the parameter beyond the schema, so 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?

States a specific resource ('current gas on one EVM chain') and enumerates exactly what is returned: base fee, slow/standard/fast priority fees in gwei, and USD costs for native, ERC-20 and Uniswap swap transactions including rollup L1 data fees. This clearly separates it from the sibling gas_compare (multi-chain comparison) 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?

The phrase 'on one EVM chain' implies this is the single-chain variant and that gas_compare handles the multi-chain case, but the description never names gas_compare or states an explicit when-to-use rule. The agent must infer the routing decision from the 'one EVM chain' scope 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 full behavioral burden. It usefully discloses decoding behavior, output fields, and pricing/payment via x402, but it does not cover rate limits, data retention, error behavior, or other operational details.

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

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

It is a single, front-loaded sentence that covers purpose, output, preprocessing, and price without wasted clauses. 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 two-parameter tool with no output schema, the description explains the return values and hidden-content decoding well. It is still missing how the context parameter should be chosen and any operational constraints beyond the schema.

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 both parameters. The description mentions the text being scanned but adds no specific meaning for the "context" parameter or how it alters scanning.

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 gives a specific verb and resource: "Classify text ... for prompt injection," and it names the exact outputs (verdict, score, findings). This is clearly distinct from the unrelated cloud-price, macro, and token-risk 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?

It establishes a clear usage context: text an AI agent is about to read. It does not state exclusions or explicitly compare against an alternative, so it falls short of the top score.

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 add real value by disclosing the paid model: '$0.002 USDC per call, paid with x402 on Base'. It still omits what happens on payment failure, whether a failed call is still charged, rate limits, and pagination behavior beyond the maxItems cap.

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: the capability and returned fields come first, with the payment cost as a compact trailing note. Dense but every clause carries information; the field list is slightly long but justifies itself as the only description of return values.

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

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

There is no output schema, so listing title, URL, domain, points, comment count, author, time, HN link and excerpt in the description is necessary and done well. For a six-parameter, zero-required, zero-annotation tool, the main remaining gap is operational detail around payment and error handling.

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 every one of the six parameters is already documented in the schema, including the mode/sort/tags enumerations and the 1-50 maxItems range. The description only restates the mode list and does not add format or interaction details beyond the schema, 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 names the resource (Hacker News stories), the two operating modes (front-page lists with the five list types, or full-text search), and the fields returned. It is specific about scope but never mentions the sibling tools hn_item and hn_user, so an agent must infer that per-item and per-user lookups live elsewhere.

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 through the mode enumeration and repeated 'Search only' markers, which tell an agent that q/days/sort/tags apply only in search mode. However, there is no explicit when-to-use or when-not-to-use statement, and no routing to hn_item or hn_user for single-story or user lookups.

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

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

With no annotations, the description carries the full burden and does disclose two things that materially affect invocation: comments are top-level only and capped at 30, text is truncated to 500 characters, and each call costs $0.002 USDC via x402 on Base. What it omits is failure behavior for an invalid id and whether truncated content or pagination is available beyond the stated caps.

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 zero waste: the identity and payload come first, the cost and payment rail second. Nothing is repeated from the title or schema.

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 read tool with no output schema and no annotations, the description covers return shape, truncation limits, and payment requirements, which is most of what an agent needs. Missing only edge-case behavior such as invalid ids or requesting more than 30 comments.

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 both parameters are already documented, and the description adds little beyond echoing the 30-comment cap. It does not clarify the string-typed maxComments despite it being a numeric range, nor what happens if maxComments exceeds 30.

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: fetch one Hacker News story or comment by id, with an enumerated set of returned fields (author, time, text, reply count) and an explicit comment cap. It is clearly distinguishable from hn_user and a listing-style hn_hackernews, but it never names those siblings to route the agent 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?

Usage is only implied: you supply an id and get a single item back, so the case for calling it is fairly self-evident. There is no explicit when-to-use/when-not or pointer to hn_hackernews for browsing or hn_user for author lookups, so the agent must infer which of the three HN tools applies.

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

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

With no annotations, the description carries the full burden, and it does disclose key behavior: the about text is HTML-stripped, only the 10 most recent submission ids come back (an implicit result cap), and every call costs $0.001 USDC via x402 on Base. It omits failure behavior, such as what happens for a nonexistent or non-case-matching username, and 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?

Two sentences, zero filler: the return contract is front-loaded and the pricing/payment sentence follows as separate, necessary context. Every clause earns its place, including the parenthetical about HTML stripping.

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

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

No output schema exists, so the description must define the return payload, and it does so field by field, including the truncation to 10 submission ids. The paid-call mechanism is also covered. Only edge-case behavior (invalid user, case mismatch, error responses) is left unspecified.

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 parameter already documents that the username is case-sensitive. The description adds no further coupling or formatting detail for the id, so it does not earn above the baseline that a fully documented schema establishes.

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 ("Hacker News user profile") and enumerates the fields returned: karma, creation date/age, about text, submission count and 10 recent submission ids. It is clearly distinct from hn_item and hn_hackernews by resource, though it never explicitly names the sibling it differs from.

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

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

There is no guidance on when to use this versus hn_item or hn_hackernews, and no prerequisites or exclusions are stated. The agent can infer it fetches a user profile from a username, but nothing is said about when a different tool would be the right call.

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 add real behavioral context: the tool is a paid call at $0.003 USDC via x402 on Base, which an agent must know before invoking. It stops short of describing failure modes, rate limits, or result shape.

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

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

Two dense sentences: the data payload is front-loaded and the cost/payment detail follows. Every clause conveys information an agent needs, 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 one-parameter tool with no output schema, the description does the necessary work by enumerating the returned metrics and the payment requirement. Only the concrete response format/units are left unspecified, which is a minor gap.

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% for the single 'q' parameter, so the schema already documents it as 'Category keywords.' The description adds no extra meaning about how to phrase or scope the category query, so 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?

States a specific verb+resource (market benchmark for an x402 category) and enumerates exactly what the output covers: supply, demand, price quartiles, top sellers. It is clearly distinct from siblings like intel_find or list_services, though it never names an alternative 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 enumerated metrics imply the use case (pricing/market analysis for an x402 category), but there is no explicit when-to-use or when-not-to-use guidance, and no routing versus siblings such as intel_find or list_services. 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 burden and does well: it discloses the $0.003 USDC per-call price, the x402/Base payment mechanism, ranking methodology (proven demand, 30-day unique payers, repeat calls, scanner filtering), and that results include the cheapest proven alternative. It does not explicitly state read-only behavior, but this is strongly implied by 'Find' and there is no hint of mutation.

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 tightly packed sentences: purpose first, return/ranking details second, payment information last. Every sentence adds value without 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 paid discovery tool with no annotations and no output schema, the description supplies the critical missing context: cost, payment chain, ranking methodology, and the nature of returned results. It could be slightly more complete by explicitly confirming the operation is read-only or describing the response shape, but overall it is sufficient to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already explains q, limit, and minPayers. The description adds useful context about what 'proven demand' and '30-day unique payers' mean, which indirectly clarifies minPayers, but it does not directly explain parameter usage 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 states a specific verb and resource ('Find the best x402 endpoints') and scopes it to 'a task'. It is clearly different from sibling tools like tokenrisk_check or guard_check, though it does not explicitly name alternatives such as intel_benchmark.

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 'for a task' and the ranking criteria, but there is no explicit when-to-use guidance or any statement about when not to use this versus siblings. An agent must infer the selection context.

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 so well: it discloses the $0.005 USDC per-call price and x402 payment on Base, the whole-Bazaar scope, one-per-seller cap, and evidence including a live unpaid 402 check. This is unusually complete context for an agent deciding whether and how to call it.

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 front-loads the core question and action, then packs in evidence details without repetition. The long middle clause is dense and could be more scannable, but each detail serves the selection decision.

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

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

No output schema exists, so the description must explain return values, and it does: 30-day unique payers, repeat calls, live price and latency, seller input example, reason codes, risk flags, and a cheaper alternative. Together with schema coverage and pricing disclosure, it is complete enough to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters are already documented. The description reiterates plain-words task input and 1-5 picks but adds little syntax or meaning for network and maxPrice, 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 opening question and phrase 'get the 1-5 best x402 APIs for it' state a specific verb and resource, and the evidence list makes clear it is a recommendation service rather than a generic search. It does not explicitly distinguish itself from siblings such as intel_find or list_services, so it falls short of a 5.

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

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

The description directly tells the agent to describe a task in plain words when asking 'Which API should I call?', giving clear context for use. It does not state when not to use this tool or name alternatives, so it is not a 5.

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

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

With no annotations, the description carries the burden and does disclose the key behavioral traits: it costs nothing ("Free"), it is scoped to this server only, and it enumerates paid tools only. It also discloses the pricing currency and network (USDC on Base), which is material context an agent needs before spending. It stops short of stating the response format or whether the catalog itself is included.

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 the cost qualifier front-loaded, followed immediately by scope and payload contents. No filler and nothing an agent must read twice.

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

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

No output schema exists, and the description compensates by naming the three things each entry contains (price, return shape, x402 endpoint). For a zero-parameter discovery tool this is nearly complete; only the response format and whether free tools appear are 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?

The tool takes zero parameters and the schema is an empty object with 100% coverage, so there is nothing to disambiguate; baseline 4 applies. The description correctly implies the tool is parameterless by describing an unconditional full listing.

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?

Specifies a clear verb ("Lists") and resource ("every paid tool on this server") plus the exact fields returned: price in USDC on Base, return value summary, and the equivalent HTTP x402 endpoint. This is unmistakably a catalog/discovery tool, cleanly distinct from the sibling action tools (cloudprice_*, intel_*, macro_*, guard_check, tokenrisk_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?

"Free." implies this should be called before paying for sibling tools, but the description never explicitly says to call it first or that alternatives exist. No exclusions or decision rules are stated, so the usage pattern is only implied.

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

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

With no annotations, the description carries the full burden and does meaningful work: it discloses data provenance (ECB and BAM fixings), that crosses may be computed rather than direct, and that each call costs $0.003 USDC via x402 on Base. It stops short of stating rate limits, error/refund behavior, or whether the fee applies to failed calls.

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 dense sentence front-loads the resource and scope, then tacks on computation and payment details. Everything earns its place, though the pricing clause shares a sentence with currency scope rather than being separated for scanability.

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 2-parameter, non-nested tool with no output schema, the description covers source, coverage, computation method, and cost. The one gap is return shape: it mentions 'method and date' in passing but does not tell the agent what fields come back for a multi-quote request.

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% (base and quote are documented, including the comma-separated multi-quote form), so baseline is 3. The description adds only the indirect hint that quote accepts multiple currencies via 'any pair' framing, which is weaker than the schema's own wording.

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?

Names a specific resource (official ECB and Bank Al-Maghrib reference exchange rates) and a precise scope (~50 currencies, named EM/MENA examples, direct or computed crosses). It never states a verb or distinguishes itself from the nearby sibling macro_policy_rates, so an agent must infer the boundary itself.

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

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

Usage is only implied by domain: an agent can deduce 'use this for FX conversion' but the description never says when to prefer it over macro_policy_rates or how to handle unsupported currency pairs. No exclusions or alternatives are given.

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

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

No annotations are provided, so the description carries the burden, and it does disclose the important non-obvious traits: the data source (BIS official), the exact coverage count, and crucially a per-call price of $0.003 USDC paid via x402 on Base, which tells the agent this call has a real monetary cost and a payment prerequisite. It does not state freshness/latency beyond the year-ago field or describe error/rate-limit behavior, keeping it short of a 5.

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 short sentences, front-loaded with what the data is, followed by filtering then cost. Every sentence adds something distinct and nothing is repeated.

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

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

There is no output schema, so the description must describe returns, and it does so explicitly (current rate, date of last change and size in bp, year-ago rate, central bank name) along with data source and scope. An agent has everything needed to decide and call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameter is already fully documented. The description's 'US, XM for euro area, MA, ZA' examples mostly restate the schema's 'XM = euro area' note, adding marginal value only as concrete example codes; 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?

A specific verb+resource statement: it names the exact dataset (BIS central bank policy rates), the coverage (39 economies), and enumerates fields returned. This is clearly distinguishable from sibling macro_fx, which covers exchange rates rather than policy rates, without needing to name it 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?

Usage is implied by the domain and the filter instructions ('Filter by ISO country codes... Omit for all 39'), but there is no explicit when-to-use/when-not or routing note versus siblings like macro_fx. An agent can infer usage from the resource itself, which is the minimum-viable level.

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

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

With no annotations, the description carries the full burden and does meaningful work: it discloses that this is a paid call ($0.002 USDC via x402 on Base), that the result is persisted server-side, and that it is signed. It stops short of disclosing idempotency (what happens if the same hash is stamped twice), failure/refund behavior on payment errors, 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?

Two tight sentences: the first gives purpose plus the full return-field set, the second gives cost and payment rail. Nothing is padded and the most decision-relevant facts (what it does, what it returns, what it costs) are front-loaded.

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 usefully enumerates the receipt fields and the verify URL, so an agent knows what comes back. Inputs are fully covered by the schema, and the payment requirement is stated. The gaps are edge-case behavior (duplicate stamping, payment failure) rather than anything blocking a correct 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% and the schema already specifies the 64-hex-char digest format, optional 0x prefix, and the label's 120-char limit and forbidden '|'. The description adds no parameter-level meaning beyond the schema, 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 opens with a precise verb+resource ('Timestamp a SHA-256 hash') and immediately enumerates the artifact produced (a stored, server-signed receipt with named fields). It never names its closest sibling notary_time, so the boundary between 'stamp this hash' and 'get current time' is left implicit rather than stated.

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 purpose implies when to call it (you need proof a hash existed at a moment in time), and the price line adds a real precondition. However there is no explicit 'use this instead of notary_time when...' routing and no statement about when not to use it or how to verify an existing receipt.

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 well: it discloses the commercial cost ($0.001 USDC per call), the payment rail (x402 on Base), and that the response includes the signed message and signature so the caller can verify independently. It does not cover failure modes, but the cost/auth profile an agent must know before calling is present.

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 that front-loads the payload (current UTC time, signed) and ends with the payment terms as the last thing an agent reads before acting. No padding.

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 and no annotations, yet the description names the return contents (ISO and unix ms, signed message, signature), which is what an agent needs to plan verification. The only missing piece is how this differs from notary_stamp, which would complete the picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. Schema coverage is 100% and additionalProperties is false, so no hidden inputs exist.

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

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

States a specific resource and verb: returns current UTC time in ISO and unix ms, signed by the genexxis notary key. An agent knows exactly what the tool produces. It never mentions the sibling notary_stamp, so the boundary between stamping arbitrary payloads and fetching a signed timestamp is left to inference.

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 statement of when to call this versus notary_stamp or why one would need a notarized timestamp. Usage is only implied by the phrase 'for independent verification'. The payment note tells the agent what it costs but not when the tool is the right choice.

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 burden well by disclosing the paid nature of the call ($0.003 USDC per call via x402 on Base) and the data returned, including 24-hour hourly price history. It does not cover error behavior, rate limits, or freshness guarantees, but it provides substantial behavioral context for a read-only data endpoint.

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 tightly written sentences with no wasted language. It front-loads the core purpose and then adds the pricing detail, so every sentence 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 two-parameter data retrieval tool with no output schema, the description adequately lists the returned fields and pricing/payment requirements. It could be slightly more complete by clarifying how to obtain the required id or what happens on invalid venue/id, but the essential invocation context is 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%, so the input schema already fully documents venue and id. The description mentions Polymarket and Kalshi but adds no parameter semantics beyond what the schema provides, making the baseline score of 3 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 states a specific resource and scope: one Polymarket or Kalshi market in detail, including the exact market data fields returned. It is clear what the tool does, though it does not explicitly distinguish itself from siblings like odds_search or odds_top.

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 ... market in detail' implies usage for retrieving a known market rather than searching or ranking markets, but the description gives no explicit when-to-use guidance or sibling alternatives. It also does not mention that odds_search may be needed to discover the required id.

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 at all, the description carries the full burden and does well: it discloses the exact return payload (outcomes, implied probabilities, 24h volume, liquidity/open interest, end date, link) and the ranking rule ('match then volume'). It also uniquely discloses a cost and payment mechanism — $0.003 USDC per call via x402 on Base — which is behavior an agent must know before calling. Missing rate limits, error behavior, and pagination semantics keep it short of a 5.

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: the capability and return shape first, the price second — front-loaded correctly with no filler. The first sentence is dense with a comma-list of returned fields, but each item supplies information the agent would otherwise lack given there is no output schema.

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

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

There is no output schema, so the description must describe returns, and it does so thoroughly, including ranking and per-market fields. Payment terms are also covered. The remaining gap is operational detail — what happens on no matches, result pagination past 'limit', and failure modes — which is minor for a two-parameter read tool.

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 including a range and default, so the schema already does the heavy lifting. The description adds only the notion of keyword matching and ranking order, not any syntax, quoting, or limit 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?

States a specific verb and resource ('Search open Polymarket and Kalshi markets by keywords') with the scope narrowed to open markets on two named venues, which separates it from a single-market lookup like odds_market. It never names the sibling tools (odds_market, odds_top) or states the boundary explicitly, so the differentiation is inferable rather than stated.

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

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

Usage is only implied: the agent can infer keyword search is the entry point when it does not have a market identifier. There is no explicit 'use this when… / use odds_top instead when…' routing, and no guidance on query construction beyond the schema example.

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 and does notable work: it discloses paid access ($0.002 USDC per call via x402 on Base), the upstream data sources, the ranking metric, and the shape of returned data (implied probabilities, links). It does not cover rate limits, freshness/caching, or failure modes, keeping it below a 5.

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 waste: the capability statement is front-loaded and the pricing/access constraint follows. Every clause carries information an agent needs.

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

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

For a no-annotation, no-output-schema, single-parameter tool, the description supplies sources, ranking, returned fields, and cost. Minor omissions (caching window, error behavior when payment fails) are the only 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 100% and the single 'limit' parameter is fully documented in the schema (1-50, default 10). The description adds nothing about the parameter, so 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?

States a specific resource and scope: most traded open prediction markets across Polymarket and Kalshi, ranked by 24h volume, with implied probabilities and links. The 'top/ranked' framing distinguishes it in spirit from odds_search and odds_market, but neither sibling is named 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?

Usage is implied by 'most traded ... right now' — an agent can infer this is a discovery/browse tool rather than a lookup. There is no explicit statement of when to prefer it over odds_search or odds_market, and no exclusions or prerequisites beyond the payment note.

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, so the description carries the full burden. It does disclose cost and payment mechanism ($0.003 USDC per call via x402 on Base), which is genuine behavioral context an agent needs before calling, plus the default OI filter. But it says nothing about output ordering, freshness of rates, 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?

A single front-loaded sentence names the resource, scope, and filter, followed by one short pricing sentence. Nothing is redundant, though the pricing clause reads as appended rather than integrated.

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 2-parameter read tool with no annotations and no output schema, the definition covers inputs and cost but not return shape: an agent cannot tell whether rows include symbol, venue, rate, and OI, or how the two 'sides' are returned (the limit description's 'rows per side' is the only hint).

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 (limit, minOiUsd) are already documented in the schema; baseline 3 applies. The description restates the $5M default and clarifies that open interest is expressed in USD, but adds no format or boundary 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?

States a specific resource and scope: highest/lowest annualized funding rates across Binance USDT-margined perpetuals and Hyperliquid perps, with open interest. That scope naturally separates it from the perps_funding and perps_oi siblings, but neither is named explicitly, so an agent must infer the routing.

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 'highest and lowest' framing implies a screener/ranking use case, but there is no explicit when-to-use guidance, no exclusions, and no pointer to perps_funding (single-asset funding) or perps_oi when those would be more appropriate.

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

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

With no annotations, the description carries the full burden, and it does disclose the important operational trait that the call costs $0.003 USDC via x402 on Base — genuine context the schema cannot convey. It omits other behavioral facts an agent would want: data freshness/latency, read-only nature, and error behavior for unsupported symbols or venues.

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

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

Front-loaded with the core resource and venue list, then the payload contents, then the pricing note. The long enumerations of return fields are dense but each item is a distinct data point; only mild trimming is possible.

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

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

There is no output schema, so the description correctly takes on the job of enumerating return values (raw and annualized rate, next funding time, mark/index price, basis %, OI in contracts and USD, spread, payer side) — this is thorough. What remains missing is freshness/timing context and any note on partial venue coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the schema itself explains the ticker-to-symbol mapping (BTC → BTCUSDT, BTC-USDT-SWAP, BTC). The description adds no parameter-level meaning beyond referring to 'a coin', 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 names a specific resource (funding rate of a coin's perpetual) and enumerates the four venues and the exact metrics returned, which is far beyond a restatement of the name. It does not, however, explicitly distinguish itself from close siblings like perps_oi or perps_extremes, even though open interest and cross-venue spread overlap with them.

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

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

Usage is only implied: an agent can infer this is the tool for per-coin funding rates, but there is no statement of when to prefer it over perps_oi (open interest focus) or perps_extremes (extremes screening). No exclusions or prerequisites are given, and the required payment step is not framed as a precondition to consider before calling.

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

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

With no annotations, the description carries the full burden and does well: it discloses the venue coverage, the limits of 24h history (only Binance and Bybit), and the commercial model ($0.003 USDC per call via x402 on Base), which is critical pre-invocation context an agent would otherwise not know. It stops short of error behavior or rate limits, so not a 5.

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 dense sentence that front-loads the resource and appends cost last, with zero filler. It is slightly overloaded with parenthetical venue lists, but every clause carries information.

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

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

For a one-parameter tool with no output schema and no annotations, the description covers what is returned, which venues, where history is missing, and the payment requirement. Only invocation-failure and pagination/format details are absent, which is a minor gap at this complexity.

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?

Only one parameter and schema coverage is 100%, with the schema already supplying the 'BTC, ETH, SOL' example. The description adds nothing about symbol format (case sensitivity, perp-specific suffixes, quote asset) beyond what the schema states, so 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?

States a specific resource (open interest of a coin's perpetual) and enumerates the venues plus the exact metrics returned (contracts, USD, market share, 24h change, long/short ratio). An agent can distinguish this from perps_funding or perps_extremes by the data returned, though the description never names those siblings 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?

There is no when-to-use guidance and no alternatives are named, even though perps_funding and perps_extremes sit in the same family. The only conditional statement — '24h change where history exists (Binance, Bybit)' — is a data-availability caveat, not usage direction.

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. It usefully discloses a paid x402 endpoint at $0.001 USDC per call on Base and the Binance spot data source, but it does not state refresh cadence, latency, error behavior, or whether the data is a snapshot or streaming.

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-loads the core operation before the payment details. Every clause adds useful information: source, filter, returned fields, breadth, 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 two-parameter read tool with no output schema, the description covers the key return fields, the data source, the filtering rule, and the payment mechanism. It is nearly complete, with only refresh cadence and error/rate-limit 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 both parameters are already well documented. The description reinforces the default minVolumeUsd behavior but does not explain the limit parameter or add syntax beyond the schema, making the baseline 3 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 states a specific resource and operation: top 24h gainers and losers among Binance spot USDT pairs, with a minimum-volume filter. This is far more specific than a generic price tool, though it does not explicitly name how it differs from siblings like prices_ohlcv or perps_extremes.

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 Binance-spot-USDT and min-volume framing implies when the tool is appropriate, but there is no explicit guidance on when to use it versus prices_ohlcv, prices_token_price, or perps_extremes. The default volume threshold is helpful context, but alternatives are 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?

With no annotations, the description carries the full burden and does well: it discloses the metered price ($0.002 USDC per call), the payment mechanism (x402 on Base) which implies wallet/auth prerequisites, and the data provenance with a fallback source. It omits error behavior for unsupported symbols/intervals and any rate-limit or caching 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?

Two tight sentences: the first front-loads capability, fields, and sourcing; the second front-loads the cost and payment requirement. Nothing is wasted and no sentence merely restates 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 3-parameter tool with no annotations and no output schema, the description covers data source, field-level return contents, granularity, and payment cost, which is most of what an agent needs. The remaining gap is failure modes (unknown ticker, unavailable exchange) and the exact response shape beyond field names.

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, but the description adds real meaning beyond the schema by establishing that symbol is the base asset quoted against USDT and summarizing the interval range and candle cap. It does not clarify the oddity that limit is typed as a string or how invalid values are rejected.

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