mcp-livedatapro

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 the categories of records returned and the word 'Live' implies real-time freshness, which is useful behavioral context. However, it omits permissions/authentication needs, rate limits, latency, geographic or jurisdictional coverage, and how many records or what form results take.

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 tight sentence with no filler, and the core subject (bankruptcy/insolvency filings) is front-loaded. The lack of an explicit verb makes it a fragment, which is slightly less immediately actionable than a full directive sentence.

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

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

For a single-parameter, read-only lookup with full schema coverage and no output schema, the description covers the data scope adequately. It still leaves open what a returned record contains, jurisdiction and historical-range limits, and whether results are point-in-time or updated continuously, which would matter to an agent assessing insolvency risk.

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 company_query is fully documented in the schema as accepting a company name, tax ID, or registration number. The description adds no further syntax, format, or matching-behavior 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 names a specific domain of data: bankruptcy filings (Chapter 11/7), liquidation notices, receiverships, and winding-up petitions tied to a company query. An agent can distinguish this from data siblings like getCorporateJobPostings or getCentralBankRates. It does not, however, explicitly differentiate itself from close company-domain siblings such as verifyCompanyRegistryVat or getCorporateDirectorsUbo, which an agent might reasonably confuse it with.

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

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

There is no guidance on when to select this tool versus alternatives, no prerequisites, and no named sibling for routing. The description only states what data exists, leaving the agent to infer the use case (e.g., assessing counterparty risk) on its own.

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 adds real context by naming the data source (dark-web breaches) and the k-anonymity prefix mechanism for password checks, but says nothing about read-only safety, auth requirements, rate limits, or what a match result looks like.

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

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

A single front-loaded sentence with all inputs enumerated and zero filler. Every clause earns its place.

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

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

For a simple one-parameter lookup with no output schema or annotations, the description covers purpose and accepted inputs adequately. The only gap is that it does not describe the shape of a match/non-match result, which an agent must infer.

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

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

Schema coverage is 100%, so the schema already documents the single parameter. The description adds a marginal clarifying framing (k-anonymity) over the schema's '5-char SHA-1 password hash prefix', which justifies keeping the baseline rather than dropping below 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?

The description states a specific verb (check) and resource (appears in known dark-web data breaches), plus the three accepted input forms. No sibling tool covers breach data, so this is unambiguous without opening 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 purpose makes the use case fairly self-evident, but there is no explicit when-to-use statement, no prerequisites, and no mention of alternatives. Usage is only implied by the tool's function.

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

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

With no annotations provided, the description carries the full disclosure burden. It does contribute two behavioral facts not present in the schema: the data is 'real-time' and quantities are 'exact' (i.e. per-warehouse), but it is silent on supported-store limitations, failure behavior for unsupported URLs, and rate or auth constraints.

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

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

A single tight sentence with the highest-value attribute ('real-time') front-loaded and no filler. It is a noun phrase rather than an action statement, which slightly weakens the lead, 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 one-parameter read-only lookup with no output schema, the description covers the main return fields but omits what the response shape looks like, which retailers are supported, and what happens when a product is unavailable. Adequate but with clear gaps.

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

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

Schema coverage is 100% and the single parameter already documents that it accepts a product URL (Shopify, WooCommerce, Amazon) or SKU. The description adds no format, normalization, or example detail beyond that, so this is the baseline case where 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?

The description names the specific resource and the exact data returned: real-time stock levels, warehouse quantities, and backorder status. The scoping phrase 'for e-commerce stores' separates it from getEcommercePricing and getProductReviewsSentiment, 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?

There is no when-to-use guidance, no statement of prerequisites, and no routing to or away from siblings such as getEcommercePricing. The agent must infer the use case from the field list 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 burden. It usefully discloses the returned data categories and that pricing is real-time, implying a read-only lookup, but it omits whether results cover all models or one, whether an API key is required, and any rate-limit or pagination 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?

One dense sentence, front-loaded with the resource and the return payload, with zero filler. Every clause earns its place.

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

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

For a zero-required-param read tool with no output schema, enumerating the returned fields (pricing, context limits, TTFT, benchmarks) gives the agent enough to call it correctly. Only the task_category value space and result-shape remain 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 the single task_category parameter is already documented by the schema. The description adds no meaning about accepted values or how the filter behaves, which is the expected baseline when 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+resource (AI model pricing) and enumerates exactly what is returned: token pricing, context limits, latency (TTFT), and benchmark scores. It also names the covered vendors (OpenAI, Anthropic, Gemini, open-weights), which no sibling tool touches, so an agent can route to it unambiguously.

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 returns but never when to use it versus alternatives such as getCloudServerPricing, nor does it state exclusions or prerequisites. 'Real-time' implies fresh data but no usage context is 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 full behavioral burden. It mentions that data is 'live' but does not disclose authentication needs, rate limits, whether estimates are approximate, data freshness, or response behavior for each platform.

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

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

The description is a single, front-loaded sentence with no filler. Every listed item contributes to understanding the tool's data scope.

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

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

With no annotations and no output schema, the description should be more complete about return values and limitations. It broadly lists data categories, which is minimally adequate, but it does not explain output structure, estimate accuracy, or platform-specific differences.

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 documents both app_identifier and platform with examples. The description adds no parameter syntax or format guidance beyond what the schema already provides, 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 specific resources: iOS/Google Play rankings, download/revenue estimates, version history, and user reviews. It clearly states what the tool retrieves for a given app, though it does not explicitly distinguish itself from sibling tools like getProductReviewsSentiment or getSoftwareReleases.

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

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

The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. An agent must infer usage from the resource list 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?

No annotations are provided, so the description carries the full behavioral burden. It lists data categories but discloses nothing about freshness/update cadence, source authority, permissions, or whether results are a snapshot versus historical series — meaningful gaps for a data-retrieval tool.

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

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

A single, front-loaded noun phrase with zero filler. It is slightly fragmentary (no explicit verb), but every word earns its place.

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

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

For a one-parameter read tool with full schema coverage and no output schema, the description does the important work of enumerating the return payload (rates, QT status, calendar). Nothing critical is missing, though freshness and update timing would round it out.

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 (central_bank) is documented with examples (FED, ECB, BOE, BOJ, SNB) in the schema itself. The description adds no parameter meaning beyond that, 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 resource set — official central bank policy interest rates, quantitative tightening status, and the rate decision calendar — so an agent knows exactly what data comes back. It does not, however, explicitly distinguish itself from close siblings like getFxExchangeRates or getConsumerLoanRates, which also deal in 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?

There is no when-to-use, when-not-to-use, or alternative-routing guidance. With many rate-related siblings (getFxExchangeRates, getConsumerLoanRates, getStockEodPrices), the agent gets no help choosing between them.

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 discloses that prices are 'live' and covers hourly/monthly and spot/on-demand models, but it does not state authentication needs, rate limits, data freshness guarantees, or whether results are cached.

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

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

The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the comparison scope, pricing types, and provider coverage.

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 covers the data domain and providers, but with no output schema it does not describe the return format, currency, or result structure. It also omits usage guidance and behavioral details that an agent might need 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 schema already fully documents min_vcpu, min_ram_gb, and gpu_required. The description adds only a loose example ('H100/A100') that aligns with the GPU filter but does not add meaning beyond the schema.

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

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

The description states a specific verb ('Compare') and resource ('live hourly and monthly VPS, dedicated CPU, and GPU server spot/on-demand prices') with named providers. It clearly distinguishes this tool from siblings like getAiModelPricing or getCommodityPrices by focusing on cloud server pricing.

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 for comparing cloud server prices but does not explicitly say when to use this tool versus alternatives or when not to use it. It gives implicit context without any exclusions or sibling routing.

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

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

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about freshness, source, rate limits, or what happens with an unknown postcode. It only enumerates data categories, which is descriptive rather than behavioral.

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 tight sentence with no filler, and the data categories are front-loaded. It is a fragment rather than a full statement, but nothing in it 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 two-parameter read tool this covers the returned data categories reasonably well, including units (per sqm). However, with no annotations and no output schema, the absence of geography scope, currency, and freshness context leaves real 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%, so both parameters are already documented, giving a baseline of 3. The description does add value by enumerating the asset classes (retail, office, industrial), partially compensating for the enum-less property_type field, but gives no format or granularity detail for city_or_postcode.

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 (commercial retail/office/industrial space data with NNN rates and vacancy) but is a bare noun phrase with no verb, so the action is only implied. It also does not distinguish itself from the sibling getRealEstateStats, leaving the agent to guess which one covers commercial property.

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, no mention of geographic coverage limits, and no pointer to alternatives such as getRealEstateStats or getParcelZoningInfo. The agent must infer usage purely from the field 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 full behavioral burden. It usefully discloses that prices are 'live' and cover both 'spot and futures', which is genuine behavioral context, but it says nothing about auth requirements, rate limits, or update frequency beyond 'live'.

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

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

A single efficient sentence with the scope terms front-loaded. Nothing is wasted, though it is arguably so terse that it omits useful routing detail.

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

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

For a one-parameter read tool this is close to adequate, but with no annotations and no output schema, the description should indicate return shape, units, or currency. That gap leaves the agent guessing at what a successful call yields.

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 'commodity' parameter already documents accepted name/ticker forms with examples. The description adds no further syntax or format guidance, so the schema does the heavy lifting — baseline 3.

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 ('get') plus resource ('CommodityPrices') and enumerates the covered categories (industrial metals, lumber, energy, agricultural raw materials). This scopes it well against price siblings like getFuelPrices and getStockEodPrices, but it never explicitly names those alternatives to draw the boundary.

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 category list implicitly signals when this tool applies versus fuel or equity price tools, but there is no explicit when-to-use statement, no exclusion, and no alternative named. Usage must be inferred.

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

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: no data freshness, no geographic coverage limits, no indication of whether rates are APRs or nominal, no auth or rate-limit notes. For a financial-data lookup where units and vintage materially affect correctness, these omissions matter.

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 with no filler; the scope and grouping dimensions come first. It is efficient, though it spends words on a grouping dimension the tool cannot actually filter by.

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 must explain what comes back, and it does not — no units, no period, no granularity, no mention of the country/loan_type keys in the response. Combined with the missing 'credit tier' parameter and undefined loan_type values, an agent cannot reliably form 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%, so baseline would be 3, but the description actively introduces a dimension with no corresponding parameter ('by ... credit tier') and lists 'commercial mortgage', which conflicts with the consumer framing of the tool name, leaving the valid values of loan_type undefined (no enum, description only says 'Loan category').

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 specific resource (average interest rates) and the loan categories covered (mortgage, auto, personal credit), which separates it from siblings like getCentralBankRates or getFxExchangeRates. It is a noun phrase rather than a verb+resource statement, but the intent is unambiguous. No explicit contrast with the nearest sibling (getCentralBankRates) is offered.

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, no statement of prerequisites, and no mention of an alternative tool. An agent must infer that this is the tool for consumer lending rates versus central bank policy rates purely from the name and description.

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

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only states what data is returned (spot rates for container sizes). It omits operational details such as data freshness, source, authentication, rate limits, 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?

The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core resource and scope without unnecessary elaboration.

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

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

For a simple two-parameter lookup with no output schema, the description tells what rates are for which containers and lanes, but it omits return value details (e.g., currency, validity period) and usage context. It is minimally viable but leaves clear gaps.

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

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

Schema description coverage is 100%, so the two required parameters (origin_port, destination_port) are already fully documented in the schema. The description adds no additional syntax, format, or validation details beyond what the schema provides, warranting the baseline 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 resource (spot ocean freight rates) and scope (20ft/40ft containers, global trade lanes), which clearly distinguishes it from sibling tools like getCourierShippingRates and getPortCongestion. An agent can identify what it returns without opening 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 gives no explicit when-to-use or when-not-to-use guidance, nor does it name alternatives. It implies the context of ocean freight pricing but leaves the agent to infer whether to use this tool versus 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 the full behavioral burden. It discloses one useful trait, that only 'active' officers are returned, but says nothing about authorization requirements, data freshness, jurisdictional coverage, rate limits, or how disqualified flags are resolved.

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

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

A single compact sentence that front-loads the verb and immediately enumerates the returned data categories. No filler or redundancy, though it is arguably under-developed rather than optimized for the tool's complexity.

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

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

For a simple two-parameter lookup with full schema coverage and no output schema, the description covers the core purpose adequately. However, with zero annotations it leaves the entire behavioral profile (auth, freshness, jurisdiction defaults) undocumented.

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 jurisdiction and company_id_or_name fully documented in the schema, so the baseline of 3 applies. The description adds no syntax, format, or dependency information beyond what the schema already supplies.

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 ('Retrieve') plus a well-defined resource and an enumeration of the entity classes returned (directors, C-suite officers, UBOs, disqualified flags). It is clearly distinguishable from sibling lookups like verifyCompanyRegistryVat or checkCorporateInsolvency, though it does not call out those differences 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 description states what is returned but gives no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusion criteria. An agent must infer its place among the ~50 sibling verification/lookup tools on its own.

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 discloses data provenance ('Live', 'across global job boards') and the fields returned, but says nothing about auth requirements, rate limits, pagination, or coverage limits. For a network data-fetch tool this is a significant gap.

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

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

A single front-loaded sentence with no filler; the content facets are listed compactly. It is appropriately sized for the tool, though it could have used the space to route to alternatives.

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 usefully enumerates the returned data (openings, hiring velocity, tech stack mentions, salary ranges). But with no annotations and no output schema, it omits freshness guarantees, result shape, and alternative-selection guidance, leaving real gaps for a live-data 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 both parameters carry examples ('Anthropic, Senior Rust Engineer'; 'City, country, or remote'). The description adds no parameter-level syntax or defaults beyond what the schema already documents, 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 the resource and its data facets concretely: 'Live corporate job openings, hiring velocity, tech stack mentions, and disclosed salary ranges across global job boards.' An agent can tell this fetches job-posting data. However it is a noun phrase with no verb, and it does not distinguish itself from the closely related sibling getRegionalSalaryBenchmarks.

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

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

There is no statement of when to use this tool versus alternatives, no prerequisites, and no exclusions. The word 'Live' implies freshness but nothing tells the agent when this is the right call versus getRegionalSalaryBenchmarks or verifyCompanyRegistryVat.

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 signals that rates are 'live' and comparative, but says nothing about authentication requirements, rate limits, caching, error behavior, or return format beyond rates and ETAs.

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 with no filler. It communicates the operation, the output objects, and the carriers in one pass.

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 low-complexity, three-parameter read tool with full schema coverage and no output schema, the description is minimally adequate. It states what is compared and returned, but lacks any usage context or behavioral caveats that would help an agent choose it over related sibling tools.

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 three parameters (weight, origin, destination). The description adds no parameter semantics beyond what the schema provides, making the baseline 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?

States a specific verb and resource: 'Compare live express parcel shipping rates and delivery ETAs across DHL, UPS, FedEx, and DPD.' The scope ('express parcel') and named carriers distinguish it from siblings like getContainerFreightRates and getParcelZoningInfo without opening either 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 gives no when-to-use guidance, exclusions, or alternatives. It implies the tool compares rates, but does not say when to choose it over sibling tools such as getContainerFreightRates or lookupHsTariffDuty.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It states 'Real-time' (data freshness) and implies read-only access, but it omits critical details such as rate limits, authentication requirements, error handling, and output structure. This is a significant gap for a tool with zero annotation coverage.

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

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

The description is a single, front-loaded sentence that efficiently lists the key data points without filler. However, it is a sentence fragment rather than a complete statement, and it could be slightly more structured to aid 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?

Given the tool's simplicity (2 parameters, no output schema, no annotations), the description gives a reasonable overview of what data is returned. Still, it lacks detail on output format, error conditions, and data granularity, which an agent might need to interpret results 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 both parameters (blockchain and token_address_or_symbol). The description adds no additional meaning about parameters, focusing instead on return values. Baseline 3 is appropriate when 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?

The description specifies the resource (DEX token liquidity pools) and the data attributes (trading volume, pool reserves, smart contract audit safety). It is clear but lacks an explicit verb like 'retrieves' or 'returns', and there is no sibling tool for direct comparison, 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 offers no guidance on when or why to use this tool versus alternatives. It does not mention prerequisites, typical use cases, or exclusions, leaving the agent to infer usage solely from the tool name and schema.

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, yet it discloses almost nothing beyond the returned fields. It does not state whether data is real-time or delayed, how far ahead 'upcoming' extends, whether auth or rate limits apply, or whether coverage varies by market. The single word 'upcoming' is the only temporal hint.

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 tight sentence that front-loads the most distinctive field (ex-dividend dates) and contains no filler or repetition. Every clause maps to actual returned data.

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

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

For a one-parameter, read-only data lookup with no output schema, the description gives a reasonable field inventory. However, it omits the temporal window for 'upcoming', data freshness, and any market-coverage caveats, leaving gaps an agent might care about when deciding between this and related market-data tools.

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 parameter has 100% schema description coverage, including format examples (AAPL, SP500, FTSE100), so the schema already does the work. The description adds no format or syntax detail beyond it, which matches the baseline 3 for high-coverage schemas.

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 exact data it returns (ex-dividend dates, payment dates, yield percentages, payout ratios) scoped to global stocks, so an agent knows this is a dividend-specific feed rather than a price feed. It is a noun-phrase list rather than a verb+resource, and it does not explicitly distinguish itself from siblings like getStockEodPrices or getSecEarningsEps. Still clear enough to select correctly.

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, no mention of alternatives, and no conditions or prerequisites. The agent must infer from the tool name alone that this is the dividend-calendar endpoint.

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 it only contributes the word 'real-time'. It says nothing about rate limits, authentication, whether the lookup is read-only (implied but not stated), or the common reality that WHOIS output is heavily redacted post-GDPR.

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 with the verb, resource, and return categories, and no filler. Every clause earns its place.

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

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

For a one-parameter lookup with no annotations and no output schema, the description does list the main return fields, partially compensating for the missing output schema. However, it omits operational caveats an agent would want (redaction, rate limits, failure when no record exists).

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 there is a single required 'domain' parameter with an example in the schema, so the schema already does the work. The description adds no format, TLD, or normalization guidance beyond what the schema provides, 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?

States a specific verb (lookup) and resource (WHOIS/RDAP for a domain) and enumerates the returned data categories, so the agent knows exactly what it retrieves. It does not distinguish itself from the sibling searchExpiredDomains, which is the nearest neighbor in intent.

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

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

There is no when-to-use guidance, no mention of when to prefer this over searchExpiredDomains or other domain-related siblings, and no prerequisites stated. The agent must infer usage purely from the name and purpose line.

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, yet it discloses only that data is 'Live'. Nothing is said about rate limits, cost/credits, authentication, latency, or whether the 90-day history is aggregated vs. per-point. For a cross-marketplace scraping-style pricing feed, this is a significant gap.

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

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

A single compact sentence with no padding, front-loading the marketplaces and the headline data. It is efficient, though its comma-list structure reads more like a field inventory than a front-loaded purpose statement.

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 return fields, which partially compensates. However, with no annotations it omits any behavioral or operational context (cost, limits, auth), leaving the definition adequate but incomplete for a two-param external data feed.

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

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

Schema description coverage is 100% for both parameters (accepted identifier formats, ISO country code), so the schema does the heavy lifting. The description adds no parameter-level detail beyond what is already in the schema, making the baseline 3 correct.

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

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

The description names the specific resource and scope: live product pricing across Amazon, eBay, and Walmart, plus the derived fields (BuyBox winner, seller count, 90-day history). No verb is stated, so the action must be inferred from the name, but the purpose is unambiguous. It implicitly differentiates from siblings like checkProductInventory and getProductReviewsSentiment by claiming pricing data rather than stock or sentiment.

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

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

There is no statement of when to use this tool versus alternatives (e.g., checkProductInventory, getProductReviewsSentiment), no prerequisites, and no exclusions. The description is purely a data listing and leaves all routing decisions to inference.

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

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

No annotations are provided, so the description carries the full burden. It discloses fuel types and the word 'Live' implies real-time data, but says nothing about coverage regions, auth needs, rate limits, or how results are shaped.

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 with the resource first and the location scoping last. No filler or redundancy, though it is short enough that it could carry a little more without becoming bloated.

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 covers what data is returned and the input mechanism. It leaves gaps on freshness semantics, geographic coverage, and result structure that a no-annotation tool arguably should address.

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

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

Schema description coverage is 100%, so both parameters are already documented in the schema (country_code ISO format, postcode/ZIP/city). The description only restates the location inputs, adding no syntax or format detail 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?

The description gives a specific verb (get), resource (petrol, diesel, LPG, EV fast-charging prices), and scope (stations by postcode or city). It clearly differentiates from sibling pricing tools like getCommodityPrices or getCloudServerPricing, though it never names an alternative directly.

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

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

It states the input mechanism (postcode or city) but gives no explicit when-to-use or when-not-to-use guidance and no alternatives. An agent can infer it is for local fuel prices, but nothing routes it against the other get*Pricing siblings.

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

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

With no annotations, the description carries the full burden, and it does disclose the data profile (real-time, mid-market spot, bid/ask, volatility) plus a real scope constraint (fiat only). However, it says nothing about auth requirements, rate limits, daily vs. intraday granularity, or error behavior for unsupported pairs.

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 no filler, and the most important information (what the rates are and for which asset class) is front-loaded. It is a noun-phrase fragment rather than a verb-led statement, which slightly weakens actionability.

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, the description adequately conveys the returned data even without an output schema, and the schema fully covers inputs. Gaps remain around call semantics (auth, rate limits, refresh cadence) that an agent would otherwise get from annotations, which are 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 description coverage is 100% – both base_currency and target_currency are documented with ISO code format and examples. The description adds no parameter-level meaning beyond that, 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 (foreign exchange mid-market spot rates) and enumerates exactly what is returned: spot rates, bid/ask spreads, and daily volatility. The 'fiat currency pairs' qualifier usefully separates it from crypto (getCryptoDexLiquidity) and policy-rate siblings (getCentralBankRates), though it never names those alternatives 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 statement, no prerequisites, and no mention of sibling tools or the conditions that would route an agent here versus getCentralBankRates or getCryptoDexLiquidity. Usage is only implied by the subject matter.

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. 'Live' usefully signals real-time data, but nothing is said about currency of rates, geographic coverage limits, rate limits, or that the tool returns availability rather than performing a booking.

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

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

A single compact sentence with the core resource front-loaded and zero filler. It reads as a content list rather than a directive, 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?

No output schema exists, so the description must convey return shape; it partially does by naming availability, nightly rates, and shuttle info. It omits pricing currency, response fields, and whether booking is possible, leaving real gaps for a data-retrieval 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%: the schema already documents the date format and the accepted location_query forms (IATA, city, postal). The description adds no parameter-level meaning beyond that, 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 resource (hotel and short-term rental room availability) and the data it returns (nightly rates, airport shuttle info), so an agent can tell what it does without opening the schema. It does not reference any sibling, but no sibling in the list covers lodging availability, so ambiguity is low.

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

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

There is no statement of when to use this tool, when not to, or which alternative covers a related need. The description is purely declarative about content and leaves routing entirely to inference.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what data is returned (METAR/TAF, 48-hour forecast, wind, visibility, precipitation risk), which is useful, but it omits any operational context such as authentication, rate limits, data freshness, coverage, or whether the result is current versus forecast. For a tool without annotations, this is a significant gap.

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

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

The description is a single efficient sentence-like fragment with no wasted words. It front-loads the core data types and is appropriately sized for a one-parameter lookup tool.

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

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

The tool is simple and the description covers the main data categories, but with no output schema and no annotations, the description should do more to explain return format, time zone or units, and usage context. It is minimally viable but leaves clear gaps for an agent invoking the 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 single location parameter is fully documented in the schema (city name, coordinates, or ICAO/IATA code). The description adds no additional meaning about the parameter, 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 specific data the tool returns: aviation METAR/TAF reports and hyperlocal 48-hour weather details including wind gusts, visibility, and precipitation risk. Combined with the tool name, an agent can tell this is a weather lookup rather than a finance, logistics, or registry tool. It lacks an explicit verb and does not differentiate from a weather-focused sibling, but none exists in the sibling list.

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 lists the data provided but offers no explicit guidance on when to use this tool, no prerequisites, and no alternatives to consider. Usage is only implied by the domain (weather), which is below the threshold for clear context 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 provided, the description carries the full behavioral burden. It states the data categories returned but does not disclose whether the operation is read-only, whether it has side effects, authentication requirements, rate limits, data freshness, or response structure.

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 efficient sentence that front-loads the key capabilities without filler. Every phrase contributes useful information about the returned data.

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

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

The tool is simple with one parameter, but no output schema is available, so the description should more fully explain return values. It lists general data categories but does not describe the response structure, field names, or error behavior, leaving meaningful 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 description coverage is 100%, and the single required parameter is well documented as an IPv4 or IPv6 address. The description adds no parameter-specific meaning beyond what the schema already provides, 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 clearly identifies the resource (IP address) and the specific data categories returned: geolocation, ASN/ISP details, and threat detection. It is easy to distinguish from sibling tools, none of which cover IP intelligence, though it is phrased as a noun list rather than a direct verb phrase.

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 guidance on when to use this tool, when not to use it, or what alternatives exist. The intended use is only implied by the tool name and data categories, with no context about prerequisites or appropriate scenarios.

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

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

With no annotations and no output schema, the description carries the full behavioral burden, and it discloses almost nothing: 'Live' hints at freshness but no update cadence, latency, rate limits, data source, geographic coverage, or behavior on an invalid/unknown IATA code is stated. 'Terminal status' is undefined and could mean anything.

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 fragment with zero filler, front-loaded with the core concept (live airport flights). It is arguably under-specified rather than over-long, which is a completeness issue rather than a conciseness one.

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 field list partially compensates for the absent output schema by telling the agent what comes back, and the two parameters are fully covered by the schema. However, for a real-time data tool with no annotations, the missing freshness cadence, coverage, and error behavior leave notable 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 description coverage is 100%, with both parameters (type, airport_iata) fully documented including examples, so the baseline of 3 applies. The description adds no parameter-level meaning 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 names the resource and the exact data returned (departures, arrivals, gate numbers, delays, terminal status), so an agent can tell this is real-time flight status for an airport. No explicit retrieval verb, but the tool name plus the field list makes the purpose unambiguous. No sibling is close enough to require differentiation (getTransitSchedule is the nearest, and it is clearly a different domain).

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

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

There is no statement of when to use this tool versus alternatives, no mention of the transit/ground-transport siblings, and no prerequisites such as valid IATA coverage or whether the query is scoped to a single airport only. The agent gets a data list but no 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 are provided, so the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, what the return format looks like, whether authentication or rate limits apply, or how current the data is. For a multi-source data aggregation tool, these omissions are significant.

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, well-structured sentence that front-loads the core purpose and data sources with zero wasted words. It is appropriately sized for a simple one-parameter retrieval tool.

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

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

With no output schema and no annotations, the description should do more to explain what the agent will receive (e.g., expenditure figures, lobbyist lists, grant award details) and any coverage limitations. It names the upstream sources, which adds useful context, but leaves return-value expectations underspecified.

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 parameter is fully documented in the schema as 'Corporation, university, or lobbying firm name'. The description adds no further parameter semantics beyond what the schema already provides. Baseline 3 is appropriate when 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?

The description names specific resources (corporate lobbying expenditures, registered lobbyists, government R&D grant awards) and data sources (USASpending, EU Horizon), making the tool's scope clear. The verb 'Track' is somewhat generic for a retrieval tool, but the resource specificity compensates. No sibling tool covers lobbying or grants, so differentiation is naturally clear.

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

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

There is no explicit guidance on when to use this tool versus alternatives, nor any stated prerequisites or exclusions. The description implies a compliance/research use case but does not state it. An agent would need to infer usage from the resource names 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, and it does disclose the return content: rates, a safety index, and a 12-month incident breakdown. However, it says nothing about geographic coverage limits, data freshness/lag, or rate limits — relevant for a hyperlocal crime dataset where coverage varies by country.

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 with no filler — the resource and the three data components come first, the accepted locator forms last. It is a noun-phrase fragment rather than a complete statement, 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 low-risk, single-parameter read lookup with no output schema, the description tells the agent what data comes back and in what granularity. Coverage region and data freshness are the only meaningful omissions, which are minor for this tool shape.

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

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

Schema coverage is 100% with a single fully documented parameter, so the baseline is 3. The description's 'by postcode or GPS' wording aligns with the schema's accepted formats but adds no format or constraint detail 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?

The description names a specific resource (street-level crime rates, safety index scores, 12-month incident breakdowns) and its scope (hyperlocal, by postcode or GPS). It is clearly distinguishable from the nearest siblings (getHyperlocalWeather, getRealEstateStats, getParcelZoningInfo) by domain, though it never names or contrasts with an alternative.

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

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

There is no statement of when to use this tool versus alternatives, no prerequisites, and no exclusions. The only implicit guidance is the 'by postcode or GPS' scope, which the agent must infer.

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, and it does not disclose whether data is scraped live from the merchant site, how fresh it is, what happens for unsupported or policy-less domains, or whether calls are rate limited. 'Parse' is also ambiguous about whether the tool fetches the source or only normalizes supplied text.

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

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

A single tight sentence with the action front-loaded and the extracted field list acting as useful scoping. The trailing 'for AI shopping agents' is mild audience filler rather than load-bearing 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?

With no output schema and no annotations, the description partially compensates by naming the returned data categories, which is genuinely useful. It still omits coverage limits and failure behavior for domains with no published policy, leaving real gaps for a lookup 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% for the single merchant_domain parameter and the schema supplies a format example, so the baseline of 3 applies. The description adds nothing about accepted input (bare domain vs URL, subdomains, international storefronts).

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 ('Parse') and resource (merchant return policies), then enumerates the exact fields extracted: return windows, restocking fees, label eligibility, refund methods. An agent can tell this apart from getEcommercePricing or getProductReviewsSentiment by resource, 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?

'For AI shopping agents' identifies an audience but gives no when-to-use, prerequisites, or alternative routing. Nothing tells the agent when to reach for this versus getEcommercePricing or getProductReviewsSentiment in a shopping workflow.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'real-time' but does not state authentication requirements, rate limits, data freshness guarantees, market-hour constraints, or any error/edge-case behavior. For a financial data tool with zero annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that front-loads the core resource and then lists key fields. It is appropriately sized for the tool's simplicity, though it lacks a clear introductory verb that would make it more structurally complete.

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 values, and it does list the key fields returned. However, it omits the format or shape of the response (e.g., whether it is a full chain, paginated, or filtered), which would help an agent understand what to expect.

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

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

Schema description coverage is 100% for both parameters (ticker and expiration_date), so the schema already documents them fully. The description adds no further parameter detail, which is acceptable but means 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 clearly identifies the resource (equity and index options chains) and lists the specific data fields returned (strike prices, IV, delta, gamma, theta, open interest). It distinguishes itself from sibling tools like getStockEodPrices by focusing on options rather than stock prices, though it does not explicitly name a sibling or state its verb ('get').

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 indication of when to use this tool versus alternatives, no prerequisites, and no exclusions. It merely lists the data available, leaving the agent to infer that it should be called for options chain data without any explicit 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 are provided, so the description carries the full burden of behavioral disclosure. It lists returned data types but does not state whether the operation is read-only, what permissions are needed, whether address resolution has constraints, or any 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 description is a single compact sentence with zero filler, front-loading the subject matter and listing the key returned data types 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?

For a low-complexity lookup tool with one fully documented parameter and no output schema, the description is adequate: it lists the data returned. However, it omits when to use it versus sibling real-estate or geocoding tools, data coverage, and any behavioral context, leaving clear gaps.

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

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

The single parameter is fully documented in the schema (100% coverage), including that it accepts a street address or GPS coordinates. The description adds no parameter meaning beyond that, 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—parcel zoning information—and enumerates the returned data categories (zoning codes, parcel boundaries, max building height/FAR, permitted commercial uses). It is clear what the tool retrieves, but it does not distinguish itself from sibling tools such as getCommercialRealEstate or validateAddressGeocode.

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 guidance on when or when not to use this tool, nor does it name alternatives. It is purely a list of data categories with no usage 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?

No annotations are provided, so the description carries the full burden. It adds one behavioral signal ('Live', implying real-time data) and lists the returned metrics, but says nothing about freshness guarantees, rate limits, or authentication. For a read-only lookup the risk is low, but the disclosure is thin.

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

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

A single front-loaded sentence that leads with the resource and then the metrics. No filler, nothing redundant.

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 tool with a fully documented schema, the description suffices: it explains what the tool returns even without an output schema. The only gap is the absence of usage context, which is a minor omission 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 coverage is 100% and the single parameter is fully documented in the schema (UN/LOCODE or port name with examples like NLRTM, USLAX). The description adds no syntax or format guidance beyond that, 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 (seaport congestion) and enumerates concrete outputs (anchored vessels waiting count, average berth wait times), so an agent knows exactly what data comes back. It is naturally distinct from siblings like getContainerFreightRates, but it never explicitly differentiates itself, which keeps it from a 5.

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

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

No guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The agent must infer usage entirely from the description of returned 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?

No annotations, so the description carries the full disclosure burden. It hints at two behavioral traits (reviews are "verified", pros/cons are "AI-aggregated"), but says nothing about data sources, coverage, rate limits, freshness, or whether the extraction can fail on unsupported URLs.

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 front-loaded sentence listing four distinct output categories with zero filler. Efficient, though it is a fragment rather than a structured statement of behavior.

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 returned data categories, which is the right move. However, for a scraping/enrichment tool with no annotations it omits the operational context (source, limits, failure modes) an agent would need to invoke it confidently.

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

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

Single parameter with 100% schema description coverage, so the schema already documents that it accepts an ASIN, UPC, or review-page URL. The description adds no further parameter meaning; 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 ("Extract") plus a well-defined resource (customer reviews, star rating distribution, defect rates, AI-aggregated pros/cons). An agent can tell this is a product-review analytics tool, distinct from siblings like getEcommercePricing or getMerchantReturnPolicy, though it never names an alternative to sharpen the boundary.

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 when-to-use guidance, no prerequisites, and no mention of alternatives. The agent must infer from the name alone that this is the tool for review sentiment rather than, say, pricing or inventory.

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 discloses the returned metric categories and ZIP-level granularity, but omits data source, freshness, coverage limitations, units for yields/cap rates/inventory, and any access or rate-limit 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?

The description is a single efficient sentence that front-loads the metric list and ends with the geographic scope. Every phrase contributes to identifying the data returned, 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?

With no output schema and no annotations, the description does a fair job listing the metrics an agent will receive. However, it leaves important contextual gaps such as time period, data freshness, units, and geographic coverage, which matter for correct interpretation of the results.

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

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

Schema description coverage is 100%, so both zip_code and country_code are already documented in the schema. The description reinforces ZIP/postcode scoping but adds no new meaning about the country_code parameter or any constraints beyond the schema.

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

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

The description clearly identifies the resource as residential property statistics and lists specific metrics: prices per sqm/sqft, gross rental yields, cap rates, and inventory. It also scopes the geography to ZIP/postcode, which distinguishes it from the commercial real estate sibling, though it does not use an explicit verb.

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 guidance on when to use this tool versus alternatives such as getCommercialRealEstate or getParcelZoningInfo. The residential focus implies its use case, but no when-to-use, when-not-to-use, or alternative-routing guidance is provided.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the return shape (percentiles, three compensation components) and asserts the data is 'verified', but says nothing about data vintage, geographic coverage limits, or sample-size caveats that matter for benchmarking.

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 then enumerates the metrics and segmentation dimensions. Zero filler; 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?

No output schema exists, and the description compensates well by spelling out the returned percentiles and compensation components. For a read-only lookup with fully documented parameters this is nearly complete, with only data-freshness and coverage caveats 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 all three parameters are already documented in the schema. The description confirms that seniority and city are segmentation axes but adds no format or allowed-value detail 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?

The description names a precise resource (regional salary benchmarks) and enumerates the exact metrics returned (base, bonus, equity at P25/P50/P75/P90) across three dimensions. No sibling tool touches compensation data, so it is trivially distinguishable from the rest of the catalog.

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 content axes (role, seniority, city) but the description never states when to reach for this tool versus alternatives, nor any prerequisites or exclusions. Adequate but leaves the agent to infer the use case.

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

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

No annotations are supplied, so the description carries the full behavioral burden, and it discloses almost nothing: no auth/licensing requirement for EDGAR data, no rate limits, no statement of whether missing quarters return empty results or errors, and no indication of how much history is available. The single useful signal is the provenance claim 'SEC EDGAR verified', which implies higher-trust data but is not operationalized.

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

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

A single compact sentence with no filler, and the data domain is front-loaded. It is a fragment rather than a statement of capability, but nothing is padded or redundant.

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 is the only place that could explain what a call returns (single quarter vs full history, consensus fields, guidance horizon). Instead it lists the data categories and stops, leaving return shape and optional-parameter default behavior undefined for an agent trying to call it correctly.

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

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

Schema description coverage is 100%, so both ticker and fiscal_quarter are already documented with formats and examples, and the description adds little beyond reinforcing the earnings scope. It does not clarify the important behavioral question the schema leaves open: what happens when the optional fiscal_quarter is omitted.

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 (SEC EDGAR quarterly earnings, EPS actual vs consensus, forward revenue guidance), so an agent can tell it apart from neighboring finance tools like getStockEodPrices or getDividendCalendar. But it is a noun phrase with no verb — it never says the tool retrieves, compares, or summarizes anything, so the action is only inferable from the name.

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

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

There is no statement of when to use this tool, when not to, or which sibling to prefer for a related question (e.g. EPS history vs dividends vs end-of-day prices). Usage must be entirely inferred from the dataset label.

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 the data granularity ('monthly') and the four metric families returned, but leaves read-only/latency/rate-limit/auth behavior to inference from the verb 'Get'. It adds real content, yet key operational traits are 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?

A single front-loaded sentence that states the action and the four outputs with no filler. Every clause earns its place.

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

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

For a two-parameter lookup with no output schema, the description helpfully enumerates the returned fields, which is the main completeness gap it needs to fill. It stops short of noting data source, freshness, or market requirements, but the essentials for calling it correctly are present.

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

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

Schema description coverage is 100%, so both parameters are already documented, including the country default. The description adds only the loose phrase 'for any keyword' and mentions Google Ads CPC (a metric, not a parameter); nothing extra about country_code semantics or how it scopes the volume figures.

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 ('Get') plus a precisely enumerated resource: monthly search volume, Google Ads CPC, competition difficulty, and search intent. No sibling tool covers SEO keyword metrics, so an agent can route here unambiguously from the name and description alone.

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

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

The description says nothing about when to use this tool versus alternatives, nor about prerequisites such as needing a specific market or the cost/quota implications of a paid Google Ads data source. 'For any keyword' only implies breadth, which is not real guidance.

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

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

With no annotations, the description carries the full behavioral burden, and it omits auth requirements (GitHub/NPM often need tokens), rate limits, result freshness, pagination, or the shape of the returned data. It does disclose the multi-source aggregation scope, but that is closer to purpose than to behavioral traits.

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 sentence, front-loaded with the resource type, then the sources. No filler, no redundancy, and it fits the size of a two-parameter lookup tool.

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

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

For a simple two-parameter read tool with full schema coverage and no output schema, the description is adequate about what data comes back and from where. It still leaves the agent guessing about token/auth needs and freshness across four very different registries, which matters for a tool that spans hosts.

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 value: it enumerates the four concrete ecosystem values (GitHub, NPM, PyPI, DockerHub) that the schema's generic 'Package ecosystem or repository host' description leaves unnamed, effectively supplying the enum for the 'ecosystem' parameter. It adds nothing further for 'package_or_repo', which the schema already exemplifies.

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 output type — 'latest stable software releases, changelogs, and CVE security patches' — and scopes the sources to GitHub, NPM, PyPI, and DockerHub. This is far more concrete than the name alone and no sibling tool covers software supply-chain data, so differentiation is implicit. It loses a point only because 'Track' is a slightly loose verb for what is a retrieval operation.

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

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

There is no statement of when to use this tool versus alternatives, no prerequisites, and no mention of when data would be stale or unavailable. Usage is only implied by the description of what it returns.

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 names the returned fields, which is useful, but says nothing about historical range, date parameters, data freshness, timezone/currency handling, or auth/rate-limit constraints an agent would need 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?

A single front-loaded sentence listing the resource and returned fields with no waste. It is appropriately sized, though a brief usage or scope clause could have been added without bloat.

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

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

For a one-parameter lookup with no output schema, the description partially compensates by naming the returned metrics. However, it omits coverage of the temporal dimension (history, ranges) and any invocation constraints, leaving an agent with open questions on how to scope a 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?

With 100% schema coverage on the single ticker parameter (including an example with exchange suffix, e.g., LSEG.L), the schema already documents the parameter fully. The description adds no syntax or format detail beyond it, 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?

States a specific resource (end-of-day stock closing prices, adjusted closes, volume, market cap) for global equities, which an agent can readily distinguish from sibling data tools like getOptionsChainGreeks or getSecEarningsEps. It lacks explicit naming of alternatives, keeping it at 4 rather than 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 implies the tool is for retrieving EOD prices but offers no when-to-use guidance, no prerequisites, and no alternatives (e.g., intraday or options data tools). The agent must infer applicability from the resource 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 discloses data freshness ('real-time') and that surge and traffic time are included, but says nothing about coverage regions, currency, provider selection (no parameter distinguishes taxi/Uber/transfer), rate limits, or whether values are estimates subject to change.

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 with zero filler; the highest-value information (what is calculated and that it is real-time) comes first. Slightly terse for the scope it claims to cover.

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 usefully enumerates the returned values, which partially compensates. However it omits provider/coverage scope and freshness caveats that an agent needs to interpret 'estimate' 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% and both params are documented there, so the baseline is 3. The description mentions outputs (surge, travel time) but adds no meaning to pickup_address/dropoff_address beyond the schema, and notably never explains why the provider types listed are not selectable inputs.

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 (calculate) and resource (taxi/Uber/airport transfer fares, surge multipliers, traffic-adjusted travel time), so an agent knows exactly what comes back. No sibling is named, but none of the listed siblings overlaps with ride-hailing fare estimation, so differentiation is unneeded.

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 never says when to use this tool, when not to, or what alternative exists for related questions (e.g. transit vs. rideshare costs). Usage must be inferred entirely from the name and purpose sentence.

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 discloses the data domains and the temporal scope ('upcoming'), which is useful, but says nothing about data freshness, historical coverage, or response format. For a read-only lookup this is thin but not misleading.

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 with no filler. It reads as a noun phrase rather than a verb-led statement, but every word earns its place.

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

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

For a one-parameter lookup with no output schema, the description names the returned data domains (schedules, cliff dates, supply inflation impact), which is enough to set expectations. It could say more about time horizon and coverage, but it is largely complete.

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

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

Schema coverage is 100% and the lone parameter (token_symbol) is already documented with examples in the schema, so the description has no parameter obligations. 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 (VC/team token unlock schedules, cliff dates, circulating supply inflation) with enough detail to distinguish it from the crypto-adjacent siblings like getCryptoDexLiquidity. An agent can tell this returns token-supply unlock data without opening 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?

Usage is implied by the content ('upcoming' unlock schedules suggests forward-looking analysis), but there is no explicit when-to-use, no mention of alternatives, and no scope constraints. Adequate at best.

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 states the data is live and lists halt types, but does not disclose authentication needs, data refresh cadence, coverage limitations, source, or the shape of returned results. For a retrieval tool with zero structured behavioral metadata, this is a significant gap.

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

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

The description is a single concise sentence with no filler, and the key content is front-loaded. It is appropriately sized for a simple data retrieval tool.

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

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

For a low-complexity tool with one optional parameter, the description conveys the kind of data returned. However, with no output schema and no annotations, it omits usage context and behavioral details that would help an agent invoke it with confidence. It meets the minimum viable threshold but leaves clear gaps.

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

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

Schema description coverage is 100%, so the exchange parameter is already fully documented in the schema. The description adds no additional parameter meaning, syntax, or filtering behavior beyond what the schema provides. Baseline 3 is appropriate when 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?

The description clearly identifies the data domain: live stock market circuit breaker triggers, LULD halts, and pending regulatory trading resumptions. It aligns with the tool name getTradingHalts and lets an agent understand the resource without opening the schema. It does not explicitly differentiate itself from sibling financial data tools such as getStockEodPrices or getOptionsChainGreeks.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention market conditions, exchanges, or any exclusion criteria. Usage is only implied by the tool name and content.

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

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

No annotations are provided, so the description carries the full burden. It discloses two useful traits: the data is real-time (live delays) and the underlying format is GTFS. However, it says nothing about coverage regions, auth requirements, rate limits, or whether the response is a single itinerary or a full timetable, leaving major behavioral gaps for a no-annotation tool.

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

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

A single compact sentence that front-loads the resource and lists the returned content types with no filler. It is efficient; only the trailing '(GTFS)' parenthetical is slightly extraneous.

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 carry more weight, and it does enumerate what is returned (schedules, platforms, delays). But it omits geographic coverage and data-freshness constraints that matter for a real-time transit lookup, so it is only minimally adequate.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters (origin, destination, departure_time format). The description adds no additional parameter semantics such as station-code format or timezone handling. Baseline 3 applies when 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?

The description names a specific resource (transit schedules) and enumerates the content returned: train, metro, and bus schedules, platform numbers, and live delays. It is more concrete than a bare tautology and no sibling (getLiveFlights, getTaxiEstimate) overlaps in scope. It stops short of an explicit sibling differentiation statement, so it lands at 4 rather than 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 statement of when to use this tool, when not to, or what alternatives exist. The agent must infer from the verb and content alone that this is the transit-schedule lookup. No prerequisites, no route/mode selection guidance.

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

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

Annotations are absent, so the description bears the full burden. It does disclose the return payload (product specs, weight, brand, category) and implicitly a read-only lookup, which is useful. However it says nothing about auth requirements, rate limits, global coverage caveats, or not-found behavior — meaningful gaps for a lookup with zero annotation support.

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

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

A single front-loaded sentence covering purpose, accepted formats, and return fields with no filler. Every clause earns its place.

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

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

For a one-parameter read lookup with 100% schema coverage, the description is nearly complete: it names the identifier formats and the returned data. What is missing — not-found handling, response shape, and any coverage limits — is minor, and the absence of an output schema is partly offset by the listed return fields.

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 'barcode' parameter is fully documented in the schema including the 8–14 digit range. The description adds a redundant list of accepted formats (GTIN, EAN-13, UPC, ISBN) but no syntax or validation detail beyond the schema. Baseline 3 is appropriate.

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

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

The description states a specific verb (lookup) and resource (GTIN, EAN-13, UPC, ISBN barcode) and even lists the returned fields (product specs, weight, brand, category). It is unambiguous what the tool does. There is no sibling in the barcode domain to differentiate against, so the 'distinguishes from siblings' bar is trivially met, but it is a clean, specific statement rather than an exceptional one.

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 when-to-use, when-not-to-use, or alternative-tool guidance is given. There is no mention of what to do when a barcode is unknown or whether scope (e.g., ISBN vs GTIN) matters for invocation. Only the implicit reading of the name conveys the intended use.

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, and it discloses little beyond the surface action. It does not say whether output includes justification/confidence for the classification, how ambiguous goods are handled, whether rates are current, or what happens when a product cannot be classified.

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 with zero filler that names both the classification and the rate computation. 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?

There is no output schema and no annotations, so the description should carry more of the burden, yet it never hints at the return shape (code plus duty/VAT figure?) or limitations. It covers the what but not the result an agent should expect.

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 all three parameters are already documented with types and examples (ISO country codes, detailed physical description). The description adds nothing about why origin vs. destination matters or how product_description detail affects accuracy, 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 (classify, calculate) with the exact resource and standard (6-to-10 digit Harmonized System HS/TARIC codes, import duty/VAT). It is immediately distinguishable from every sibling, none of which touch customs classification.

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 only states what the tool does; it gives no when-to-use cues, no prerequisites (e.g., that origin/destination must be supplied for a rate, not just a code), and names no alternative. Usage is only inferable from the purpose itself.

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

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

With no annotations, the description carries the full behavioral burden. It does disclose genuinely useful scope information — which three registries are queried and that oppositions and Nice classes are covered — which goes beyond what the schema says. However, it says nothing about authentication needs, rate limits, result freshness, or what happens on no-match, leaving meaningful gaps for a lookup tool with zero annotation coverage.

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

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

A single front-loaded sentence covering verb, jurisdictions, and data categories with no filler. It is dense but each listed item earns its place; slightly list-like but not wasteful.

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

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

There is no output schema, so the description is the only source of return-shape information, and it lists data categories (registrations, classifications, oppositions, availability) without indicating format, result counts, or how conflicts are presented. Adequate but incomplete for a lookup tool whose output is otherwise undocumented.

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 only two parameters, so the schema already documents both brand_mark_text and nice_class thoroughly (including examples like 9 for software). The description adds no syntax, format, or matching-rule detail beyond the schema, 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?

The description gives a specific verb ('Check') and names the exact resources: USPTO, EUIPO, and UKIPO trademark registrations, Nice classifications, and oppositions. This clearly distinguishes it from sibling tools such as searchGlobalPatents, getDomainWhois, or verifyCompanyRegistryVat, though 'brand availability' partially restates the tool's own name.

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 listed data categories imply the use case (pre-filing or brand-risk checks), but there is no explicit statement of when to use this versus alternatives like searchGlobalPatents, nor any exclusions or prerequisites. Usage is inferred 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?

No annotations are provided, so the description carries the full burden. It does disclose useful capabilities (anti-bot bypass, JS rendering, LLM-ready output), which is real behavioral value, but omits rate limits, auth requirements, timeout behavior, and failure modes for blocked pages.

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 that front-loads the differentiating capabilities with zero filler. Every clause earns its place.

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

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

For a two-parameter scraping tool with no output schema, the description covers capability and output format (Markdown) adequately. It falls short only on operational details like timeouts and error behavior for pages that resist scraping.

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 target_url and wait_for_js are already documented in the schema. The description adds no syntax, format, or default information beyond what the schema provides, 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?

States specific verbs (bypass, render, extract) and a concrete resource (clean LLM-ready Markdown from any URL). This is the only scraping tool among a sibling set of data-lookup APIs, so the distinction is obvious without opening any 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 explains what it does but gives no guidance on when to reach for it versus other tools, nor any exclusions or prerequisites. An agent must infer usage entirely from the name and capability 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?

No annotations are provided, so the description carries the full disclosure burden. It does add real behavioral context beyond the name — the domain lifecycle states covered (dropped/expired/deleting), the authority-quality angle, and registration availability — but says nothing about read-only versus mutating behavior, authentication needs, rate limits, or result 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?

A single front-loaded sentence with no filler. It is information-dense and wastes no words, though the trailing qualifier clause stacks three ideas (backlink authority, availability, registration) without separating 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?

For a simple two-parameter, no-required-arg search tool with no output schema, the description conveys the essential purpose. However, with no output schema present, it should have described the shape of results (domain list, backlink metrics, registration links) and any pagination, which it omits.

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 'tld' and 'keyword' fully documented and exemplified in the schema. The description adds no additional parameter semantics (e.g., whether filters are ANDed, partial matching, or multi-value support), 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 ('Find') and a specific resource ('recently dropped, expired, or deleting domains') with a qualifying scope ('high backlink authority, available for immediate registration'). An agent can tell this is a domain-availability search, but it never distinguishes itself from the sibling getDomainWhois, which also operates on domains.

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

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

There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The phrase 'available for immediate registration' hints at the use case (acquiring expiring domains), but the agent is left to infer when this beats getDomainWhois or searchGlobalPatents-style lookups on its own.

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 lists data sources and searchable fields but says nothing about whether the operation is read-only, authentication requirements, rate limits, result pagination, or the return format. This is a significant gap for a tool with zero structured behavioral hints.

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 efficiently conveys the main scope. It is appropriately sized, though the list of searchable fields makes it slightly dense. Every clause serves a purpose.

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

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

For a simple one-parameter search tool with no output schema, the description adequately covers what is searched and where. However, it does not clarify what is returned (e.g., full patent records, snippets, metadata) or how results are structured, and with no annotations it should do more to explain behavior. It is minimally viable but with clear gaps.

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

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

Schema description coverage is 100%, and the parameter schema itself fully documents the accepted input formats (keywords, inventor/assignee, patent ID with example). The description adds no additional meaning about the parameter, so the baseline score 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 states a specific verb (Search) and a clearly bounded resource (patent grants and applications across USPTO, EPO, and WIPO), and enumerates the searchable fields (abstract claims, assignees, expiration dates). This unambiguously distinguishes it from sibling tools like lookupTrademarkAvailability or getIpIntelligence.

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 only states what the tool searches. It provides no guidance on when to use it versus alternatives, no prerequisites, and no exclusions (e.g., whether it covers only patents, not trademarks, or which office to prefer). Usage context is left entirely 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 full behavioral burden, but it only discloses that the data is 'live' and lists the covered registries. It does not explain authentication needs, rate limits, pagination, result-count limits, or whether expired tenders are excluded, all of which matter for a search tool.

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

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

The description is a single, compact sentence with no filler and is effectively front-loaded with the core resource. Its fragmentary phrasing without a verb slightly reduces clarity, but it is not bloated.

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 live search tool with no annotations and no output schema, the description should explain what a result contains, how many are returned, and how live-vs-historical coverage works. It lists data fields and sources but leaves significant behavioral and output expectations unaddressed.

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

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

Schema description coverage is 100%, so both parameters are already fully documented in the input schema. The description adds no syntax or format guidance for region or industry_keywords, 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 identifies the resource as public sector procurement tenders and names the source registries (EU TED, UK Find a Tender, US SAM.gov), which helps distinguish it from generic search tools. However, it is a noun phrase with no explicit action verb, so the agent must infer from the tool name that this performs a search rather than, for example, retrieving a known tender by ID.

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

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

There is no guidance on when to use this tool, what distinguishes it from siblings such as getLobbyingAndGovGrants or searchGlobalPatents, or any exclusions such as historical versus live-only tenders. The agent is left to infer applicability entirely from the resource description.

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

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing beyond subject matter. There is no mention of jurisdictional defaults, court coverage dates, whether statutory vs case-law results are mixed, result counts, or any auth/permission requirement for a legal research 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?

A single front-loaded sentence with a clear verb and enumerated resource scope; no filler, no redundancy, no restating 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 research tool with no annotations and no output schema, the description should explain at least what a result set looks like and how jurisdiction scoping behaves. It stops at the subject-matter list, leaving an agent unable to predict result shape or scope limits.

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 legal_query and jurisdiction documented and exemplified in the schema, so the baseline is 3. The description adds no extra meaning about query syntax, citation formats, or jurisdiction defaults 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 (Search) plus a rich resource set: federal/supreme/appellate rulings, case citations, judicial opinions, statutory precedents. This clearly separates it from the patent/tender/insolvency siblings, but it never names a sibling or differentlyiates itself explicitly from searchGlobalPatents or searchGovernmentTenders, 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 says only what the tool searches, never when to reach for it, what conditions make it the right choice, or which alternatives exist. With a dense sibling list including other search_* legal-adjacent tools, the absence of routing guidance is a real gap.

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 discloses the three core behaviors and promises rooftop-level precision, which is useful. It omits failure/ambiguity handling, accuracy fallback, authorization or rate-limit requirements, and what happens when a postal code cannot be verified.

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 primary action (standardize) and follows with verification and geocoding outcomes. No filler or redundancy.

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

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

For a simple one-parameter tool with no output schema, the description adequately covers the input intent and the returned value (rooftop GPS coordinates). Given the absence of annotations and output schema, it could do slightly more on error/ambiguity behavior, but it is sufficient to invoke 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% for the single raw_address param, so the schema already documents it as 'Unformatted or partial street address string.' The description's 'messy street addresses' restates this without adding format, syntax, or example detail, matching the baseline 3 when 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?

The description states specific verbs and resources: standardize addresses, verify postal codes, return rooftop GPS coordinates. An agent immediately knows this is an address normalization/geocoding tool. It lacks an explicit contrast to any sibling, but no sibling tool in the list overlaps with address geocoding, so differentiation is not needed.

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 'messy street addresses' – call it when you have an unformatted or partial address to clean and geocode. However, there is no explicit when-to-use statement, no exclusions (e.g. non-US addresses, PO boxes), and no mention of alternatives.

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

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

No annotations are supplied, so the description carries the full burden, and it discloses almost nothing behavioral beyond the word 'Official' hinting at an authoritative registry source. It does not say what happens on an unregistered company or invalid VAT, whether country coverage is limited to EU VIES despite the US/UK examples, whether calls are billable, or any rate limits. For a no-annotation lookup tool this is a significant gap.

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

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

A single front-loaded sentence with no filler or repetition. It is efficient, though it reads as a feature list rather than a scoped instruction, which slightly weakens how quickly the agent grasps the primary action.

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 carry return and behavior detail; it partially does so by naming the returned fields (VAT status, tax ID status, registered legal address). It omits error/not-found behavior and does not reconcile the EU VIES scope with the US/UK country_code examples, leaving the agent under-informed for a two-required-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 both parameters are fully documented in the schema, so the baseline is 3. The description does not add syntax or format meaning beyond what the schema already states; its terms (VAT validation, tax ID status, legal address) describe outputs rather than the two inputs.

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 (company registry / EU VIES VAT) and enumerates the data it returns (registration, VAT validation, tax ID status, legal address), so an agent can tell it apart from checkCorporateInsolvency or getCorporateDirectorsUbo. It is a capability list rather than a verb-led statement, and it never explicitly names the sibling it replaces, so it stops 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?

Usage is only implied by the enumerated capabilities: an agent can infer 'call this to validate a VAT number or look up a registration,' but there is no statement of when this is preferable to checkCorporateInsolvency, getCorporateDirectorsUbo, or lookupTrademarkAvailability, and no exclusions or prerequisites. Adequate but with clear gaps.

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