MCPFax Public-Data Utility API

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds useful context beyond annotations: the data source (OurAirports), per-call pricing, HTTP method, and the fact that results are ordered/determined by distance. No contradictions with annotations.

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

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

The description is compact and front-loaded with the primary purpose. Every clause earns its place: purpose, data source, cost, and endpoint. There is no redundant filler.

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

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

The description conveys the essential output type (nearest airports with IATA/ICAO identifiers by distance), and the schema fully documents required and optional parameters. Annotations cover the safety profile. It does not explicitly describe pagination or detailed result shape, but for a simple nearest-airport lookup the definition is sufficiently complete.

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

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

Schema description coverage is 100%, with all four parameters documented and examples provided, including the radius default. The description adds no parameter-level meaning beyond 'Coordinates' and 'by distance', so the baseline of 3 applies.

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

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

The description states a specific transformation: Coordinates → nearest airports (IATA/ICAO) by distance. It clearly identifies the resource and result, and is distinct from sibling geospatial tools like v1_geocode or v1_reverse_geocode. It does not merely restate the tool 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 phrase 'Coordinates → nearest airports' makes the intended use case clear: when an agent needs airports near a latitude/longitude point. It does not explicitly mention alternatives or when-not-to-use, but no sibling tool covers airport proximity, so ambiguity is low.

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 already mark the tool as read-only, idempotent, open-world, and non-destructive. The description adds useful behavioral context beyond annotations: data source, cost per call, HTTP method, geographic scope, and output content. No rate-limit or auth details are given, but the safety profile is already covered by annotations.

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

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

The description is compact and front-loaded with the core function, followed by concise source, cost, and endpoint metadata. Each clause adds relevant information, though 'Air quality' somewhat repeats the title.

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

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

For a simple read-only tool with optional parameters and full schema descriptions, the description and schema together provide enough to invoke it correctly. The lack of an output schema is offset by the description naming the return content, though the relationship between 'q' and lat/lon could be clearer.

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

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

The input schema already documents all three parameters with descriptions and examples, so schema coverage is 100%. The description adds only high-level 'by coordinate' context and does not meaningfully enhance understanding of the parameters beyond the schema.

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

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

The description clearly identifies the tool as returning US & European AQI and pollutant concentrations by coordinate, which is specific and distinguishes it from most siblings. It lacks an explicit action verb like 'retrieves' or 'gets', but the meaning is unambiguous.

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

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

The description implies when to use this tool: when air quality data is needed by coordinate or place name. However, it provides no explicit guidance about alternatives, exclusions, or how this relates to sibling tools like v1_weather or geocoding.

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 already cover read-only, idempotent, and non-destructive behavior. The description adds useful context beyond those annotations by mentioning the computation source (Nager.Date), the per-call cost, and the HTTP endpoint, which helps the agent understand external dependencies and pricing.

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

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

The description is compact, front-loaded with the core purpose, and every part earns its place. The source, cost, and endpoint are included without clutter.

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

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

The tool is simple and the schema covers parameters well, but there is no output schema and the description does not state the return shape, explain inclusive/exclusive date behavior, or clarify what happens when only 'start' is provided without 'end' or 'days'. These are real but minor 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?

Input schema coverage is 100%, so the schema already documents every parameter clearly. The description adds a useful high-level explanation of the two modes, but does not meaningfully extend parameter meaning beyond what the schema properties already state.

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

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

The description clearly states the resource ('business days') and specific operations ('Count business days in a range, or add/subtract N business days'), which distinguishes it from sibling tools like v1_holidays. It is specific and not a tautology of the tool 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 description gives clear context for when to use the tool: counting business days in a range or applying a business-day offset. It does not explicitly name alternatives or exclusions, but the mode split ('count mode' vs 'offset mode') is enough to guide correct 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, providing a clear safety and idempotency profile. The description adds context about the source (eCFR/GovInfo) and pricing ($0.01 per call), but does not disclose additional behavioral traits such as default date behavior or what happens for invalid citations. With strong annotation coverage, this is adequate but not enriched.

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 one compact sentence followed by a source note, pricing, and endpoint. Each piece is informative and no unnecessary words are present. The key action and resource are front-loaded.

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

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

For a simple read-only tool with full schema coverage, the description is mostly adequate. However, it does not clarify common usage details like whether a title alone is enough to fetch an entire title, how part/section are scoped, or the meaning of 'current' relative to the optional date parameter. These gaps are not severe given the schema's per-parameter examples, but an agent might benefit from a brief usage example.

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 fully documents all four parameters. The description adds only the phrase 'for a citation' and does not explain how to construct a citation or how parameters interact. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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

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

The description clearly states the tool fetches current Code of Federal Regulations text for a citation, naming the resource (CFR legal text) and the action (fetch). It is distinguishable from siblings, though it does not explicitly contrast with any sibling that might also deal with legal or reference data.

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

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

The description implies usage by specifying 'for a citation' and mentions the source (eCFR/GovInfo), but it does not provide explicit when-to-use guidance or alternatives. Since the sibling list contains many unrelated tools, the lack of an explicit exclusion is not critical, but no direct usage scenario guidance 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds meaningful context beyond annotations by citing the ISO 3166 source, the 'edge dataset' nature, the $0.005 per-call cost, and the GET /v1/country endpoint. No contradictions, and the description reinforces an expected deterministic read-only 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 core purpose is delivered in a single front-loaded sentence, followed by three compact metadata tokens: source, cost, and endpoint. Every element contributes information and no words are wasted. The structure is easy to parse quickly for an agent.

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 thorough annotations, the description is complete. It states the input format, enumerates the returned fields, identifies the data source, and includes the endpoint and pricing. Although no output schema is present, the listed return fields mitigate that gap and give an agent enough information to invoke the tool correctly.

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

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

Schema coverage is 100% and the schema already describes q as 'Country name or ISO alpha-2/alpha-3/numeric' with an example. The description's 'Name/ISO code' adds no new semantic detail beyond what the schema provides. Score remains at the baseline 3 because the schema carries the full parameter documentation burden.

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

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

The description clearly states the resource (country metadata) and the exact mapping from input to output: 'Name/ISO code → currency, capital, calling code, languages, flag.' This differentiates it from sibling tools like v1_geocode or v1_timezone by specifying the precise domain and what the lookup returns. The verb is implied but unambiguous for a GET endpoint.

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 should be used when country metadata is needed, but it does not explicitly state when to prefer it over alternatives or provide any exclusions. Sibling tools are not mentioned, so an agent must infer suitability from the country-specific focus. This meets the standard of implied usage only.

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 already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: the data source (Coinbase/CoinGecko), the per-call cost, and the GET method. This is meaningful extra information for an agent deciding whether to invoke the 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 compact and front-loaded with the core purpose, followed by source, pricing, and endpoint. Every clause provides useful information with no redundancy. It is appropriately sized for a simple 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 read-only lookup with fully documented parameters and strong annotations, the description is nearly complete. It includes source, cost, and HTTP method, but it does not describe the response format; however, no output schema exists and the return value is strongly implied by 'spot price'. This is a minor gap rather than a critical omission.

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 parameters are already documented with examples and defaults. The description adds minor semantic context by noting that the quote currency can be fiat or crypto, but it does not significantly enrich the parameter meaning 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 clear resource and outcome: the spot price of a crypto asset in a quote currency. It differentiates from siblings such as v1_stock_quote and v1_fx by explicitly specifying 'crypto asset' and 'coinbase / coingecko' as the source. The endpoint path reinforces the specific resource.

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 crypto spot price lookups, but it does not explicitly mention when to choose this tool over alternatives like v1_stock_quote or v1_fx. The word 'crypto' provides strong implicit guidance, yet no explicit exclusions or alternative conditions are stated.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds meaningful operational context beyond annotations: it identifies the upstream sources (Google/Cloudflare DoH), the HTTP method, and the cost per call, which helps an agent understand external dependencies and side effects.

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

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

The description is a single front-loaded sentence containing the purpose, source, cost, and HTTP method. Every element earns its place, and there is no redundant filler or repetition of schema details.

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

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

For a simple two-parameter tool with full schema coverage and strong safety annotations, the description provides enough for an agent to invoke it correctly. It lacks explicit return-value details, but the phrase 'Resolve DNS records' makes the expected output reasonably clear, and the source/cost/endpoint information adds useful context.

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

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

The input schema already describes both parameters with examples and defaults, achieving 100% schema description coverage. The description does not add further parameter-level meaning beyond saying that DNS records are resolved, so the baseline score of 3 applies.

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

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

The description uses a specific verb and resource: 'Resolve DNS records via DNS-over-HTTPS.' It also names the source and endpoint, making the tool's function unmistakable and clearly distinct from every sibling tool, none of which are DNS record lookups.

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 DNS resolution but provides no explicit when-to-use guidance, exclusions, or alternatives. An agent can infer the context from the name and description, but the tool does not help differentiate it from similar-looking network or lookup tools.

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

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

Annotations already cover idempotency, read-only behavior, and non-destructiveness. The description adds useful operational context beyond annotations: the data source (openFDA), the HTTP method (GET), and per-call cost. It does not discuss rate limits or errors, but these are not critical for a simple lookup.

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

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

The description is compact and front-loaded with the tool identity, followed by source, cost, and endpoint. There is minor redundancy in mentioning openFDA twice, but overall the text is efficient and scannable.

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 read-only lookup with fully documented parameters, the description plus schema and annotations cover the tool's purpose, source, cost, and request shape. The absence of an output schema is partially mitigated by naming the content types in the description, though exact return fields are not specified.

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

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

The input schema has 100% coverage: q, field, and limit each have descriptions and examples, including allowed values for field. The description adds no parameter-specific meaning, 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 clearly identifies the resource as FDA structured drug labeling and names the content areas (uses, warnings, dosage), which distinguishes it from sibling tools. It stops short of an explicit action verb like 'search' or 'retrieve,' but the endpoint and example field make the lookup intent 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?

The description provides no guidance about when to choose this tool over alternatives, nor does it state exclusions or context conditions. It only mentions the source, price, and endpoint, leaving usage inference entirely to the title 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context beyond those annotations: it identifies the source as World Bank Open Data, states the cost ($0.008 per call), and specifies the GET endpoint, reinforcing that this is a read-only, paid HTTP lookup. No contradiction or omitted major behavioral trait is apparent.

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 remarkably compact: one purpose statement, a source attribution, pricing, and the HTTP endpoint. Every segment earns its place, and the central purpose is front-loaded before supporting details. There is no redundant or filler language.

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-only tool with robust annotations and full schema coverage, the description is largely complete. It states the source, examples, cost, and endpoint. It does not describe the exact response format or time-series structure, but 'time series' plus the indicator code context provides enough for an agent to select and invoke the tool correctly.

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

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

The input schema already provides full documentation for both parameters (country and indicator), including formats and examples. The description adds no parameter-level information, so it does not meaningfully extend the schema. Baseline 3 is appropriate because the schema covers 100% of parameter meaning.

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

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

The description clearly identifies the resource as World Bank economic indicator time series for a country, with concrete examples (GDP, unemployment) and the source (World Bank Open Data). It is more informative than the generic title 'Economic indicator', though it does not explicitly differentiate itself from related siblings like v1_inflation or v1_country.

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 context is implied rather than stated: the description indicates this tool serves World Bank indicator time series for a country, so an agent can infer when to use it. However, it offers no explicit guidance about alternatives, such as v1_inflation for inflation-specific data, or conditions that would make this tool the right choice over a sibling.

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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds source (Frankfurter/ECB), pricing, and the GET endpoint, but does not disclose response shape, pagination, or potential 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 concise one-liner that front-loads the core purpose and includes useful metadata such as source, pricing, and HTTP method. It avoids filler, though the pricing detail is arguably non-essential.

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 read-only 3-parameter tool, the description plus schema covers what the tool does and how to invoke it. However, it omits the return format and does not differentiate when to use this tool versus unit conversion or crypto price tools, leaving some selection ambiguity.

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

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

Schema coverage is 100%, so the baseline is 3. The description's mention of 'optional amount conversion' loosely maps to the amount parameter but adds no syntax or edge-case detail beyond what the schema already provides.

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

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

The description clearly identifies the resource as ECB reference FX rates and mentions optional amount conversion. This distinguishes it from generic unit conversion (v1_unit_convert) and crypto pricing (v1_crypto_price), though it lacks an explicit verb such as 'retrieve'.

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

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

No guidance is given about when to use this tool versus alternatives like v1_unit_convert or v1_crypto_price. The description does not state scenarios, exclusions, or when a different tool would be more appropriate.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context: the external data source (Open-Meteo / Nominatim), a per-call cost, and the GET HTTP method, which are not present in the annotations or schema.

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

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

The description is a single compact sentence broken into scannable fragments: operation, mapping, source, pricing, and endpoint. It is front-loaded with the core purpose and contains no filler or redundant 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 simple read-only geocoding tool, the description plus fully-covered schema is largely sufficient. It states the key outputs, the source, and the price, though it omits a detailed response structure (no output schema). Given the low parameter count and clear scope, the missing response shape is a minor gap.

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

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

Schema description coverage is 100%, with q, count, and country each documented and exemplified (e.g., 'London', '3', 'GB'). The description itself does not elaborate on parameters, so it adds nothing beyond the schema, matching the baseline score of 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?

The description opens with the specific operation 'Forward geocoding' and an explicit mapping 'Place name → coordinates, country, timezone,' which clearly distinguishes this tool from siblings like v1_reverse_geocode and v1_ip_geolocation. The resource (place name) and the output (coordinates) are concrete and immediately understandable.

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 term 'Forward geocoding' provides clear context for when to use it: when you have a place name and need coordinates or location metadata. It does not explicitly name alternatives or state when-not-to-use, but the directionality is self-explanatory and differentiates from the reverse geocoding sibling.

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 already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context beyond annotations: source (Nager.Date), cost ($0.005 per call), and the HTTP endpoint, but it does not disclose response format or error behavior.

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

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

The description is very short and front-loaded, with source, cost, and endpoint appended efficiently. Minor redundancy exists: 'Public holidays' is repeated in the title and the description prefix, wasting a few characters.

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-only tool with full schema coverage and informative annotations, the description is largely complete: resource, scope, source, cost, and endpoint are all present. The absence of any detail about the returned holiday list shape is a minor gap given no output schema 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%, with clear descriptions for year and country, so the schema already carries the parameter meaning. The description simply restates that holidays are for a country and year, adding no new parameter-level detail.

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 holidays for a country and year, and the endpoint 'GET /v1/holidays' signals retrieval. It is distinguishable from siblings like v1_business_days by the 'country & year' scope, though it does not explicitly name alternatives.

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 use case is implied: call this when public holidays for a country and year are needed. However, there is no explicit when-to-use, when-not-to-use, or comparison against sibling tools such as v1_business_days.

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 already establish read-only, idempotent, non-destructive behavior. The description adds useful context: NLM as the source, per-call cost, and the HTTP method. It does not disclose output format, pagination behavior, or whether at least one of q/code is effectively required despite the schema marking all params optional.

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

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

The description is compact and front-loaded with the core action. The only minor redundancy is the 'ICD-10-CM search:' prefix, which restates the title, but the rest of the sentence earns its place by adding source, pricing, and method.

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 read-only lookup with three optional, well-documented parameters, the description is mostly complete. The lack of an output schema means a brief note on return value shape would improve completeness, but the low complexity and strong schema coverage offset that gap.

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

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

The input schema has 100% parameter description coverage, so the schema already explains q, code, and maxList with examples. The description's phrase 'by term or code' adds mild reinforcement but not significant new semantics.

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'), a specific resource ('ICD-10-CM diagnosis codes'), and the two supported access modes ('by term or code'). This clearly distinguishes the tool from the many other domain-specific sibling lookup tools.

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

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

No guidance is given about when to prefer this tool over alternatives, nor are any exclusions or prerequisites mentioned. The intended use is only implied by the domain name and search language, not explicitly 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral context with the data source (World Bank CPI), pricing ($0.008 per call), and HTTP method (GET), but it does not disclose return format, error behavior, or how missing optional parameters are handled. Given the annotations, this level of added context is acceptable but not exceptional.

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 that leads with the core function, followed by three concise clauses for source, pricing, and endpoint. Every element provides useful information with zero redundancy. This is appropriately front-loaded and efficient.

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 calculator with rich annotations and 100% schema parameter coverage, the description covers the essential function, scope, source, and cost. However, since there is no output schema, the description does not explain what the tool returns (e.g., adjusted amount as a number, a table, or additional metadata). Without any return-value information, an agent may be uncertain about how to interpret the response.

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

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

Schema description coverage is 100%, so the baseline is 3. The main description's phrase 'Adjust an amount between years' loosely maps to the `from`, `to`, and `amount` parameters but does not add syntax, format, or default details beyond what the schema already provides. The per-parameter descriptions in the schema carry the semantic load.

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

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

The description states a specific verb and resource: 'Adjust an amount between years using CPI.' It also clarifies scope ('any country, not just US') and data source (World Bank CPI), which distinguishes it from related tools like v1_fx or v1_unit_convert. The title 'Inflation calculator' reinforces the purpose, creating a clear, unambiguous definition.

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

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

The description implies when to use this tool (adjusting monetary amounts for inflation using CPI) and the 'any country, not just US' note adds useful context, but it does not explicitly mention alternatives or exclusion cases such as 'for currency conversion use v1_fx instead.' Usage guidance is present by implication rather than explicit 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?

Annotations already declare this as read-only, idempotent, non-destructive, and open-world, so the description's main responsibility is adding beyond those hints. It does add useful context: the upstream source (ipwho.is / ip-api), pricing, and HTTP route. However, it does not disclose potential failure modes, data freshness, or whether results may vary between the two sources.

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

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

The description is extremely compact and front-loaded, leading with the core transformation and then adding only high-value details: source, cost, and endpoint. Every phrase earns its place, and there is no redundant prose. It is a model of concise tool documentation.

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 tool with rich annotations, the description covers the essential facts: what it does, the output categories, the upstream service, the cost, and the route. There is no output schema, but the description names the expected result fields well enough. It could add a note about possible error cases or the difference between the two data sources, but nothing critical is missing for basic invocation.

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

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

Schema description coverage is 100%, and the schema already explains the ip parameter format with an example. The description adds that the IP maps to location, ASN, and ISP, which indirectly clarifies what the parameter produces but does not explain restrictions such as public vs private addresses. Baseline 3 is appropriate because the description does not need to compensate for missing schema fields.

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

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

The description clearly states the resource (IP address) and the result (location, ASN, ISP), which distinguishes it from sibling geocoding tools that operate on street addresses or places. The arrow notation is a concise substitute for a verb, making the operation obvious. It could be slightly stronger with an explicit 'look up' or 'resolve' verb, but the intent is unambiguous.

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

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

There is no guidance about when to prefer this tool over alternatives such as v1_geocode, v1_reverse_geocode, v1_country, or v1_timezone. The description mentions the source and cost but does not state the intended use case or exclude scenarios where another sibling would be more appropriate. An agent must infer usage solely from the tool name and the IP-specific input.

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

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

Annotations already cover read-only, idempotent, open-world, and non-destructive behavior. The description adds useful operational context such as the data source, pricing, and HTTP endpoint, but it does not disclose details like what happens when both q and lei are provided, how matches are returned, or error behavior.

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

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

The description is compact and front-loaded, with all key facts in one sentence. 'LEI lookup:' is slightly redundant with the title, but the rest—source, pricing, and endpoint—is useful and 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 lookup with no output schema, the description gives the essential inputs and source but stops short of explaining response shape, match semantics, or whether one of q/lei is required. Since the schema allows empty required fields, a note that at least one of q or lei should be supplied would improve completeness.

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

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

Schema description coverage is 100%, with both q and lei well documented including examples. The description's mention of 'by LEI code or entity name' adds no meaning beyond what the schema already provides, 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 clearly states the tool performs a lookup of Legal Entity Identifier records, with the resource (LEI code or entity name), source (GLEIF), and endpoint. It is specific enough to distinguish from the other lookup tools, especially since no other tool targets LEI data.

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

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

The description gives clear context: use this to retrieve an LEI record by either the 20-character LEI code or an entity legal name. It does not explicitly mention when not to use it or name alternatives, but the GLEIF source and LEI-specific purpose make the intended usage reasonably obvious.

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 already declare readOnly, idempotent, non-destructive. The description adds cost ($0.008 per call), the HTTP method (GET), and the data source (U.S. Census 2022 NAICS). These are useful behavioral details beyond the annotations. No contradictions.

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 compact sentence conveys the purpose, two usage modes, expected output, source, and cost. It is front-loaded with the core function and contains no fluff. Every element earns its place.

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

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

For a simple lookup tool with two parameters, the description covers the main behavior (code vs. keyword) and the return type (title, hierarchy, children). It lacks a caveat about required parameters (none are marked required) but implies you need one of the two. The source and cost details add context. This is largely complete for a tool of this complexity.

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

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

Schema coverage is 100% — both parameters are described with examples. The description adds no extra semantic meaning beyond what the schema provides (e.g., it does not explain mutual exclusivity or what happens if both are provided). Baseline 3 is appropriate given the schema's completeness.

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

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

The description states the specific resource (NAICS lookup) and the two operations (code → title/hierarchy/children, and keyword search). It is clearly distinct from all sibling tools, which cover different domains (airports, weather, DNS, etc.), so an agent can easily differentiate it.

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 indicates the two modes ('code' or 'keyword search') and what each returns. It implicitly tells the agent when to use it (when NAICS info is needed). It does not explicitly compare to alternatives, but none of the siblings are relevant, so the guidance is sufficient.

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

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

Annotations already cover the safety profile (read-only, idempotent, non-destructive), so the bar is lower. The description adds useful operational context such as the source (rdap.org), per-call cost, and HTTP endpoint, but it does not disclose response format, error behavior, or request limitations beyond the annotations.

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 front-loads the core purpose and packs in source, pricing, and endpoint without any wasted words. It is highly efficient and well-structured.

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, but with no output schema, the description could better explain what registration fields are returned and how to choose between the domain and IP parameters. The current text gives only a general sense of the output and leaves some operational detail to inference.

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

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

The input schema provides 100% coverage of both parameters, each with descriptions and examples, so the schema carries the burden. The description adds no additional parameter-level meaning beyond what is already in the schema.

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

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

The description clearly identifies the tool as providing RDAP whois data for domains or IPs, with 'Structured domain/IP registration data (modern whois)' conveying the core function. It distinguishes the tool from generic DNS or geolocation lookups, though it does not explicitly name sibling tools.

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

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

No guidance is given on when to use this tool versus alternatives like v1_dns or v1_ip_geolocation. The description only provides the data source and pricing, leaving the selection context implicit.

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

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

Annotations already cover the read-only, idempotent nature of the tool. The description adds valuable behavioral context beyond annotations: the data sources (Nominatim/BigDataCloud), the cost per call ($0.005), and the HTTP method (GET). It does not mention response format or errors, but the added operational detail earns a solid score.

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 operation, then appends three useful metadata facts: source, cost, and HTTP endpoint. Every clause contributes value with zero 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 two-parameter, read-only, idempotent tool, the description combined with annotations and schema is nearly complete. It covers the core behavior, cost, source, and endpoint. The only notable gap is the response format, but since there is no output schema and the tool is simple, this is a minor omission.

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

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

The input schema covers 100% of parameters with descriptions and examples for lat and lon. The description ties the parameters to the coordinate-to-address operation, but it does not add new meaning beyond the schema. With full schema coverage, 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 states the operation as reverse geocoding, expressed as 'Coordinates → nearest address'. This is specific and makes the tool's purpose immediately obvious. It implicitly differentiates from sibling v1_geocode, though it does not name it explicitly.

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

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

The description clearly implies the use case: converting latitude/longitude to a nearby address. It does not provide explicit when-to-use/when-not-to-use guidance or name alternatives like v1_geocode, but the semantic clarity gives an agent enough context to choose it correctly.

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 already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the source ('direct fetch') and pricing ($0.005 per call), which are useful behavioral context beyond the annotations. It does not mention parsing behavior details or edge cases, but the annotation coverage raises the baseline.

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

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

Two sentences plus a compact source/cost/method suffix. Every part earns its place: the title explains what's fetched, the description adds parsing behavior, and the suffix provides source, pricing, and HTTP method 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 single-parameter, read-only, idempotent tool with full schema coverage and helpful annotations, the description is nearly complete. It lacks explicit mention of return format or failure behavior, but no output schema is expected and the annotations cover the operation's safety profile. A 4 reflects the minor gap in response semantics.

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

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

Schema description coverage is 100%, with the url parameter clearly described as 'Any URL on the target site' and a helpful example. The description adds the overall purpose but no additional parameter-level semantics, so the baseline 3 is appropriate.

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

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

The description states a specific verb ('Fetch and parse') and resource ('a site's robots.txt and llms.txt'), clearly distinguishing it from the data-lookup siblings. It doesn't explicitly contrast with a similar tool, but the resource is specific enough that an agent can understand what it does.

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 by noting it fetches and parses robots.txt/llms.txt and takes any site URL, but it gives no explicit guidance on when to prefer this over alternatives or when not to use it. For a niche tool among many data-lookup tools, this minimal context is adequate but not enriched.

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

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

The annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context by distinguishing between the default company-profile/filings mode and the concept-specific XBRL mode, plus source and cost information. No contradiction with annotations.

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

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

The description is concise and front-loaded with the core functionality. The endpoint and per-call cost are extra but useful operational details; they do not bloat the text significantly.

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 read-only lookup tool with no required parameters and no output schema, the description provides sufficient context: it explains the two output modes and identifies the data source. It does not detail output formatting or edge cases, but those are not declared by the schema either and are less critical here.

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

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

The input schema has 100% parameter description coverage, so the description does not need to re-document each parameter. It does add a little semantic linkage between the two output modes and the concept/taxonomy parameters, but most parameter meaning is already in 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 states the resource (SEC EDGAR company facts) and the two distinct kinds of output: company profile plus recent filings, or XBRL financial facts for a concept. This differentiates it from sibling tools like stock quotes or economic indicators.

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

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

The description gives clear context for when to use the tool: for SEC company profiles, recent filings, or XBRL concept facts. It does not explicitly name alternatives or exclusion conditions, but the use cases are specific enough to guide selection.

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

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context beyond that: the quote is delayed rather than real-time, the data source is Yahoo Finance, and there is a per-call cost. This gives an agent important expectations that annotations alone do not provide.

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 that front-loads the core function and includes the most decision-relevant details: delay, asset scope, source, and cost. The leading 'Stock quote:' phrase is slightly redundant with the title but does not materially waste space.

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

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

This is a simple one-parameter, read-only tool with no output schema. The description adequately conveys what an agent needs: what it returns, for which instrument types, from what source, and at what cost. It does not enumerate all 'key stats' but that level of detail is not necessary for selecting or 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?

The input schema fully documents the only parameter, 'symbol', with a description and example. The tool description adds no additional parameter-level meaning, so the baseline of 3 applies for high schema description coverage.

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

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

The description clearly identifies the tool's output as a delayed quote with key stats and scopes it to equity/ETF/index instruments. It does not use an explicit verb like 'get' but the meaning is unambiguous and the asset-class scope helps distinguish it from crypto, FX, and economic data siblings.

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

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

Usage context is implied through 'equity/ETF/index' and the Yahoo Finance source, suggesting it is for stock and fund quote lookups. However, it does not explicitly state when not to use it or mention alternatives like v1_crypto_price or v1_fx, leaving the routing decision partly 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?

Annotations already declare the operation read-only, idempotent, and non-destructive, lowering the burden on the description. The description adds useful context: the upstream sources, the per-call cost, the GET endpoint, and the returned fields. It does not mention response timezone or location-precedence behavior, but the schema covers the default date and the annotations cover safety.

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

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

The description is extremely compact and well front-loaded: the returned values appear immediately, followed by source, pricing, and endpoint. Every clause carries useful information and there is no redundant filler.

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

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

For a simple read-only tool with zero required parameters and full schema coverage, the description provides enough for an agent to infer the result categories. The lack of an output schema is partially offset by listing the returned fields. A minor gap is the absence of timezone conventions or how the q parameter interacts with lat/lon, but these are not blocking.

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

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

Schema description coverage is 100%, so all four parameters are already documented with types and examples. The description adds almost no parameter-specific meaning beyond hinting that location is coordinate-based. Baseline 3 is appropriate because the schema is doing the semantic 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 identifies the resource—sunrise, sunset, solar noon, and day length—and specifies that it works by coordinate. It goes beyond the title by listing the exact computed fields and the data source. It does not use an explicit action verb like 'get' or 'retrieve', and it does not actively distinguish itself from time/weather siblings, but the meaning is 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 guidance about when to use this tool versus v1_timezone, v1_weather, v1_geocode, or v1_holidays. The intended use case is implied by the name and field list, but the description never states the conditions under which this tool is the right choice.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context by naming Open-Meteo as the external source, noting the per-call cost, and specifying the GET endpoint. This informs the agent about the external dependency and billing implications beyond the annotations.

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

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

The description is compact and information-dense, including the mapping, the output categories, the data source, the cost, and the HTTP method in one line. It repeats the title phrase 'Timezone by coordinate,' which is slightly redundant, but overall the structure is efficient and front-loaded with the core 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?

Given the tool's simplicity, two required parameters, and strong annotations, the description provides adequate context: it lists the return concepts, the external provider, the cost, and the endpoint. There is no output schema, but the description names the important output fields, so an agent has enough information to decide to call it and understand the result.

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 clear examples for lat and lon, so the schema carries the parameter semantics. The description only refers generically to 'Coordinates' and does not add additional meaning about parameter formats, bounds, or coordinate order beyond what the schema already provides.

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

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

The description clearly defines the tool's function: converting a coordinate into an IANA timezone, UTC offset, and local time. This is a specific resource-action mapping that distinguishes it from geo-related siblings like reverse_geocode or geocode, which return different data types.

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

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

The title 'Timezone by coordinate' implies when to use it, but the description does not explicitly state when it should be preferred over alternatives or mention any exclusions. An agent can infer usage from the coordinate inputs and output fields, but there is no direct 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds genuinely useful behavioral context beyond annotations: 'deterministic edge compute', cost ($0.005 per call), and HTTP method (GET). There is no contradiction between the description and annotations.

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

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

The description is compact and informative, with the core action front-loaded and supported categories listed efficiently. The addition of source, cost, and endpoint is useful and not wasteful. Minor redundancy exists in the 'Unit conversion:' prefix, but overall every sentence earns its place.

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

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

The tool is simple, and the schema plus annotations provide a good baseline. However, the description does not list the exact unit symbols accepted for each category, does not describe the response format, and does not state behavior for invalid units. Since there is no output schema, a bit more return-value guidance would strengthen completeness.

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

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

Schema description coverage is 100%, with each parameter (value, from, to) already documented and examples provided. The description does not add parameter-level detail, but the baseline of 3 applies because the schema already carries the semantic weight. It does not need to repeat what the schema covers.

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

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

The description states a specific verb and resource: 'Convert between units' with an explicit list of measurement categories (length, mass, volume, speed, area, data, time, pressure, energy, temperature, angle). It clearly distinguishes itself from the sibling v1_fx by omitting currency and covering physical units instead, so an agent can pick this tool without ambiguity.

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

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

The description implies usage: use this tool for unit conversions within the listed categories. However, it provides no explicit guidance about when not to use it, no mention of alternative tools, and no edge-case routing (e.g., 'for currency use v1_fx'). It is minimally adequate but leaves usage 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?

Beyond the read-only and idempotent annotations, the description reveals that validation is live (MX/deliverability), sourced from edge compute and DoH, and costs $0.005 per call. This adds meaningful behavioral and operational context without contradicting the annotations.

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

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

The description is compact and efficiently packages the core purpose, behavior, source, cost, and endpoint into one short line. It has no filler and front-loads the most important information.

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

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

For a tool with a single required parameter, rich annotations, and no output schema, the description provides all necessary invocation context: what it does, how it behaves, the HTTP method/path, and the cost. Nothing critical is missing for an agent to call it correctly.

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

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

The input schema already covers the single email parameter fully with a description and example. The tool description adds no additional parameter-level semantics, 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 clearly states the resource (email) and the specific validation checks performed: syntax check, live MX/deliverability, and disposable-domain flag. This makes the tool's purpose unambiguous and visibly distinct from the unrelated sibling endpoints.

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 clear context for when this tool is relevant: whenever an email address needs validation, including deliverability and disposable-domain checks. It does not explicitly state when not to use it, but no sibling tool is an obvious alternative for email validation, so the guidance is sufficient.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds valuable context beyond annotations: data source (NHTSA vPIC), cost ($0.01 per call), and HTTP method/endpoint (GET /v1/vin-decode). It does not describe response format or error behavior, but for a safe read-only lookup this is acceptable.

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

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

The description is compact and front-loaded with the core purpose, followed by source, cost, and endpoint. The leading 'VIN decode:' label is slightly redundant with the tool name, but it does not meaningfully detract from the overall efficiency.

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 required parameter, the schema fully documents parameters, and annotations cover the safety profile. The description provides source, cost, endpoint, and US-market scope. No output schema exists, but the return value is implied by the decode operation, so nothing essential for calling it correctly is 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%, with both 'vin' and 'year' already documented through types, examples, and descriptions. The description adds the 'US-market' qualifier and source, which enriches the context but does not add param-specific syntax 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 explicitly states 'Decode a US-market Vehicle Identification Number' and names the source 'NHTSA vPIC'. This is a specific verb+resource and clearly distinguishes it from all sibling tools, none of which relate to VIN decoding.

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 does not explicitly state when to use this tool versus alternatives, nor does it name exclusions. However, the purpose is so domain-specific that usage is strongly implied: when a US-market VIN needs decoding. Sibling tools are all unrelated, so no alternative routing is needed.

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

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

Annotations already convey read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable context beyond annotations: data source (Open-Meteo/NWS), per-call cost, HTTP method, and response scope. No contradiction with annotations.

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

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

The description is compact and front-loaded: it opens with the core forecast purpose, then adds source, cost, and endpoint in a single tight line. 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 read-only tool with all optional parameters, the description provides enough context by listing the output categories and operational details. It doesn't explain coordinate-vs-place precedence, but the schema already covers that. No output schema exists, yet the response contents are summarized.

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 each parameter already described by name, type, and example. The description doesn't elaborate parameters, but the baseline of 3 applies since the schema carries the semantic burden.

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

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

The description clearly states what the tool returns: current conditions, 12-hour rain probability, multi-day forecast, and a plain 'will it rain' answer. It is concrete and largely distinguishes itself from the weather_alerts sibling by focusing on forecast data, though it does not explicitly name that sibling.

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 capability list implies when to use this tool, but there is no explicit when-to-use guidance or mention of alternatives such as v1_weather_alerts. The usage context is inferable rather than stated.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context by noting the data source (NWS) and that alerts are 'active' rather than historical. It does not disclose output shape, pagination, rate limits, or the implications of optional parameters, but the annotations carry much of the burden.

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 short and front-loaded with the core purpose, followed by source and pricing/endpoint details. The word 'Active' is repeated from the title, which is minor redundancy, but the overall size is appropriate and every other part adds useful context.

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 read-only tool with rich annotations, the description is mostly adequate. However, it does not clarify that lat/lon and q are alternative inputs, nor does it warn that all parameters are optional and at least one is presumably needed for a meaningful call. With no output schema, some indication of what the response contains would strengthen completeness.

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

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

The input schema has 100% description coverage, so parameters are already documented. The description reinforces that lat/lon represent a US coordinate but does not explain the relationship between q and lat/lon or that none are required. Since schema coverage is complete, the baseline 3 is appropriate.

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

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

The description clearly identifies the resource as 'Active NWS watches/warnings for a US coordinate', which is specific enough to distinguish from sibling tools like v1_weather or v1_air_quality. The 'US coordinate' scope adds precision, and the endpoint path reinforces the retrieval purpose.

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

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

The description gives clear context: this tool returns active NWS watches/warnings for a US location. It does not explicitly name alternative tools or state when not to use them, but the alert-specific wording makes the appropriate use case obvious. It lacks explicit exclusion guidance, which keeps it from a 5.

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

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

Annotations already indicate a safe, read-only, idempotent operation, and the description does not contradict them. The description adds useful operational context such as the Wikipedia REST source, endpoint path, and per-call cost, though it does not describe response format or failure behavior.

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

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

The description is compact and front-loaded with the core purpose, followed by source, cost, and endpoint details. It is slightly repetitive with 'Wikipedia' appearing multiple times, but no sentence is wastefully long.

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-only tool with strong annotations, the description covers purpose, source, cost, and endpoint. It does not specify the response format, but the phrase 'Lead summary/extract' gives a reasonable expectation, and the schema covers the parameters well.

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 already explained with examples: 'title' for the page title and 'lang' for the language with a default. The description adds no additional parameter-level meaning, 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 clearly states the tool returns the lead summary/extract for a Wikipedia page, which is a specific verb-plus-resource action. It is easily distinguished from all sibling tools, which cover separate domains like weather, DNS, or country data.

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

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

The description makes it clear the tool is for obtaining Wikipedia page summaries by page title. It does not explicitly say when not to use it, but none of the sibling tools are close enough to create confusion, so the context is sufficient.

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

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

Annotations already establish read-only and non-destructive behavior. The description adds valuable context: the external source (Zippopotam.us), per-call cost ($0.005), and the GET endpoint, which are useful operational details beyond the annotations.

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 line that front-loads the purpose and then adds source, pricing, and endpoint information efficiently. Every element earns its place, with no redundancy.

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

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

For a simple read-only postal code lookup, this is complete: it states the input, output, data source, cost, and HTTP method. Annotations cover safety, and the output 'place(s) & coordinates' is sufficient given the tool's simplicity.

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

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

Schema description coverage is 100%, so the schema already documents both 'code' and 'country'. The description does not add extra meaning about either parameter beyond what the schema provides.

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

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

The description opens with 'Postal code lookup' and clearly specifies the transformation '(ZIP/postal code → place(s) & coordinates)', making the tool's purpose obvious. It also distinguishes itself from sibling tools like v1_reverse_geocode (coordinates → place) by showing input and output.

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 arrow implies when to use the tool: when you have a postal code and need places or coordinates. However, it does not explicitly mention alternatives such as v1_geocode or v1_reverse_geocode, nor does it state when not to use this tool.

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