MCPFax Public-Data Utility API

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context beyond that: it discloses pricing per call, the data source (OurAirports), that it is an 'edge dataset', and that results include IATA/ICAO codes by distance. This goes beyond the minimum and helps set expectations.

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 purpose and then adds high-value metadata (endpoint, source, price). There is no filler or repetition of schema content.

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

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

For a simple four-parameter lookup with rich annotations, the description covers the essential context: purpose, input mapping, output identifiers, data source, and cost. There is no output schema, so a little more detail about the exact response shape could help, but the description gives enough for correct selection and invocation.

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

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

Schema description coverage is 100%, so every parameter is already documented in the input schema. The description does not add parameter-specific guidance, but it does not need to because the schema provides clear examples and defaults. 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 defines the operation as converting coordinates into the nearest airports by distance, with a specific resource ('airports') and endpoint path. It distinguishes this tool from siblings by naming the exact functionality and even notes IATA/ICAO output. The arrow notation is a clear functional mapping, so the purpose is unambiguous.

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

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

The description clearly implies this tool should be used when coordinates are available and the nearest airports are needed. It does not explicitly state when not to use it or point to alternatives like reverse geocoding, but the context is strong enough for an agent to select 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 the tool read-only and idempotent. The description adds useful behavioral context beyond that: data source (Open-Meteo/CAMS), geographic scope (US & European AQI), cost per call, and the underlying endpoint. It does not describe response format limitations, but annotations lower the burden here.

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: one sentence for what it returns, one for source, and one for cost/endpoint. Every piece adds useful context without redundancy or filler.

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

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

For a low-complexity lookup tool, the description covers what the tool returns, how it is invoked (by coordinate), its data source, and its cost. It does not explicitly describe the output schema or clarify whether q vs lat/lon is preferred, but the schema covers those details sufficiently.

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 well documented with types, descriptions, and examples. The tool description adds only the general notion of coordinate-based lookup and does not need to explain each parameter further.

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 air quality and specifies the data returned: US & European AQI plus pollutant concentrations, with coordinate-based lookup. It is clear and unambiguous, though it lacks an explicit verb and does not differentiate from neighboring tools like v1_weather.

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 by stating that this is a coordinate-based air quality lookup, so an agent can infer when to call it. It does not explicitly mention alternatives or exclusions, which prevents 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 declare the tool read-only, idempotent, and non-destructive; the description adds useful non-obvious context: 'Source: computed + Nager.Date', '$0.005 per call', and 'GET /v1/business-days'. This informs an agent about data provenance, cost, and HTTP interface 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 front-loaded: one sentence covering behavior, data source, cost, and endpoint path, with no filler. Every clause earns its place.

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

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

For a simple read-only calculator, the description plus the fully documented schema is mostly sufficient: required start, count vs offset modes, and country default are all covered. The main gaps are the return format and inclusive/exclusive boundary semantics, but these are minor given the examples and low 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?

The schema already documents all four parameters with examples and mode labels ('count mode', 'offset mode'), giving 100% coverage. The description restates the count/offset concepts but does not add parameter-level meaning beyond what the schema already provides, so it stays at the baseline 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 'Business-day math' and explicitly states the two operations: 'Count business days in a range, or add/subtract N business days, skipping weekends & holidays.' This clearly names the resource and behavior, making it easy to distinguish from siblings like v1_holidays or v1_timezone.

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: any business-day calculation that skips weekends and holidays. It does not explicitly name alternatives or exclusion cases, such as pointing to v1_holidays for raw holiday data, so it stops short of full when-not guidance.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds the source, per-call price, and GET method, which are consistent with the annotations. However, it does not disclose the return format or clarify that the date parameter can select a historical effective date, despite the word 'current.'

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 followed by three short metadata fragments. Every clause adds value: what it does, the source, the cost, and the endpoint. The 'CFR legal text:' prefix is slightly redundant with the title but does not detract from clarity.

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

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

With no output schema, the description's statement that it returns 'text' is helpful, and the schema fully documents the four flat parameters. However, the description does not clarify how a CFR citation is constructed (which combinations of title, part, and section are valid) even though only title is required. This leaves an agent uncertain whether a title alone is sufficient and whether part/section are additive filters.

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 having a description and examples, so the schema carries the parameter documentation burden. The description adds only the vague notion of 'a citation' without explaining how title, part, and section combine. Therefore, the baseline score of 3 is appropriate.

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

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

The description states a specific action and resource: 'Fetch current Code of Federal Regulations text for a citation.' It also names the source (eCFR/GovInfo), making the tool's scope immediately identifiable. Among the sibling tools, none covers CFR legal text, so it is readily distinguished without requiring schema inspection.

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 frames the use case: retrieving current CFR text for a citation. It adds useful context by specifying the source, cost, and HTTP method. It does not explicitly list when-not-to-use alternatives, but because no sibling tool competes for this exact resource, this is a minor gap.

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

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful operational details beyond annotations: cost per call ($0.005), HTTP method (GET), and data source (ISO 3166). 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?

A single, dense sentence that communicates the resource, input-to-output mapping, source, cost, and endpoint with no wasted words. The primary purpose is front-loaded in the first three words.

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

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

For a one-parameter, read-only lookup with no output schema, the description covers input format, expected return fields, source, and cost. Minor omissions like error behavior or exact response structure are not critical given the tool's simplicity and the parameter-level schema.

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

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

Schema coverage is 100%, with the 'q' parameter fully described and an example provided. The description reinforces input semantics by mentioning 'Name/ISO code' but adds little beyond what the schema already states, so baseline 3 is appropriate.

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

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

Description clearly states the tool provides country metadata, mapping a name or ISO code to currency, capital, calling code, languages, and flag. It is distinguishable from sibling lookup tools by naming the country-data domain, though it lacks an explicit action verb like 'look up' or '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?

The description implies usage by defining the resource and output fields, but it does not explicitly state when to use this tool versus alternatives or when not to use it. For a simple, domain-specific lookup, this is adequate but not fully explicit.

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, and non-destructive behavior. The description adds useful behavioral facts beyond those: data source (Coinbase/CoinGecko), per-call cost ($0.005), and the GET endpoint. It does not discuss freshness or response shape, but the annotation coverage lowers 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 a dense single sentence with source, cost, and endpoint appended, with no unnecessary filler. The leading 'Crypto spot price:' is slightly redundant with the title but does not hurt usefulness.

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

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

For a low-complexity, two-parameter, read-only GET with fully documented schema fields and strong annotations, the description is sufficient for selection and invocation. The lack of an output schema means response structure is not covered, but the domain is simple and the annotations carry the safety 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 documents both parameters with descriptions and examples, so coverage is 100%. The description adds the semantic relationship between symbol and quote currency, but that is a modest increment over the schema's existing explanations.

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 and operation: the spot price of a crypto asset in a quote currency. It also adds the source and endpoint, and the 'crypto asset' wording differentiates it from siblings like v1_fx or v1_stock_quote.

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 it: crypto spot price lookups with a fiat/crypto quote currency. It does not explicitly name alternatives or exclusions, but the scope is specific enough that an agent can select it without ambiguity.

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, and non-destructive behavior. The description adds context beyond those annotations by specifying Google/Cloudflare DoH as sources, a per-call price, and the GET endpoint, which helps an agent understand network behavior and cost.

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

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

The description is a single compact sentence that front-loads the core purpose, then adds source, cost, and endpoint information without wasted words. Every clause contributes useful operational 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?

Given the low complexity, 100% schema coverage, and rich annotations, the description is nearly complete. It could mention the response format or supported record types, but the core invocation context—purpose, source, endpoint, and key params—is sufficiently covered.

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

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

Schema description coverage is 100%, so the schema fully documents both 'name' and 'type'. The description does not add parameter-level detail, but the baseline of 3 is appropriate because the schema carries the semantic weight.

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 action ('Resolve DNS records') and the mechanism ('via DNS-over-HTTPS'), making the tool's purpose immediately clear. It also names the data sources, which distinguishes it from the broader set of lookup-style 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?

The description implies when to use the tool: whenever DNS records need to be resolved. However, it provides no explicit when-not-to-use guidance or alternatives, though no close DNS-specific sibling exists in the provided list.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds operational context: the $0.01 per-call cost and the GET endpoint, which are consistent with those hints. It does not detail response format or limitations, but the strong annotation coverage lowers that 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, front-loaded with the resource and content, and includes useful cost/endpoint metadata. The phrase 'Source: openFDA' repeats the earlier mention of openFDA, creating minor redundancy, but overall it is 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?

This is a simple read-only lookup with fully documented parameters and strong annotations. Combined, the description and schema cover the search term, optional field, limit, cost, and endpoint. The absence of an output schema and return-format note is a minor gap for a tool this straightforward.

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 descriptions cover all three parameters with examples, allowed values, and defaults (100% coverage), so the description does not need to repeat them. The description only adds high-level domain context, which does not exceed the baseline for fully documented parameters.

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 (FDA drug label) and the content domain (structured drug labeling: uses, warnings, dosage), making it clear what the tool returns. It lacks an explicit action verb like 'retrieve' or 'search', but the resource is unique among siblings, so no differentiation is needed.

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

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

Usage context is implied by the resource name and the mention of uses, warnings, and dosage, but the description does not explicitly state when to use this tool, what it should not be used for, or any sibling alternative. An agent must infer the proper context from the tool's name and schema.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful behavioral context: source is World Bank Open Data, cost is $0.008 per call, and endpoint is GET /v1/econ-indicator. It does not explain response format, frequency of data, or error behavior, but the annotations lower 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-loads the core function before adding source, cost, and endpoint details. The opening 'Economic indicator:' is slightly redundant with the title, but otherwise every word is purposeful and there is no fluff.

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 GET tool with rich annotations, the description is reasonably complete: it identifies the data source, the kind of return (time series), and cost. It lacks details about units, date ranges, and pagination, but these are not critical for an agent to make an initial correct call.

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

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

Schema coverage is 100%, with both parameters described and examples provided. The description adds no additional meaning beyond the schema, so it is at the baseline level.

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

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

Description states it provides World Bank indicator time series for a country (GDP, unemployment, etc.), which is a clear resource and scope. However, it does not distinguish itself from sibling tools like v1_inflation or v1_country, so it is clear but not fully differentiated.

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

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

No guidance on when to use this tool versus alternatives such as v1_inflation. The description gives no selection criteria, exclusions, or context about choosing among economic data tools, leaving an agent to infer usage from the schema alone.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context beyond annotations: it identifies the rates as 'Reference FX rates (ECB)' (implying they are reference, not necessarily real-time market rates), names the sources (Frankfurter/ECB and open.er-api), and notes the per-call cost. This helps an agent understand the nature of the data and cost implications.

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, tight sentence that front-loads the main function, then provides source, cost, and endpoint details. Every phrase earns its place, with no filler or redundancy. It is compact and immediately scannable 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 simple read-only tool with 3 parameters fully documented in the schema, the description is nearly complete. It includes source, pricing, and endpoint, which are useful operational details. It does not describe the response format or whether historical rates are supported, but given the absence of an output schema and the low complexity, these are 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?

Schema description coverage is 100%, with clear descriptions and examples for from, to, and amount. The description adds only the notion of 'optional amount conversion,' which is already encoded in the schema's 'Amount to convert (default 1)'. With the schema doing the heavy lifting, a baseline score of 3 is appropriate; the description does not introduce new parameter-level 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 states the tool provides 'Reference FX rates (ECB) with optional amount conversion,' specifying the resource (currency exchange rates) and the key feature (amount conversion). It is distinct from sibling tools like v1_unit_convert and v1_crypto_price based on the explicit 'FX rates' scope. The title and description together leave no ambiguity.

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

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

The description provides clear context: this is for currency exchange rates and conversions, and it mentions the data source and pricing. It does not explicitly mention when not to use it or name alternatives, but the purpose is sufficiently specific for an agent to select it for currency-related requests. No misleading usage guidance is present.

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 valuable context beyond annotations: the data source (Open-Meteo / Nominatim), the cost per call ($0.005), and the HTTP method/endpoint (GET /v1/geocode). It does not disclose rate limits or attribution requirements, but these are not critical for basic selection.

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

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

The description is a single concise sentence that front-loads the core purpose, then adds source, pricing, and HTTP method without waste. Every piece of information 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, read-only geocoding tool with a fully documented schema, the description covers the essential behavior: input type, output fields, source, and cost. It does not detail the exact response envelope or error behavior, but those are not necessary for correct selection and invocation 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?

Input schema coverage is 100%, so the schema already documents all three parameters with examples and descriptions. The description adds general context about what is returned but does not need to restate parameter details.

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 operation: forward geocoding converting a place name into coordinates, country, and timezone. It clearly distinguishes this from the sibling v1_reverse_geocode and the broader geolocation-related 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?

The phrase 'Forward geocoding' clearly signals the input-output direction (place name to coordinates), which implies when this tool should be used versus reverse geocoding. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select 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 mark the operation as read-only, idempotent, open-world, and non-destructive. The description adds useful extra behavioral context: the third-party source (Nager.Date), the per-call cost, and the HTTP endpoint. This goes beyond the annotations without contradicting them.

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, cost, and endpoint. One minor redundancy exists: 'Public holidays' appears in both the title and the first phrase. Overall, it wastes very few words and is easy to parse.

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 two well-documented parameters, the description provides enough context to select and invoke the tool: what it returns, for which country/year, and at what cost. There is no output schema, but the description sufficiently conveys the expected result type. It could add a brief statement about the response shape or holiday names/dates, but this is a minor gap.

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

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

Schema coverage is 100%, with both parameters (country and year) described with examples. The description only repeats that the tool uses country and year, adding no new meaning beyond the schema. The baseline of 3 is appropriate because the schema already carries the semantic weight.

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 public holidays for a given country and year. It names the resource (public holidays), scope (country & year), and source (Nager.Date), making the tool understandable. It does not use an explicit verb like 'get' or 'list,' but the intent is unambiguous.

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

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

The description states that this tool is for retrieving public holidays filtered by country and year, which gives clear context for when to call it. It does not explicitly mention alternatives or exclusion cases, such as comparing with v1_business_days, but the usage context is reasonably clear.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful context beyond annotations by naming the data source (NLM Clinical Tables), pricing, and HTTP method (GET), which reinforces the read-only nature.

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 and cost details. The leading 'ICD-10-CM search:' phrase somewhat repeats the title, but the rest of the sentence earns its place with concrete functional and operational information.

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

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

For a simple search tool with comprehensive parameter schema and safety annotations, the description is largely complete. It covers what is searched, how, and operational details. The main missing piece is what happens if neither q nor code is supplied, but this is minor given the optional parameter schema.

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

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

The input schema already provides full coverage for all three parameters with examples and descriptions, so the baseline is 3. The description adds no extra semantics beyond mentioning search by term or code; it does not further explain the relationship between q and code or maxList behavior.

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

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

The description clearly states the tool's purpose with a specific verb ('Search') and resource ('ICD-10-CM diagnosis codes'), and it indicates the two search modes: by term or by code. This distinguishes it from sibling tools that handle other data domains.

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

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

The description conveys what the tool searches and how, but it does not explicitly say when to prefer it over alternatives or mention exclusions. The usage context is implied 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=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context: data source (World Bank CPI), global country coverage, per-call pricing, and endpoint. 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 a single dense sentence that front-loads the core purpose ('Adjust an amount between years using CPI') and appends only high-value metadata: source, pricing, and endpoint. No wasted words.

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

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

For a simple lookup-style calculation tool with full schema coverage and non-destructive annotations, the description provides all essential context: what it calculates, the data source, country scope, and cost. No output-schema expectations are stated, but the adjusted-amount outcome is implied and unlikely to confuse an agent.

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

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

Schema description coverage is 100%, so the schema fully documents each parameter. The description reinforces the 'any country' aspect relevant to the country parameter and the year-to-year adjustment relevant to from/to, but adds no parameter-level details beyond the schema.

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

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

The description states a specific verb and resource: 'Adjust an amount between years using CPI'. It also differentiates from generic conversion tools by noting 'any country, not just US', making the tool's purpose and scope 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 gives clear context for when to use the tool: adjusting monetary amounts across years via CPI. It does not explicitly name alternatives like v1_fx or v1_unit_convert, but the CPI-specific framing is sufficient to route an agent appropriately.

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

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

Annotations already signal read-only, idempotent, and non-destructive behavior. The description adds value beyond annotations by naming the external data sources (ipwho.is / ip-api) and the cost per call, giving agents awareness of the external dependency and pricing. It does not cover failure modes or caching, but this is acceptable for a simple read-only 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 a single compact line that front-loads the core function, then adds source, cost, and endpoint details without redundancy. Every element earns its place and there is no filler.

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

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

For a one-parameter tool with no output schema, the description provides the essential return fields (location, ASN, ISP), identifies the upstream source, and notes the cost. It omits a detailed response format and error behavior, but for this simple read-only tool, the information is largely sufficient.

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

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

Schema coverage is 100%, and the 'ip' parameter is already documented as an 'IPv4 or IPv6 address' with an example. The description does not add parameter-level detail beyond the arrow notation, so the schema carries the semantic load and the baseline score 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 the exact transformation ('IP address → location, ASN, ISP'), making the tool's function concrete. The IP-specific input plus ASN/ISP output clearly distinguish it from address-based geocoding siblings like v1_geocode and v1_reverse_geocode.

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: when an agent needs location, ASN, or ISP data from an IP address, this tool is the match. However, there are no explicit when-to-use or when-not-to-use statements, and no direct comparison with v1_geocode or v1_reverse_geocode.

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 communicate `readOnlyHint`, `openWorldHint`, `idempotentHint`, and non-destructiveness. The description adds useful contextual facts—source GLEIF, per-call pricing, and GET endpoint—but it does not disclose response format, pagination, or behavior when both `q` and `lei` are absent. With annotations covering the safety profile, the description provides moderate added value.

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 packs the tool's purpose, query modes, source, pricing, and endpoint without wasted words. It is front-loaded with the core purpose and every clause adds useful 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 simple two-parameter lookup with rich read-only annotations and no output schema, the description gives enough to invoke the tool correctly: the resource, the two search modes, the data source, the cost, and the HTTP method. The only minor omission is a note about providing at least one of `q` or `lei`, since neither parameter is required in the schema.

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

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

Schema description coverage is 100%: both `q` and `lei` have examples and their meaning is documented. The description only restates the two query modes already present in the schema, so it adds little parameter-level meaning beyond what the input schema provides. The baseline of 3 is appropriate.

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

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

The description clearly identifies the tool as an LEI lookup, states the resource (Legal Entity Identifier records), and specifies the two supported query modes: by LEI code or entity name. It is specific enough to distinguish from all sibling tools, none of which relate to 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 makes the usage context clear: use this tool when you need an LEI record and have either an LEI code or an entity name. It does not explicitly name alternatives or exclusions, but among the sibling tools there is no close substitute, so the lack of explicit 'when not to use' guidance is not a significant gap.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety and idempotency are covered. The description adds valuable context beyond those: the data vintage ('2022'), the authoritative source ('U.S. Census', described as an 'edge dataset'), per-call cost ($0.008), the GET endpoint, and a preview of response content (title, hierarchy, children). It does not describe response shape or invalid-input behavior, but the annotation coverage lowers 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?

A single compact sentence packs purpose, source, vintage, cost, and endpoint with zero filler. The core function ('NAICS lookup:') is front-loaded, and every clause — Census source, 2022 edition, edge dataset, pricing, GET route — contributes new information not present in the schema or annotations.

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-optional-parameter tool with strong read-only/idempotent annotations, the description covers the essentials: what the code path returns, what the keyword path does, the source, and the price. There is no output schema, so the description's return-value preview is helpful; however, it leaves two minor gaps: behavior when neither 'q' nor 'code' is supplied, and the result shape for keyword searches.

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

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

Schema description coverage is 100% — both 'q' and 'code' are already documented with type, examples, and descriptions, so the baseline is 3. The description adds only marginal meaning by framing 'code ... or keyword search' as alternatives and signaling that code is the hierarchical lookup path, but it introduces no format, constraint, or default details beyond the schema.

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

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

The description states a specific purpose: 'Official 2022 NAICS code → title, hierarchy, children; or keyword search' — a clear verb (lookup/search), a specific resource (2022 NAICS from U.S. Census), and the two operational modes. The NAICS domain is unique among the 30+ sibling tools, and the title 'NAICS lookup' combined with the output detail distinguishes it 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 gives clear usage context by spelling out both invocation modes: you either supply a NAICS code to get title/hierarchy/children, or perform a keyword search via the alternative path. It does not state explicit when-not or alternative-tool guidance, but no sibling tool competes with NAICS classification queries, so no exclusion 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 declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful context beyond that: the data source (rdap.org), the HTTP method (GET), and per-call pricing, which is relevant for cost-aware agent decisions.

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 tight sentence: it front-loads purose, then includes source, price, and endpoint metadata. No word is wasted.

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

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

The description is adequate for a low-complexity read-only lookup, but it does not explicitly state that a domain or IP value must be provided, and there is no output schema to document response shape. Since the tool is otherwise well-annotated, this is a moderate gap rather than a critical one.

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 and examples for both 'domain' and 'ip'. The description adds little beyond indicating that the tool handles domain/IP registration data, and it does not clarify that one of the two parameters should typically be supplied even though neither is marked required.

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 RDAP as the resource and states it returns structured domain/IP registration data, distinguishing it from related tools like v1_dns or v1_ip_geolocation. It lacks an explicit action verb like 'get' or 'retrieve', so it stops short of a 5.

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

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

The phrase 'modern whois' and 'domain/IP registration data' implies the tool is for registration lookups rather than DNS resolution or geolocation. However, there is no explicit guidance on when to choose this tool over specific siblings, and no stated exclusions or alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context beyond those: the upstream providers (Nominatim / BigDataCloud), the per-call cost, and the HTTP GET endpoint. It does not contradict any annotation.

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: purpose first, then source, cost, and endpoint. Every clause contributes information without filler, making it easy for an agent to parse quickly.

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 geocoding tool with rich annotations and full schema coverage, the description is nearly complete. It lacks explicit output-format details, but no output schema exists and the phrase 'nearest address' conveys the expected result. Minor gaps like error handling or no-result behavior prevent a 5.

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 examples and descriptions for both lat and lon. The tool description itself does not add extra parameter-level meaning, but it does not need to because the schema already documents the parameters fully. Baseline 3 applies.

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

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

The description states a specific transformation ('Coordinates → nearest address') and reinforces it with the 'Reverse geocoding' label. This clearly differentiates from sibling v1_geocode and other tools 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 clearly communicates the direction of the tool (coordinates in, address out), which tells an agent when to use it versus the likely forward-geocoding sibling. It does not explicitly name alternatives or exclusions, but the context is unambiguous for the main use case.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context by stating the source is a 'direct fetch', that the tool parses the files, and that it uses GET. 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 one tight, front-loaded sentence that conveys action, resource, source, cost, and endpoint without redundancy. Every element earns its place, and there is no filler.

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

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

For a single-parameter, read-only fetch tool, the description covers what is retrieved, the source, and the HTTP method. It does not detail output shape or behavior when robots.txt or llms.txt is absent, but the annotations and simple schema make these 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?

Schema coverage is 100%: the only parameter, url, is described with an example and the note 'Any URL on the target site'. The description does not add additional URL semantics beyond the schema, so the baseline score of 3 is appropriate.

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

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

The description states a specific action ('Fetch and parse') on a specific resource ('a site's robots.txt and llms.txt'), making the tool's function immediately clear. It is also distinct from all sibling v1_* tools, none of which target these files.

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

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

The intended use is implied by the tool name and resource description, but there is no explicit when-to-use/when-not-to-use guidance and no named alternatives. An agent can infer the use case, but the description does not proactively help with 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?

The annotations already establish read-only, idempotent, open-world, non-destructive behavior. The description adds useful context beyond those: the data source, the per-call cost, the HTTP endpoint, and the fact that the tool has two distinct output modes depending on whether a XBRL concept is supplied.

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

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

The description is compact and packed with relevant details: output modes, source, cost, and endpoint. Minor redundancy exists because 'SEC EDGAR' appears in both name/title and source, and the endpoint mirrors the tool name, but overall it is efficiently 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?

With no output schema, the description does convey the high-level output categories. However, it does not specify how the two modes are selected, whether cik or ticker is required, or what happens when neither is supplied. This leaves room for an agent to mis-invoke the tool despite the 100% schema coverage.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds only general mentions of 'concept' and 'XBRL taxonomy', which do not meaningfully clarify parameter usage beyond the existing property descriptions.

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 names the resource (SEC EDGAR) and specifies what it returns: company profile plus recent filings, or XBRL financial facts for a concept. It lacks an explicit verb like 'retrieve' or 'fetch', and does not contrast itself with similar data tools, so it misses the top score.

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 two use cases ('profile + filings' or 'XBRL financial facts') but never says when to choose this tool over alternatives such as v1_stock_quote or v1_econ_indicator. There is no explicit 'use when' guidance or mention of exclusions.

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

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context: data is delayed rather than real-time, the source is Yahoo Finance, and there is a per-call cost of $0.005—information not available from 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 line that front-loads the core purpose and then adds source, cost, and endpoint details. Every element contributes meaningful information with no redundancy or filler.

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

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

For a one-parameter read-only tool with no output schema, the description provides enough to invoke it correctly: asset class, data delay, source, and cost. It could name the specific quote fields, but that is not necessary for selecting or calling the tool.

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

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

Schema description coverage is 100%, so the schema already documents the single 'symbol' parameter with an example. The description does not add parameter-level detail, 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 a delayed quote and key stats for equities, ETFs, and indices, naming Yahoo Finance as the source. This scope distinguishes it from sibling tools like v1_crypto_price and v1_fx, which cover different asset classes.

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 asset-class scope ('equity/ETF/index') tells the agent when this tool is appropriate, and by contrast implies crypto and FX are handled elsewhere. It does not explicitly name alternatives or give exclusion conditions, but the context is clear enough for correct selection among the listed siblings.

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

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful context in the form of the data source ('sunrise-sunset.org / Open-Meteo') and cost ('$0.005 per call'), but does not go further into response shape or rate limits. 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 extremely compact: one resource line, source, pricing, and endpoint, with no filler. Every element earns its place and key facts 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 lookup, the description covers the output fields and source while annotations cover safety. The absence of an output schema is mitigated by listing the data fields explicitly; however, it does not clarify that q or lat/lon is needed (all schema fields are optional).

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 every parameter has an example and description, so the description adds no parameter-level meaning beyond signaling the coordinate basis. Baseline 3 is appropriate because the schema already carries 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 specifies the resource ('Sunrise, sunset, solar noon, day length') and scope ('by coordinate'), which clearly distinguishes it from unrelated siblings like weather or timezone. It lacks an explicit verb such as 'get' or 'retrieve,' but the output list makes the action 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?

Usage is only implied: the description says the data is 'by coordinate,' so an agent can infer this is for solar timing queries, not for general weather or timezone. It names no alternatives, exclusions, or whether lat/lon vs q is preferred, leaving selection guidance 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 declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds useful operational traits such as the data source (Open-Meteo), per-call cost, and HTTP method, but does not go into deeper behaviors like DST handling or response format.

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

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

The description is extremely compact and front-loaded. The core mapping is stated in one sentence, followed by source, cost, and endpoint details. 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 two-parameter read-only lookup with full schema coverage, the description is complete. It lists the expected outputs, compensating for the missing output schema, and adds source, pricing, and endpoint context. No critical operational detail appears to be 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?

The schema provides 100% coverage for both lat and lon parameters, each with descriptions and examples. The description only refers to 'Coordinates' generically and does not add any parameter-level meaning beyond what the schema already documents.

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

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

The description clearly states a coordinate-to-timezone transformation and enumerates the specific outputs (IANA timezone, UTC offset, local time). The phrase 'by coordinate' distinguishes it from sibling location-related tools like v1_geocode or v1_reverse_geocode.

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

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

The intended usage is clear: use this tool when you have latitude/longitude coordinates and need timezone information. It does not explicitly name alternatives or exclusion conditions, but the context is strong enough to guide an agent without confusion.

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 readOnly, idempotent, and non-destructive behavior. The description adds useful context beyond annotations by noting 'deterministic edge compute,' a per-call cost, and the HTTP GET endpoint, which helps agents understand reliability and side effects without contradicting 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 short and front-loads the core purpose followed by concise metadata. The opening phrase 'Unit conversion:' is slightly redundant with the title, but the rest of the description is compact and informative.

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 three-parameter conversion tool, the description is adequate: it names the unit categories, confirms deterministic behavior, and full schema documentation covers inputs. It does not specify valid unit code formats or return structure, but this is not critical given the open schema and examples.

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 having descriptions and examples. The tool description adds no meaningful parameter-level detail beyond the schema, 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 clearly states the tool converts between units and enumerates the supported domains (length, mass, volume, speed, area, data, time, pressure, energy, temperature, angle). It is a specific verb+resource description, though it does not explicitly distinguish itself from the sibling v1_fx currency conversion tool.

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

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

The listed unit categories imply when the tool is relevant, but the description provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives such as v1_fx for currency conversion, leaving some routing decisions to inference.

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

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

Beyond the annotations, the description discloses meaningful behavior: live MX/deliverability checks, edge compute and DoH as the source, a per-call cost, and the HTTP method. This adds operating context that annotations alone do not provide, and it does not contradict the readOnly/idempotent hints.

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

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

The description is a single, information-dense sentence with no filler. It front-loads the core purpose and appends only high-value operational details: source, cost, and endpoint.

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, the description covers the essential behavior and expected outcomes (syntax, MX/deliverability, disposable-domain flag). There is no output schema, so a bit more detail on the exact response structure could help, but the described checks are sufficient for an agent to invoke and interpret 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 coverage for the single email parameter is 100%, so the schema already fully documents the input. The description adds no parameter-level detail, but the schema provides an example and clear meaning, meeting the baseline expected for high schema 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 resource (email) and operation (validation), and enumerates concrete aspects: syntax check, live MX/deliverability, and disposable-domain flag. This distinguishes it from the broad set of data-lookup sibling tools 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 makes the use case clear: validate an email address with syntax, MX, and disposable-domain checks. While it does not explicitly name alternatives or exclusions, no sibling tool directly competes, so an agent can infer when to select this tool confidently.

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, and non-destructive. The description adds valuable operational context beyond those annotations: external source (NHTSA vPIC), cost ($0.01 per call), and HTTP method (GET). This helps an agent understand 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 compact single line that front-loads the core purpose before adding source and pricing details. The opening 'VIN decode' is slightly redundant with the title, but otherwise 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 two-parameter read-only endpoint, the description provides essential context: purpose, market scope, source, price, and endpoint. It omits return-value details, but the absence of an output schema and the straightforward decode operation make the definition sufficient for correct selection and invocation.

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

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

The input schema fully documents both parameters with 100% coverage, including vin format/example and year as a model-year hint. The description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.

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

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

States a specific verb ('Decode') and a specific resource ('US-market Vehicle Identification Number'), and adds the data source (NHTSA vPIC). No sibling tool overlaps with VIN decoding, so the purpose is unambiguous and the tool stands out at a glance.

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

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

The description clearly sets the target scope ('US-market') and the operation (decode), which tells an agent when this tool applies. It does not explicitly name alternatives or exclusion conditions, but the unique domain and stated source give sufficient context for 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: data sources (Open-Meteo / NWS), cost per call, the HTTP method (GET), and the specific output categories. 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 a single compact sentence that front-loads the core purpose, then lists the key outputs and practical metadata (source, cost, endpoint). Every clause contributes useful information with no filler.

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

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

There is no output schema, so the description helps by naming what the caller receives: current conditions, hourly rain probability, multi-day forecast, and a plain answer. It also includes source and cost. Some minor details like response format or units are absent, but for this simple read-only weather tool the description is nearly complete.

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

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

The input schema covers all parameters (q, lat, lon, days) with 100% description coverage, so the schema does the heavy lifting. The description adds no additional parameter-level meaning beyond saying the forecast is multi-day, which is acceptable given the high schema 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 specifies a concrete resource ('Weather forecast') and enumerates distinct output types: current conditions, 12-hour rain probability, multi-day forecast, and a direct rain answer. This clearly separates it from weather-related siblings like v1_weather_alerts, v1_sun, and v1_air_quality.

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 communicates clear use cases: general weather forecasting, rain probability, and a simple 'will it rain' query. It does not explicitly name alternative tools or state when not to use it, but the strong purpose framing makes the intended usage 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 readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful context by specifying the source (US National Weather Service), geographic limitation to the US, and the active-status of alerts, but it does not disclose response shape, pagination, rate limits, or whether a coordinate or place name is required.

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. It includes useful operational details like source, cost, and endpoint without excess. Minor redundancy exists between the title phrase 'Active weather alerts' and the description's 'Active NWS watches/warnings,' but it does not harm clarity.

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

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

For a simple read-only tool with no output schema, the description is mostly adequate: it states the resource, source, and scope. The main gap is that the schema lists zero required parameters, yet the description implies a location is needed; it should clarify the requirement between q and lat/lon, and what to expect when both or neither are provided.

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

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

Schema description coverage is 100%, so the parameters lat, lon, and q are already well documented with examples. The description contributes little to parameter meaning beyond saying the tool is for a 'US coordinate,' which aligns with lat/lon; the baseline of 3 is appropriate.

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

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

The description clearly identifies the tool's resource as 'Active NWS watches/warnings for a US coordinate,' which goes beyond the title by adding the specific alert type, source, and geographic scope. It distinguishes itself from the sibling v1_weather by focusing on alerts rather than general weather, 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 description implies usage: to retrieve active NWS watches/warnings for a location in the US, with 'US coordinate' hinting at lat/lon input. However, it does not explicitly state when to prefer this tool over v1_weather or mention any exclusions, prerequisites, or fallback logic.

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, so the safety bar is met. The description adds operational context: external Wikipedia REST source, GET endpoint, and per-call cost. It does not mention 404/error behavior for unknown titles, but this is minor for a safe read-only tool.

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

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

The description is a single sentence plus useful metadata (source, price, endpoint). It is front-loaded with the core purpose and contains no redundancy or filler.

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

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

For a simple 2-parameter tool with rich safety annotations and no output schema, the description is largely sufficient. It tells the agent what to pass, what to expect, and the source. A note about missing-title handling would improve completeness, but the absence is not critical.

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 already documented via descriptions and examples. The tool description adds no additional parameter-level meaning; it only restates the general resource ('Wikipedia page'). Baseline 3 applies.

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

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

Description states exactly what it returns: 'Lead summary/extract for a Wikipedia page'. It names the resource (Wikipedia page) and the specific outcome (lead/extract), and is distinct from the data-lookup siblings by naming Wikipedia as the source.

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, nor any exclusions. The description simply states what it does, leaving the agent to infer appropriate usage from the tool name and sibling context.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context like the data source, per-call price, and HTTP verb, but does not disclose response format details, pagination behavior, or country handling beyond what the schema already notes.

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, front-loaded sentence communicates the core operation, result type, source, cost, and endpoint with no filler. 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 two-parameter lookup with no output schema, the description provides the essential return concept and enough constraints to call it correctly, especially with the schema covering defaults. The only notable gap is the absence of explicit differentiation from sibling geocoding tools.

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

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

Schema description coverage is 100%, with both code and country having descriptions and examples, so the schema carries the parameter-documentation burden. The description does not add parameter-specific meaning beyond the generic postal-code-to-place transformation.

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

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

The description clearly states the verb-resource pair ('Postal code lookup') and the expected transformation ('ZIP/postal code → place(s) & coordinates'), which distinguishes it from address geocoding or reverse geocoding. It does not explicitly contrast itself with sibling tools, but the input type is sufficiently 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 implies when to use it: when the user supplies a postal code and wants places/coordinates. However, it gives no explicit exclusions or alternative tool routing, such as when v1_geocode or v1_reverse_geocode might be more appropriate, so the guidance is inferred rather than stated.

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