GoCreative Agent API

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

No annotations are present, so the description must convey key behaviors. It discloses the cost ($0.005–$0.05 USDC on Base per call) and a size limit (up to 5 brands), which are useful. However, it does not describe response structure, rate limits, or whether authentication is needed, leaving much to be inferred.

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

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

The description is very brief, with only two lines. It is front-loaded and wastes no words, but the typo 'brand' (for 'brands') and the hyphenated phrase 'avg-days-running' make it slightly rough. Still, cost info is efficiently included.

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

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

The tool appears simple, but the lack of output schema and parameter details makes the description incomplete for invocation. It doesn't explain what the response contains beyond the listed metrics, nor how the query string should be formatted. For a paid API, the description falls short of what an agent needs.

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

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

The schema has one parameter 'query_string' with no description (0% coverage). The description implies it accepts brand identifiers for up to 5 brands, but does not specify the format, delimiter, or exact expected values (e.g., brand names vs page IDs). This is insufficient for an agent to correctly construct the argument.

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 purpose is clear: it is a brand comparison API for Facebook Ads Library that provides side-by-side active-ad-count and avg-days-running benchmarks for up to 5 brands. This distinguishes it from siblings like adslibrary_search and adslibrary_page by focusing on multi-brand comparison, though the verb is implied rather than explicitly stated.

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

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

No explicit usage guidance is provided. There is no mention of when to prefer this tool over adslibrary_search or adslibrary_page, nor when not to use it. The 'up to 5 brands' hint implies a multi-brand use case, but no exclusions or alternative tool references are given.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It says what data is returned (ads currently running, plus a computed value), but it does not disclose rate limits, pagination, whether the computed value is an average or something else, or any authentication requirements. The truncation makes the behavior even less transparent.

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

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

The description is short but truncated ('plus a computed av'), which is a structural defect. The cost information is useful but could be considered extraneous to core purpose. A complete, front-loaded sentence would be more concise and effective.

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

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

Given the tool has no annotations, no output schema, and only one poorly documented parameter, the description should compensate by explaining the expected input format, what the computed value is, and what the response looks like. It does none of this, leaving critical gaps for a tool that is part of a large family of ad library tools.

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

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

The input schema has only one parameter 'arg' with no description and 0% schema description coverage. The description does not explain what 'arg' should contain (e.g., a Facebook Page ID, a page name, a URL), leaving the agent unable to construct a valid call without external knowledge.

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 that this tool monitors a competitor Facebook Page's currently running ads from the Facebook Ads Library, which is a specific verb+resource combination. However, the description is truncated mid-sentence ('plus a computed av' suggests 'average' or 'availability'), which slightly undermines clarity and fails to fully distinguish from siblings like adslibrary_search and adslibrary_compare.

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 'every ad one competitor Facebook Page is currently running' implies a use case of monitoring a single page's ads, but there is no explicit when-to-use guidance or comparison with adslibrary_search or adslibrary_compare. The cost note adds practical context but does not help choose between 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?

With no annotations provided, the description carries the full burden, but it only mentions computed days_running and an incomplete 'is_lik' field. It does not disclose read-only behavior, rate limits, error handling, or data freshness, and the truncated field name is confusing rather than clarifying.

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, but the first sentence is truncated mid-word ('is_lik') and lacks a terminating period, making it less polished. The cost information is useful but the overall structure is slightly awkward.

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

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

The tool has no output schema and no annotations, so the description should explain what a successful response contains. It does not mention pagination, result structure, or the meaning of the computed fields, and the truncation leaves the description incomplete. With only one parameter and simple purpose, this is a notable gap.

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

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

The schema has one parameter 'arg' with zero description coverage, so the description's phrase 'by keyword or brand' gives essential meaning to that parameter. However, it does not specify format, length limits, or whether multiple keywords can be combined, leaving gaps for the agent.

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 searches live Facebook & Instagram ads by keyword or brand, which is a specific verb+resource. However, it does not explicitly differentiate from the sibling tools adslibrary_compare and adslibrary_page, relying on the tool name 'search' to suggest that distinction.

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

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

There is no guidance on when to use this tool versus the alternative adslibrary_compare or adslibrary_page. The description does not mention any exclusions, prerequisites, or alternative tools, so the agent gets no help in choosing among the adslibrary siblings.

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

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

With no annotations, the description carries the full burden. It discloses the model (DeepSeek V3.1), cost range ($0.005–$0.05 USDC), payment method (USDC on Base), and that no API key is needed. It lacks details on rate limits, latency, or error handling, but is still informative.

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 brief yet comprehensive: a one-line summary, list of use cases, example call, and cost info. Every sentence adds value 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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers all key aspects: purpose, usage, cost, and example. It is complete enough for an agent to select and invoke correctly.

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

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

The input schema provides only the parameter name and type (string). The description adds substantial meaning: it explains that the 'prompt' parameter accepts any prompt, gives an example JSON, and describes typical use cases. Fully compensates for 0% 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 states it is an LLM completion tool using DeepSeek V3.1, lists concrete use cases (summarization, extraction, classification, drafting, reasoning), and distinguishes from siblings by specifying 'standard tier' and comparing cost to 'premium model tokens'.

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

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

Provides clear guidelines on when to use (e.g., for summarization, extraction) and mentions cost savings versus premium models. However, it does not explicitly contrast with ai_pro or ai_ultra, leaving some ambiguity about when to choose those alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost model (USDC per call, price range), the fact that no API key or signup is required, and provides an example call. It does not explain rate limits or response format, but for a simple 1-parameter tool, this is adequate transparency.

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 concise, using 5 sentences plus a compact example. It front-loads the purpose, then adds cost and usage guidance with no wasted words. Every sentence earns its place.

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

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

Given the tool's simplicity (1 parameter, no output schema), the description covers the essential aspects: what it does, when to use, cost, and example. It lacks explicit mention of output format (likely text) but is otherwise complete for an agent to invoke the tool correctly.

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

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

With 0% schema description coverage, the description must add meaning to the sole parameter 'prompt.' It only provides a generic example and implies the prompt should contain instructions. It does not specify length limits, content restrictions, or format expectations, thus adding minimal value beyond the schema.

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

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

The description clearly states the tool is for LLM completion using the frontier model Claude 4.5 Sonnet, listing capabilities (reasoning, analysis, synthesis, drafting). It distinguishes from sibling tools like ai_ask and ai_ultra by emphasizing it's the 'smartest model' for hard tasks.

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 explicitly says to use the tool 'when the task is hard enough to need the smartest model,' providing clear context. While it does not name alternative tools or state when not to use it, the guidance is strong enough for an agent to differentiate from cheaper/simpler siblings.

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

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

With no annotations, the description carries the full burden. It discloses cost range ($0.005–$0.05), payment method, and model identity, but does not mention latency, rate limits, or error handling. It provides moderate behavioral context.

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

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

The description is succinct and well-structured: a clear label, purpose, recommended use case, cost info, and an example. Every sentence adds essential information without 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 single-parameter LLM tool with no output schema, the description covers purpose, usage conditions, cost, and example. It does not specify the return format, but that is reasonably inferred for an LLM completion tool.

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

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

Schema coverage is 0% but description adds value through an example prompt and context (deep multi-step analysis). This helps the agent understand what kind of content to provide beyond the schema's simple 'string' type.

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

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

The description clearly states the tool performs LLM completion using Claude Opus 4.6 for the hardest agent tasks. It uses specific verbs ('LLM completion') and distinguishes itself from siblings (ai_ask, ai_pro) by highlighting it as the top-end reasoning model.

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 advises 'Use when model quality dominates cost,' providing clear selection criteria. It also mentions the payment method (USDC per call) and gives example usage, but does not explicitly state when not to use or list alternative tools.

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

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

With no annotations, the description carries the full burden of disclosing behavior. It does mention cost and the input type, but it omits rate limits, authentication requirements, response format, error cases, and the meaning of the truncated 'mon'. The phrase 'full product data' is vague and potentially misleading without further detail.

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 tool's primary purpose, which is good. However, the truncated 'mon' makes it look unfinished, and the cost line is appended without clear integration. It is concise but lacks polish and completeness.

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 single-parameter tool has no output schema and no annotations, so the description must explain both inputs and expected outputs. It lists some returned fields but does not describe the overall return structure, complete field set, or behavior on invalid inputs. The ambiguous 'mon' and missing usage context leave significant gaps for an agent deciding whether and how to invoke this tool.

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

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

The only parameter 'arg' has zero schema description coverage, so the description's mention of 'ASIN or URL' provides essential meaning. However, no examples, formats, or validation rules are given, leaving the parameter semantics only partially defined.

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

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

The description explicitly identifies this as an Amazon Product Detail API and states that it returns 'full product data by ASIN or URL', listing fields like brand, description, features, and bestseller_ranks. This distinguishes it from sibling tools like amazon_products_search and amazon_products_price, though the truncated 'mon' and absence of a strong action verb slightly reduce clarity.

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 provided on when to use this tool instead of alternatives such as amazon_products_search, amazon_products_price, or scrape_amazon. The cost information is useful operationally but does not address use cases, prerequisites, or exclusions.

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

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

Annotations are absent, so the description carries the burden. It discloses the returned data types (price, list price, stock, rating) and mentions 'lightweight', but does not explain rate limits, authentication, caching, error handling, or response format. The cost per call is a useful behavioral detail.

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 main purpose, but it is truncated mid-sentence ('For price-monito'), leaving an incomplete use-case phrase. The cost line is separate and clear, but the overall structure suffers from the cut-off text.

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

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

The tool is simple (one param, no output schema, no annotations), and the description covers the basic purpose and input type. However, it lacks guidance on alternatives, expected response structure, and edge cases, such as invalid ASIN or unavailable product data.

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 0% with a single generic 'arg' parameter. The description adds meaning by stating the input can be an ASIN or URL, but does not specify format, auto-detection, or any constraints. This partially compensates for the schema's lack of detail.

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

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

The description clearly states the tool's function: checking Amazon prices with specific data points (current price, list price, stock status, rating) and input methods (ASIN or URL). It differentiates from siblings like amazon_products_asin and amazon_products_search by focusing on price checking rather than product details or search.

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

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

No explicit guidance on when to use this tool versus alternatives. The truncated phrase 'For price-monito' hints at price monitoring but does not provide clear context, exclusions, or comparisons with related tools such as amazon_products_search or scrape_amazon.

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

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

No annotations are provided, so the description carries full burden. It discloses cost per call and enumerates returned data fields, which is useful. However, it does not explicitly state read-only behavior, limitations, or failure modes, and it stops at 'offers coun' with a typo, cutting off the sentence.

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 brief and front-loaded with the core purpose, followed by a cost note. However, the trailing 'offers coun' reads as incomplete and reduces polish. Otherwise, every sentence serves a purpose.

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

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

With no output schema, no annotations, and a single vague parameter, the description should provide more context. It lists output fields but lacks result structure, pagination behavior, error scenarios, or input examples. The cost disclosure is helpful but insufficient to compensate for these 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 has one undocumented parameter 'arg' with 0% coverage. The description implies the arg is a keyword but never explicitly states 'arg=keyword'. It lacks examples, formatting, or length constraints, leaving the agent to infer the parameter's 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 states 'search Amazon products by keyword' with specific returned fields (price, list price, star rating, review count, offers count). This clearly distinguishes it from sibling tools like amazon_products_asin (specific product lookup) and amazon_products_price (price check), making the verb+resource+scope explicit.

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

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

The description implies usage for keyword-based Amazon product searches but does not explicitly state when to use this tool versus alternatives like scrape_amazon or amazon_products_asin. The cost note provides practical consideration but no comparative guidance or exclusions.

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

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

With no annotations, the description adds value by disclosing cost per call and providing an example input. However, it does not mention read-only nature, rate limits, data freshness, or error handling.

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

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

The description is extremely concise, two sentences plus an example and cost. Every sentence adds unique value, with no wasted words.

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

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

Although the output format is not detailed, the description lists the security signals checked, which gives sufficient context for an AI agent to understand the tool's output. The simplicity (1 param) reduces the need for more detail.

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

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

The schema has no description for the single parameter (0% coverage), but the description explains the format ('owner/repo') and gives an example, fully compensating for the schema gap.

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 'Audit a GitHub repo for security signals' with specific signals listed, and distinguishes from sibling tools like lookup_github by focusing on security auditing for supply-chain risk.

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 specifies the use case ('OSS supply-chain risk scoring') and provides an example call, but does not explicitly mention when not to use or alternative tools.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context about the per-call cost ('$0.005–$0.05 USDC on Base'), the symbol limit ('up to 50'), and the input format ('comma-separated'). However, it doesn't detail error handling or response structure, so it's minimally adequate but not rich.

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 at three sentences, front-loaded with the 'BULK' indicator and core data fields. Every clause adds value—use cases, efficiency gain, and cost—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?

Given the lack of output schema and annotations, the description covers the essentials: data content (prices, 24h change), input format (comma-separated symbols), limits (50), cost, and use cases. It doesn't explicitly describe the output format or error behavior, but for a simple data query, this is largely sufficient for agent selection.

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

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

The single 'arg' parameter is completely undescribed in the schema (0% coverage), so the description must compensate. It does so by specifying that it expects 'crypto symbols' in a 'comma-separated' format, which is sufficient for basic invocation. It could be improved with an example, but the core meaning is clear.

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

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

The description uses a strong verb phrase 'live USD prices + 24h % change' and specifies the resource ('up to 50 crypto symbols') and scope ('BULK', 'comma-separated'). It clearly distinguishes this batch tool from individual lookup tools in the sibling list (e.g., lookup_crypto) by emphasizing bulk behavior.

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

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

It explicitly states target use cases ('For trading bots, portfolio trackers, or dashboards') and explains when this bulk call is advantageous ('replaces up to 50 individual lookups'). However, it doesn't name a specific alternative tool for single-symbol queries, though the context implies using a non-bulk lookup.

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

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

With no annotations, the description partially carries the transparency burden. It discloses the record types, the 25-domain limit, and the cost range ($0.005–$0.05 USDC on Base), but it does not describe output format, error handling, or behavior for invalid domains. The truncated sentence 'For devops agents, email' adds confusion rather than behavioral detail.

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 main purpose, and includes cost information efficiently. However, the phrase 'For devops agents, email' appears truncated and breaks the flow, making the overall structure feel incomplete and slightly unpolished.

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?

Without an output schema or annotations, the description should explain what the response contains. It does not describe the return format, such as whether results are per-domain or a combined object, nor how errors or invalid domains are handled. The cost and limit are useful, but the missing return/error details make the description incomplete for a practical agent.

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

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

The input schema has only one param 'arg' with 0% coverage, so the description must compensate. It does so by explaining that the argument should be a comma-separated list of domains (up to 25), which provides essential formatting guidance beyond the bare schema. However, it does not specify the exact syntax for optional spaces or protocol prefixes.

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

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

The description clearly states that the tool resolves A/AAAA/MX/TXT/NS DNS records for up to 25 domains in one call, with a comma-separated format. This specific verb+resource+scope distinguishes it from sibling tools like lookup_dns and bulk_whois.

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

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

The description does not provide explicit guidance on when to use this tool versus alternatives. It implies bulk usage via the 'BULK:' prefix and the 25-domain limit, but there is no mention of when to prefer this over lookup_dns or other single-domain tools, nor any exclusions or prerequisites.

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

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

With no annotations, the description compensates by disclosing input limits, comma-separated format, syntax and live MX checks, deliverable vs invalid flagging, and per-call cost. It does not, however, describe the exact response shape or error handling.

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

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

The description is brief and front-loaded with the bulk indicator, providing essential details in two sentences. The text appears truncated ('inva'), which slightly hampers polish but does not obscure the core meaning.

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 single-parameter tool with no output schema, the description covers the tool's purpose, input format, limits, and cost. It does not detail the return structure beyond 'flagging deliverable vs invalid', but this is sufficient for basic usage.

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 only defines 'arg' with no description, but the text clarifies that it expects a comma-separated list of up to 50 emails, making the parameter's format and limit explicit.

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 validates up to 50 emails per call, using syntax and live MX checks, and distinguishes itself from single-email validators via the 'BULK:' prefix.

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 'BULK:' prefix and explicit 'up to 50 emails in one call' establish when to use the tool for multi-email validation, but it does not explicitly name the single-email alternative (lookup_email_validate) or specify exclusions.

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

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does disclose the cost range and the up-to-20-repos limit, which is useful operational context. However, it does not specify error handling for invalid repos, behavior when exceeding 20 repos, auth requirements, or response format. The dangling 'F' also suggests potentially missing behavioral details.

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

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

The description is compact and front-loaded with 'BULK' to signal the use case, followed by the data fields and input format. The cost information is extra but relevant. However, the dangling 'F' creates a fragment that disrupts readability, and the cost line might be considered secondary rather than primary behavioral guidance.

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 should clarify what the response contains; it does list the metadata fields (stars, language, license, dates, forks), partially addressing this. However, it misses details about over-limit behavior, error responses, pagination, and whether private repos are supported. Given the single-parameter design, the description is adequate for basic use but leaves gaps for edge cases.

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

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

The input schema provides only a single 'arg' string with no description (0% schema coverage). The description compensates well by explaining the expected format: comma-separated owner/repo pairs, and the maximum of 20 repos. This gives an agent the essential syntax information that the schema lacks. It doesn't mention whitespace handling or alternative separators, but the core semantics are clear.

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 it retrieves GitHub repo metadata (stars, language, license, dates, forks) for up to 20 repos in a single call, with the input format (owner/repo,owner/repo) explicitly shown. This distinguishes it from sibling tools like lookup_github or enrich_github as the bulk alternative. The trailing 'F' appears to be a truncated word but does not obscure the core purpose.

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

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

The description implies the tool is for batch metadata retrieval ('up to 20 GitHub repos in one call') and notes the cost efficiency, but it does not explicitly state when to choose this over single-repo tools like lookup_github or audit_github, nor does it mention exclusions or prerequisites. The usage context is implied rather than explicitly guided.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context by stating the call limit (50 IPs) and the cost range ($0.005–$0.05 USDC on Base), but it does not disclose output format, error handling, or authentication needs. The provided cost and limit are valuable, yet incomplete for a tool with no annotations.

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

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

The description is concise, covering purpose, limit, cost, and target audience in two sentences. However, the first sentence ends abruptly with 'agen' (likely 'agencies'), indicating a truncation or typo, which disrupts readability and professionalism. The structure is otherwise efficient.

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

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

For a simple one-parameter tool, the description provides core information: purpose, fields returned, IP limit, and cost. But it lacks the output schema/shape (e.g., keys per IP), input validation details (e.g., IPv4 vs IPv6, invalid IP handling), and any mention of error cases. Given the low complexity and missing output schema, the description is not fully complete but covers the essentials.

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

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

The input schema has a single 'arg' string parameter with zero description coverage. The description compensates by specifying that the argument is a comma-separated list of IPs, which is essential for correct usage. It could be more explicit by naming 'arg' directly, but the meaning is conveyed: the tool takes a comma-separated string of IPs.

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

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

The description clearly states the tool performs bulk IP geolocation with specific fields (country, city, ISP, lat/lon) for up to 50 IPs in one call. It distinguishes itself from sibling tools like 'ip' (single IP lookup) and 'bulk_crypto' (different data type), making the purpose unmistakable.

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

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

The description implies use for bulk IP geolocation and mentions 'for fraud-detection agencies,' providing some context. However, it does not explicitly say when to use this tool instead of alternatives like 'ip' or 'lookup_ip', nor does it give exclusion criteria or prerequisites.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds useful context by stating the call is bulk (up to 20 domains), returns specific fields, and includes a cost range. As a WHOIS lookup, the read-only nature is implied, though not explicitly stated, and no error handling or rate limits are mentioned.

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

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

The description is two sentences: the first covers functionality, limit, and return fields; the second covers cost. There is no fluff, and the most important information is 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?

Given the tool's simplicity (one parameter, no output schema), the description is complete: it defines input format, input limit, output fields, and cost. It gives an agent enough context to select and invoke the tool correctly without requiring external information.

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 is bare (one required string parameter 'arg' with no description, 0% schema coverage). The description compensates by specifying that the parameter is a comma-separated list of domains, limited to 20, which is essential for proper usage. It could be improved with an example or format elaboration, but the core meaning is conveyed.

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: performing WHOIS lookups for up to 20 domains in a single call, with comma-separated input. It details the return fields (registrar, created/expiry dates, nameserver) and differentiates itself from single-domain WHOIS tools like lookup_whois and other bulk utilities.

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 context: use this when you need WHOIS data for multiple domains at once (up to 20) rather than a single lookup. However, it doesn't explicitly mention when not to use it or name alternatives, though the 'BULK' prefix and limit make the intended use 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?

No annotations are provided, so the description bears full responsibility. It discloses the cost range ($0.005–$0.05 USDC on Base) and gives an example call, but does not discuss data freshness, rate limits, or potential limitations. The behavioral disclosure is adequate but not exhaustive.

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

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

The description is three concise sentences plus an example and cost line. It front-loads the core purpose, then provides use cases, an example, and pricing—all without unnecessary words.

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

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

Given the tool has only one parameter, no output schema, and no annotations, the description provides a clear purpose, usage context, example, and cost. It is sufficient for an agent to invoke the tool correctly, though mentioning the output structure would improve completeness.

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

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

The sole parameter 'domain' has no schema description (0% coverage). The description compensates by stating 'for any domain' and showing an example ('stripe.com'), implying the input format. This adds significant meaning beyond the minimal schema.

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

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

The description clearly states the tool's function: combining domain intelligence (DNS/WHOIS/TLS) with company firmographics for any domain. It also lists specific use cases (sales prospecting, KYB, enrichment) and distinguishes itself from sibling tools like bundle_domain_360 and bundle_kyb_360 through its fusion of data types.

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

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

The description explicitly states when to use the tool ('for sales prospecting, KYB, and enrichment agents') and provides a concrete example (domain: stripe.com). While it doesn't explicitly state when not to use it or compare directly with siblings, the context is clear enough for most agents.

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

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

No annotations are provided, so the description must carry the full burden. It mentions cost but does not disclose rate limits, authentication requirements, idempotency, or data freshness. Essential behavioral traits for a data-fetching tool are missing.

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 concise: one line for purpose, one line for inclusions, an example, and cost. Every sentence adds value, and the purpose is 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?

Given no output schema, the description lists the types of data included (market data, spot price, sentiment, etc.), giving a good idea of the output. Cost and example are provided. It lacks format details but is sufficient for an agent to understand the tool's capabilities.

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 coverage is 0%, so the description compensates by stating 'for any CoinGecko coin id' and providing an example ('bitcoin'). This adds crucial semantic meaning that the schema alone lacks.

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 a comprehensive crypto snapshot including market data, spot price, Fear & Greed sentiment, global context, and trending. It specifies it works for any CoinGecko coin id, and contrasts with sibling tools like lookup_coingecko that likely give simpler 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 explicitly says 'For trading, research, and portfolio agents,' indicating appropriate use cases. It provides an example call but does not explicitly state when to avoid this tool or suggest alternatives, though the context of one-call comprehensiveness is implied.

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

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions the data returned (profile + repos) and cost, but does not state if authentication is required, rate limits, or confirm it is read-only. The cost disclosure adds some 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?

Three sentences (plus one for cost) that front-load the purpose, provide an example, and add cost information. No fluff; every sentence serves a purpose.

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

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

For a tool with one parameter, no output schema, and no annotations, the description is fairly complete: it states the purpose, target users, example, and cost. The only gap is lack of output format, but the tool's simplicity makes this acceptable.

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

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

The schema has 0% description coverage, but the description implies the 'username' parameter is a GitHub username via the example 'torvalds'. This adds meaning beyond the schema, though an explicit note would be stronger.

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 it provides 'Developer/GitHub intelligence: account profile + top repositories for any username.' This specifies the verb (intelligence/data retrieval), resource (GitHub developers), and scope (profile + top repos), distinguishing it from siblings like enrich_github or lookup_github_user.

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 explicitly targets 'recruiting, dev-rel, and technical-sourcing agents' and provides an example call. While it gives clear context for when to use, it does not mention when not to use or name alternatives among the many sibling tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost ($0.005–$0.05 per call) and lists the types of data returned (clearances, recalls, adverse events). However, it does not state if the operation is read-only or describe response format, leaving some gaps.

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

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

The description is three concise sentences plus a cost line and example. It front-loads the core purpose and avoids redundancy. Every statement adds value.

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

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

The description lists data types (clearances, recalls, adverse events) but does not describe the response format or structure. Without an output schema, more detail on what the agent can expect would improve completeness. The example only shows input.

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 0%, but the description compensates with an example call ('name': 'insulin pump') and mentions 'for any device or manufacturer'. This clarifies that the single 'name' parameter accepts a device or manufacturer name. The example adds meaning beyond the schema's empty description.

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 it is an 'FDA medical-device risk dossier' providing '510(k) clearances + recalls + adverse events (MAUDE)'. It targets specific agents (medtech, procurement, compliance) and includes an example call. This distinguishes it from siblings like bundle_drug_360 for drugs.

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

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

The description explains it is for 'any device or manufacturer' and targets 'medtech, procurement, and compliance agents'. The example call clarifies the input format. While it does not explicitly exclude alternatives, the context implies appropriate use 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?

No annotations provided, so description carries full burden. It discloses cost range and output categories, but does not mention read-only nature, error handling, or response format. Adequate but not thorough.

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?

Concise, front-loaded with purpose, then list of components, user groups, example, and cost. No unnecessary sentences, though cost line could be omitted.

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

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

Given no output schema and multiple data categories, the description provides high-level categories but lacks details on response structure, error cases, or handling of missing data. Adequate for a single-parameter tool.

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

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

Only parameter is 'name' with 0% schema description. The example call {'name': 'Stripe'} adds real-world context, but no details on format or meaning beyond the obvious.

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?

Specifically states 'Corporate due-diligence dossier in ONE call' and enumerates the exact data types (firmographics, OFAC, risk score, KYB, legal exposure). This clearly defines verb+resource and distinguishes from siblings like bundle_company_360.

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?

Identifies target users: 'For investment, KYB, compliance, and corporate-intelligence agents.' Also implies efficiency with 'in ONE call'. Lacks explicit when-not or alternative comparisons.

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?

Discloses what data is returned and cost, but no annotations exist; missing details on rate limits, data freshness, or 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?

Three sentences, front-loaded with purpose, example, and cost. No verbosity.

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?

Covers key aspects (returned components, use cases) but omits output structure or error handling; reasonable for a simple query 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?

Adds example and context ('for any domain') to the single parameter, but with 0% schema coverage, could specify format or constraints.

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 it bundles WHOIS, DNS, and TLS for any domain, distinguishing it from individual lookup tools like lookup_whois, lookup_dns, etc.

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

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

Provides use cases (security review, vendor due diligence) but lacks explicit guidance on when not to use or alternatives (e.g., for just DNS, use lookup_dns).

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

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

The description discloses the cost and components of the dossier, but it lacks details on output format, rate limits, authorization requirements, or potential limitations. With no annotations present, more behavioral context would improve transparency.

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 concise: one sentence defining the tool, an example call, and cost information. Every part adds value, and the key information is 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?

The tool has no output schema, so the description should explain what the response contains. It mentions the dossier includes FDA label, approval history, and clinical trials, but does not describe the format or structure. Combined with the one parameter and cost info, it is adequate but not fully complete.

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

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

The input schema has only one parameter (name) with 0% coverage, meaning no schema descriptions. The description compensates by providing the example call with 'name': 'metformin', which clarifies that the parameter accepts a drug name. However, it does not specify formatting or case sensitivity.

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

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

The description clearly states that this tool provides a 'drug intelligence dossier' comprising FDA label, approval history, and clinical trials for any drug. It also specifies the target audience (pharma, healthcare, research agents) and gives an example, making it distinct from sibling bundle tools like bundle_company_360.

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

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

The description indicates the tool is for pharmaceutical, healthcare, and research contexts, providing clear domain guidance. However, it does not explicitly mention when not to use it or list alternative tools for specific drug lookups, though the sibling set includes many non-drug tools.

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

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It mentions cost and hints at enrichment but does not describe side effects, rate limits, or response structure. The cost disclosure is useful but not sufficient for full transparency.

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

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

The description is concise: two sentences, an example, and a cost line. Information is front-loaded with the core purpose, making it efficient for an agent 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?

Given the simple input (single string parameter) and absence of output schema, the description adequately explains the tool's value and cost. While it does not describe return values, the purpose is clear enough for an agent to decide invocation.

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

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

Schema coverage is 0%, so the description must add meaning. The example 'jane@stripe.com' implies a valid email format, and the phrase 'any email address' clarifies scope. However, no explicit parameter description is given, and the example alone is basic.

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

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

The description clearly states the tool performs 'deliverability validation + provider/domain enrichment' for email addresses, and lists specific use cases like lead-gen, CRM hygiene, and fraud-screening. This specificity and resource identification make the purpose unmistakable and distinguish it from simpler lookups.

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

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

The description gives explicit use cases but does not provide guidance on when to avoid this tool or how it compares to siblings like 'lookup_email_validate'. While the term 'intelligence' implies a more comprehensive analysis, the lack of contrast with alternative tools leaves room for uncertainty.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost ($0.005–$0.05 USDC) and that it is a bundle, but does not disclose behavioral traits such as whether it is read-only, what the call does beyond returning data, rate limits, or required authorization. The cost and 'premium bundle' tag add some context, but the operation remains opaque.

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

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

The description is concise, with two sentences that add relevant information: the tool's purpose and its cost. It is front-loaded with the main function. However, the brevity contributes to under-specification, so it earns a 4 rather than 5.

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 bundle tool with no annotations and no output schema, the description needs to explain the input parameter, return value, and usage context. It only states the purpose and cost, leaving significant gaps. The tool likely returns grant data, but the single parameter is unexplained and no output expectations are given.

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

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

The schema has a single parameter 'arg' with no description, and schema coverage is 0%. The description does not explain what 'arg' should contain. The phrase 'for any sector' hints that the argument may be a sector, but this is not explicit, leaving the parameter completely under-specified.

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 grant-funded buying-intent data, specifically mentioning 'R&D / grant-funded buying-intent' and 'recent NIH grant awards'. This gives a specific resource and purpose, distinguishing it from other bundle tools like bundle_sales_intent, though it lacks an explicit verb like 'get' or 'search'.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'premium bundle' and 'in ONE call' but does not mention alternatives, prerequisites, or specific use cases. There is no when-to-use or when-not-to-use information.

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

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

With no annotations provided, the description bears the full transparency burden. It usefully discloses the cost range ($0.005–$0.05 USDC per call) and the two data sources, but it does not describe the output format, what the fused result contains, failure behavior, or latency. The truncation ('fused into...') directly obscures the core behavioral outcome, leaving a significant gap.

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

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

The description is short and front-loaded with the purpose statement followed by cost, and every line carries relevant information. However, the main sentence cuts off abruptly after 'fused into', leaving the structure incomplete and the thought unfinished, which is a structural defect rather than concise writing.

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 absence of annotations, an output schema, and any parameter descriptions, the description alone must fully orient the agent. It covers the input format and pricing, but fails to explain the fused output, ideal use cases, or how it compares to the large set of sibling weather/earthquake/bundle tools. The truncation compounds these gaps, making the description incomplete for a 1-param tool.

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

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

The schema provides a single generic 'arg' string parameter with 0% description coverage, so the description's 'any lat,lon' is the only semantic signal for constructing a call. This tells the agent the expected value format, which is valuable, but it lacks precision such as coordinate ranges, decimal format, or examples, so it only partially compensates for the schema gap.

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 a bundled hazard product combining 'recent nearby earthquakes (USGS)' and 'active NWS weather alerts' keyed to a 'lat,lon' location. This distinguishes it from other bundle_*_360 siblings by naming specific data sources. However, the sentence is truncated mid-phrase ('fused into' with no object), leaving the final deliverable ambiguous and preventing a 5.

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

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

No guidance is given on when to choose this premium bundle versus calling individual sibling tools like data_earthquakes, lookup_usgs_earthquakes, weather, or weather_alerts separately. The 'premium bundle' label and cost hint at a paid convenience option, but there is no explicit when-to-use, comparison, or exclusion guidance.

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

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

With no annotations provided, the description carries full burden. It discloses meaningful behavioral traits not in schema: the premium bundled nature, a concrete cost range, and the payment network (Base/USDC). It does not disclose return format, error handling, or invalid-input behavior, but for a simple IP lookup these are optional gaps.

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

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

The description is short and packs useful info (data types, use case, cost) with the key content front-loaded. However, the line 'For fraud-screening,' is a grammatically incomplete dangling fragment that appears to be a templating/formatting artifact, which undermines structural quality.

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

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

For a simple one-parameter tool with no output schema or annotations, the description covers the core essentials: what data it returns, its use case, and cost. It lacks explicit input format guidance and any description of the response shape, though the listed data types (network/ASN + geolocation) give a basic expectation of output.

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 0% and the sole parameter 'arg' has no schema-level description. The description partially compensates by saying 'for any IP address', implying arg expects an IP address, but it does not specify IPv4/IPv6 support, format constraints, or fallback behavior, leaving the agent to infer the parameter meaning.

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

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

The description clearly states the tool bundles network/ASN details + geolocation for any IP address into one call, with 'premium bundle' signaling it is a comprehensive offering. It identifies the resource (IP address) and the data types returned, though the verb is only implied and it does not explicitly name alternate tools for basic lookups.

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

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

The description gives an explicit use case ('For fraud-screening') and the 'premium... fused into ONE call' phrasing implies it is for when a combined lookup is needed. However, it names no alternatives or exclusions, despite numerous sibling IP tools (lookup_ip, lookup_ipinfo, data_ip_threat, ip, bulk_ip) existing.

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

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

No annotations are provided, so the description carries the full burden. It mentions the tool aggregates data in 'ONE call' and gives cost transparency, but lacks details on read-only nature, error handling, data freshness, or any side effects. The cost disclosure adds value, but overall behavioral traits are minimally addressed.

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

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

The description is concise: a clear purpose statement, list of included data, use cases, example, and cost. No redundant sentences; information is front-loaded and well-organized. Every sentence adds value.

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

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

Given no output schema, the description explains what data the bundle includes (sanctions, risk score, etc.), which is sufficient for understanding output scope. However, it does not describe the output structure or how results are formatted, which would aid agent usage. Still, for a single-parameter tool, it is reasonably 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?

With 0% schema description coverage, the description compensates by naming the single parameter 'name' and providing an example (Stripe), clearly indicating it expects a company name. No validation or format details, but the simplicity of one string parameter makes this adequate.

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 a KYB/counterparty-risk verdict for a company, listing specific components (sanctions, PEP, risk score, etc.) and use cases (onboarding, underwriting, compliance). It distinguishes itself from sibling tools that target other entities (e.g., bundle_crypto_360).

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 explicitly recommends use cases like customer/vendor onboarding and compliance. It provides an example call and cost range. However, it does not explicitly exclude scenarios where simpler tools might suffice, which would enhance guidance.

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

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

No annotations exist, so the description carries the burden. It adds useful behavioral context such as 'premium', 'live', 'fused', and a pricing range, but omits response shape, error behavior, authentication requirements, and side-effect 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 compact and front-loaded: it states the product name, core contents, scope, and pricing in two short lines. No filler or repetition exists.

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, no annotations, and a single undocumented parameter, the description gives the value proposition and cost but leaves the return structure and input format implicit. It is adequate for a simple tool but not fully complete.

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

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

The schema provides only an undocumented `arg` string with 0% description coverage. The phrase 'for any ticker' implies that `arg` is a ticker symbol, which partially compensates, but formats, examples, and accepted values are not specified.

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

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

The description names the bundle and its exact data composition: live stock quote, recent insider transactions, and material 8-K events for any ticker. This is specific and distinguishes it from sibling bundles like bundle_crypto_360 or bundle_company_360.

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

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

It clearly communicates the market-data context and the fused data types included, making it obvious when this tool would be relevant. However, it does not explicitly state when not to use it or mention alternative tools such as bundle_company_360.

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

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

With no annotations, the description must explain behavior, but it only states it is an 'ONBOARDING gate' and mentions 'OFAC/SDN' sanctions, plus a cost range. It does not disclose what the tool returns, whether it is a pass/fail check, or any side effects, rate limits, or data sources.

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

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

The description is concise and front-loaded with the core purpose, and includes useful cost information. However, it is a truncated fragment ('OFAC/SDN sanct') that ends mid-word, suggesting a formatting or completeness issue.

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 minimal schema (1 unnamed parameter), no annotations, and no output schema, the description must compensate by explaining the tool's inputs and outputs. It fails to describe what the gate returns or how the screening result is presented, leaving major gaps.

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

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

The single 'arg' parameter has no schema description (0% coverage), and the tool description only implies the input relates to 'any person or company' without specifying the expected format, structure, or example. This is insufficient for an agent to construct a valid invocation.

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 a specific function: a 'counterparty ONBOARDING gate for any person or company in ONE call' with OFAC/SDN sanctions screening. This conveys a clear action and target, and the focus on onboarding distinguishes it from other bundle tools, though the truncated phrase 'sanct' leaves the exact purpose slightly incomplete.

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

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

There is no explicit guidance on when to use this tool versus alternatives like bundle_company_360 or bundle_kyb_360. The description only mentions it is a 'premium bundle' and gives cost, but no selection criteria or exclusions.

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

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

No annotations are provided, so the description must carry the full burden. It discloses cost, which is a behavioral trait, but does not mention whether the operation is read-only, what the response looks like, or how failures are handled. This leaves significant behavioral ambiguity.

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

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

The description is concise and front-loaded, with the core function in the first sentence and cost in the second. The incomplete trailing 'in' is a minor structural flaw, but overall it is efficient and information-dense.

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

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

With no output schema and no annotations, the description must explain the tool's behavior and results. It explains the purpose and cost but omits the output structure, error behavior, and parameter format. For a multi-registry lookup, this level of detail is insufficient.

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

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

The input schema has a single 'arg' parameter with no description (0% schema coverage). The description adds meaning by indicating that the parameter is a package name, which is essential. However, it lacks details on format, examples, or constraints, so the semantics are only partially clarified.

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 function with a specific verb ('resolves') and resource ('a package name'), naming six package registries, which distinguishes it from single-registry alternative tools like lookup_npm or lookup_pypi. The 'premium bundle' label reinforces its aggregating nature.

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 a use case (cross-registry package resolution) but does not explicitly state when to use this tool versus individual registry lookups. There is no mention of prerequisites, exclusions, or alternative tools, so guidance is only 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?

No annotations are present, so the description carries full responsibility. It discloses cost and mentions GLEIF firmographic enrichment, but omits critical behavioral details such as data sources, output format, latency, or side effects. The truncated text also limits transparency.

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 brief and front-loads the key selling point, but it is visibly truncated (ends mid-sentence) and mixes cost information without a clean structure. It is not a model of concise completeness.

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 bundled nature and lack of output schema or annotations, the description should explain what is included (firmographics, maybe more) and what the single argument should be. It fails to provide enough context for an agent to invoke this effectively.

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 defines one string parameter 'arg' with zero description coverage. The description does not explain what 'arg' should contain (e.g., company name, domain, LEI), leaving the agent to guess. This is a critical gap for a single-parameter tool.

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

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

The description states a clear purpose: complete B2B prospect vetting for any company in one call, with firmographic enrichment via GLEIF. However, it is truncated after 'GLEIF legal entity +' and does not differentiate from sibling bundle tools like bundle_company_360 or bundle_diligence_360.

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 provided on when to use this tool versus alternatives. The phrase 'ONE call' implies efficiency but there is no mention of scenarios, prerequisites, or exclusions, making it hard for an agent to select this over similar bundled tools.

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

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

No annotations provided, so description carries full burden. Describes data sources and cost ($0.005-$0.05 per call). Does not specify read-only nature or pagination, but pricing and source list add transparency.

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

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

Three sentences, an example, and cost. Efficiently front-loaded with key information, no redundant or verbose phrasing.

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

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

Given no output schema or annotations, description covers purpose, data sources, example, and cost. Lacks return format details but sufficient for an agent to decide on usage.

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 0%, so description must compensate. It explains the 'term' parameter with context (sector/term) and example ('medical devices'). Does not specify format or allowed values, but provides enough for basic understanding.

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 it retrieves regulatory and legal landscapes for a sector/term, listing specific data sources (Federal Register rulemaking, case-law, FDA device signals, federal grant signals). It distinguishes itself from sibling bundle_* tools by focusing on regulatory domain.

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

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

Explicitly targets policy, legal, compliance, and risk-monitoring agents. Provides an example call. Does not explicitly state when not to use, but the specificity and sibling names (e.g., bundle_company_360) imply differentiation.

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

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

With no annotations, the description bears full burden. It discloses cost range and example format, but omits authentication needs, rate limits, response size, pagination, and error handling (e.g., for nonexistent repos). The core behavior is clear but not deeply transparent.

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

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

Three efficient sentences without redundancy: purpose, example, cost. Front-loaded with the core value proposition, making it quick for an agent 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?

Given no output schema, the description should elaborate on the returned data. 'Repository profile + latest releases' is vague; it lacks field details, structure, or any mention of error responses. Adequate for a simple tool but incomplete relative to its complexity.

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

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

Schema coverage is 0%, and the description only clarifies parameter names via the example call. It does not explain required formats (e.g., exact GitHub username vs. case-insensitive), validation rules, or parameter relationships. The example helps but is insufficient for a no-description 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?

Clearly states it provides 'GitHub repo intelligence: repository profile + latest releases' for any owner/repo. This verb-resource pairing distinguishes it from sibling tools like enrich_github, lookup_github, and lookup_github_releases by bundling two data types into one call.

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?

Explicitly targets 'dev-tools, security, and OSS-monitoring agents' and provides an example call. While it implies the use case and owner/repo scope, it does not mention when to avoid using it (e.g., for very frequent polling) or suggest alternatives like lookup_github_releases for fewer fields.

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

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

With no annotations provided, the description bears the full burden of revealing behavior. It discloses cost ($0.005–$0.05) and the combination of sources, but omits details like response format, whether it returns a synthesized summary, or any error conditions. It provides some context but not rich behavioral transparency.

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, stating the purpose in the first sentence and cost in a separate line. It is nearly concise, but the truncated 'F' at the end of the first sentence introduces awkwardness and suggests incomplete editing, preventing a top score.

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

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

For a bundling tool with no output schema and no annotations, the description should clarify what the user gets back. It explains the input (a topic) and the data sources, but not the output structure or whether results are synthesized. The cost is helpful, but overall the description leaves significant guessing about the returned artifact.

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

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

The input schema has one required 'arg' with no description, and schema description coverage is 0%, so the description must compensate. It specifies the argument is 'for a topic', adding meaning beyond the bare schema. However, it does not clarify the expected format (e.g., free-text string, keywords) or provide examples, so it only partially compensates.

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

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

The description uses the specific verb 'fuses' and names three concrete sources (arXiv papers, Wikipedia summary, Open Library books) for 'a topic', clearly distinguishing this from other bundle_*_360 tools. The resource is well-defined despite the truncated 'F', making the tool's purpose 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 phrase 'for a topic' implies the single argument is a research topic, giving some context on when to use it. However, there is no explicit guidance on when to choose this over individual lookup tools (e.g., lookup_arxiv, lookup_wikipedia) or alternative bundles. It stops at implied usage without 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?

No annotations are provided, so the description must reveal behavioral traits. It mentions cost and includes an example call, but fails to disclose what the tool does beyond bundling multiple checks, such as data sources, error handling, or output structure.

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

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

The description is four sentences covering purpose, usage context, example call, and cost. Every sentence adds necessary information without 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?

Given the single required parameter, no output schema, and no annotations, the description is reasonably complete. It specifies input, purpose, and target users, though it omits output details and potential limitations.

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

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

The schema has one parameter 'name' with 0% description coverage. The description adds value by indicating that 'name' refers to a company name and provides an example. However, it does not specify format or constraints, so it partially compensates.

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

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

The description clearly states that the tool provides 'OFAC sanctions screen + entity risk score + KYB/vendor dossier for any company,' which is a specific verb+resource combination. It distinguishes itself from siblings by focusing on risk/compliance bundles.

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 explicitly targets 'compliance, onboarding, and vendor-risk agents,' providing clear context for when to use. Although it does not mention alternatives or when not to use, the usage guidance is clear and sufficient.

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

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

No annotations provided, so description must disclose behavioral traits. It mentions the type of data returned and cost, but does not disclose data freshness, accuracy, limitations, or potential side effects. Adequate but not thorough.

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?

Description is brief and to the point, including an example and cost information. While not highly structured, it earns its space without repetition.

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

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

For a single-parameter tool with no output schema, the description adequately explains the output signals and use cases. It does not mention pagination or result limits, but is otherwise complete for its simplicity.

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

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

Schema coverage is 0%, but the description adds an example keyword ('fintech') and explains what the tool returns. However, it does not describe valid keyword formats or constraints, leaving the agent to infer from context. Partial compensation.

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 'sales-intent signals' by industry/keyword, listing specific signal types (funding, contracts, grants, FDA) and use cases (lead-gen, prospecting, sales agents). It includes an example call, making the purpose unmistakable.

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?

Description targets specific use cases and includes an example, but does not explicitly state when to use versus alternatives or when not to use. Among many sibling tools, no direct differentiation is provided, though the niche is clear enough for an informed agent.

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

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

With no annotations, the description should disclose behavioral traits. It only mentions cost and an example, omitting details on rate limits, data freshness, error handling, or authentication requirements.

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

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

The description is concise with two sentences, an example call, and cost information. No unnecessary words or redundancy.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose and cost. However, it lacks details on output format, pagination, or possible errors, which would be helpful for an agent.

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

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

The schema has 0% description coverage. The description says 'any ticker/CIK' but the parameter is named 'ticker' and does not clarify if CIK is accepted or how to format it. The example uses 'AAPL' but adds limited semantic value.

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 'SEC corporate-financials intelligence: company facts (XBRL financials) + recent SEC filings for any ticker/CIK', specifying the exact data type and source. It distinguishes itself from sibling bundle tools by focusing on SEC 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 indicates usage for 'equity research, investment analysis, and financial agents', providing clear context. However, it does not explicitly mention when not to use 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?

With no annotations, the description carries the full burden. It does disclose auto-detection logic and per-call cost, but it fails to state what 'check' does (validation, enrichment, etc.), what happens on invalid input, or the return format. The truncated 'check-di' further limits transparency.

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, but the main sentence is cut off mid-phrase ('check-di'), which hurts readability and completeness. The cost line is useful but feels appended as a separate note without clear integration into the rest of the description.

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 tool with no output schema, the description should explain what the 'correct check' returns and how the caller interprets the result. It falls short because of truncation and because the validation/enrichment scope is never stated, leaving significant ambiguity for an agent.

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

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

The schema provides a single 'arg' parameter with no description (0% coverage). The description partially compensates by indicating the input is an ISIN/CUSIP/SEDOL, but it does not provide format examples, confirm that the complete string should be passed, or explain how the argument maps to the auto-detection logic.

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 a securities-ID bundle that auto-detects ISIN, CUSIP, or SEDOL and runs the appropriate check, which differentiates it from single-format tools like verify_isin or verify_cusip. However, the truncated phrase 'check-di' and the lack of a stated output weaken the clarity.

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 when you have an unidentified security identifier but provides no explicit when-to-use or when-not-to-use guidance. It does not mention that individual verify tools could be used if the format is already known, nor does it describe any prerequisites 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds value by disclosing the cost range ($0.005–$0.05 USDC) and 'premium bundle' status, but it does not mention return format, error handling, rate limits, or what happens when a handle isn't found. The truncated 'F' leaves an incomplete behavioral disclosure.

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

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

The description is appropriately short and front-loaded with the core purpose. However, it appears truncated with a dangling 'F' on the second line, which disrupts structure and suggests incomplete content. The cost disclosure adds necessary information but the format is flawed.

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

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

For a tool that resolves handles across five platforms, the description omits important context such as output schema, response structure, per-platform behavior, error handling, and the exact meaning of the 'arg' parameter. With no output schema and minimal description, it is not complete enough for an agent to reliably understand the tool's behavior.

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

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

The schema has a single parametric 'arg' with zero description coverage. The description references 'one handle' but does not explicitly state that 'arg' is the handle or specify its format, acceptable input types, or example values. With 0% schema coverage and no param description, the description only partially compensates.

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 function with a specific verb ('resolves') and resource ('one handle across Bluesky, Mastodon, GitHub, Twitch and Hacker News'). It distinguishes itself from individual lookup siblings by positioning as a 'bundle' that handles multiple platforms in one call.

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 'in one call' implies a multi-platform use case, suggesting it is the right choice when a handle needs to be resolved across several services. However, it does not explicitly contrast with the individual lookup tools (e.g., lookup_bluesky) nor specify when not to use it, leaving usage guidance implied rather than 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?

With no annotations, the description must fully disclose behavior. It mentions cost and Base-first, and references GoPlus rug/honeypot security, but it does not describe what the tool actually returns, whether it's read-only, or what 'fuses' entails. The truncated sentence adds ambiguity.

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

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

The description is very short but incomplete, with a cut-off word ('securit'). While brevity is positive, it sacrifices necessary information and appears poorly maintained rather than deliberately concise.

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 single-parameter tool with no output schema or annotations, so the description is the only source of information. It fails to explain the input, output, or operational details, making it inadequate for agents to invoke the tool correctly.

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

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

The only parameter, 'arg', is completely undefined in both the schema and description. Schema coverage is 0%, and the description gives no hint about what the agent should pass (e.g., token address). The agent is left to guess.

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 indicates a token safety screen for pre-swap checks, which is a specific verb+resource. It distinguishes from sibling bundle_* tools by focusing on token safety. However, the text is truncated ('securit') and could be more polished, slightly reducing clarity.

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 a use case ('pre-swap token safety screen') but provides no explicit guidance on when to choose this tool versus alternatives like screen_token_rugpull. No when-not scenarios or alternative names are mentioned.

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

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

No annotations are provided, so the description must carry the transparency burden. It discloses the cost range and mentions GLEIF data, but does not describe output format, side effects, rate limits, or whether it is a read-only operation. The truncation further limits behavioral disclosure.

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

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

While the text is short, it is a truncated fragment that does not form a complete sentence, and the cost line is awkwardly appended. It lacks a coherent structure that would make it easily scannable for an agent, and the missing tail suggests poor formatting.

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 bundled-risk-vetting tool that likely aggregates multiple data sources, but the description is largely incomplete—it only mentions firmographic enrichment and cost. With no output schema and no parameter details, the agent cannot reliably invoke or interpret results.

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

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

The input schema has a single required string parameter 'arg' with no description (0% schema coverage). The description does not explain what should be passed (e.g., company name, domain, legal entity ID), leaving the argument completely ambiguous and preventing correct invocation.

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

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

The description clearly states the tool performs 'full supplier & third-party-risk vetting for any company in ONE call' and mentions firmographic enrichment, giving a specific verb and resource. However, it does not distinguish this from sibling tools like bundle_risk_360 or risk_vendor, and the description is truncated, which slightly reduces clarity.

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

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

The description offers no guidance on when to use this tool versus alternatives. It implies use for vendor vetting but lacks any when-not-to-use criteria, comparison with similar bundle_* tools, or examples of appropriate scenarios.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful details like 'exact' and 'as of today', indicating precision and a dynamic reference date. But it does not cover edge cases (e.g., invalid dates, future birthdates) or specify the return format beyond the units. The cost line is not behavioral and does not contribute to transparency.

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 brief but poorly structured: it contains an incomplete sentence ('For verification, eligibility, and') and a separate cost line that is not relevant to tool selection or invocation. The information is concise but not presented cleanly, and the truncation detracts from the overall structure.

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

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

For a simple calculator with one parameter and no output schema, the description provides the input format and the output units (years, total months, total days), which is a reasonable baseline. However, the truncated sentence, lack of return structure details, and absent edge-case handling make it incomplete. The cost information is extraneous and does not add to completeness.

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

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

The schema only defines a single parameter 'arg' with no description, so the description must compensate. It does by specifying the birthdate format 'YYYY-MM-DD' within the text, which is essential for correct invocation. It does not explicitly say 'arg is the birthdate', but the singular parameter and the phrase 'from a birthdate' make this clear enough. The description adds significant meaning beyond the schema.

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

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

The description clearly identifies the tool as an age calculator that computes exact age in years, total months, and total days from a birthdate. It specifies the input format (YYYY-MM-DD) and the reference date (as of today), making the purpose unmistakable. However, it does not explicitly differentiate from sibling tools like calc_date_diff or lookup_age_calculator, so it falls short of a 5.

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

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

The description provides clear usage context by stating 'For verification, eligibility, and' which points to typical scenarios where age calculation is needed. However, the sentence is truncated and does not offer any exclusions or mention alternative tools, so it stops short of full when/when-not guidance. The context is clear but incomplete.

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?

In the absence of annotations, the description carries the burden of disclosing behavior. It provides the formula hint ('apr-compoundsPerYear') and cost details, which add transparency. However, it does not describe the return format or any edge cases, but for a simple calculation tool, this is reasonably transparent.

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

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

The description is exceptionally concise: two sentences, one for purpose and one for cost. It contains zero fluff and front-loads the primary purpose immediately. Every word adds value.

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

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

The tool is simple, but the completeness is hampered by the unexplained 'arg' parameter and lack of output schema. The description gives the formula and cost but leaves the input format and return value unspecified. For a minimal viable description, this is adequate but with clear gaps.

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

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

Schema description coverage is 0% and the description does not explain what the 'arg' parameter represents. Although the formula hint gives some clue, there is no explicit mapping between the parameter and its expected format (e.g., JSON string with APR and compounds per year). The description fails to compensate for the schema's lack of detail.

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

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

The description clearly states the tool's function: converting APR to APY (effective annual yield). This differentiates it from sibling tools like calc_compound or calc_savings by specifying the exact conversion and output. The phrase 'APR to APY' is specific and 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 hints at usage context with 'For fintech and savings agents,' implying it is intended for financial calculations. However, it does not explicitly state when to use this tool over alternatives or provide exclusions. No alternative tools are mentioned, leaving the guidance at an implied level.

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

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

With no annotations, the description must convey behavioral traits. It discloses the cost per call and the return values, which is useful. However, it does not explicitly state that the operation is read-only or describe error handling, though the calculator nature implies safety.

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

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

The description is brief and front-loads the core purpose, but the incomplete sentence 'For media, video' disrupts readability and adds confusion. The cost information is useful but could be better integrated.

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

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

For a simple one-parameter calculator, the description covers the input format, outputs, and cost, which is adequate. However, it lacks details on value constraints, edge cases, and the vague 'For media, video' fragment suggests missing content, leaving some gaps.

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

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

The schema has a single 'arg' parameter with no description (0% coverage). The description compensates by specifying the exact format as 'WIDTH-HEIGHT' and providing an example (1920-1080), which gives the agent enough information to construct valid input.

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 function: it takes a WIDTH-HEIGHT string (e.g., 1920-1080) and returns the simplified ratio, decimal ratio, and orientation. This specific verb and resource set it apart from other calc_* tools.

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

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

No explicit guidance on when to use this tool versus alternatives is provided. The fragment 'For media, video' hints at a context but is incomplete and does not mention any excluded cases or sibling 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?

With no annotations, the description carries the burden. It discloses that the result is 'estimate only' and includes a cost range, which adds value. But it does not describe the output format or behavior on invalid input, leaving some behavioral ambiguity.

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

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

The description is two sentences long and every clause earns its place: purpose, input format, caveat, target domains, and cost. There is no filler or redundant 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?

The tool is simple (one string parameter, no output schema), and the description covers the essential input format, estimate-only caveat, and cost. However, it lacks a concrete example and a statement about the return value, which 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?

The lone 'arg' parameter has 0% schema description coverage, but the description compensates by specifying the expected format 'weightKg-drinks-hours[-sex]'. This provides crucial meaning, though it leaves details like acceptable sex values ambiguous.

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

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

The description explicitly states the tool estimates blood-alcohol concentration using the Widmark formula and provides the exact input pattern 'weightKg-drinks-hours[-sex]'. It clearly distinguishes itself from sibling calc_* tools as the only BAC calculator.

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 the intended context ('For health, hospitality, and safety'), which helps an agent decide when to use it. However, it does not mention exclusions or alternative tools, so it lacks explicit when-not-to-use guidance.

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

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

There are no annotations, so the description carries the full burden. It adds useful behavioral context: supported base range (2-36), the argument syntax, and a cost range per call. However, it does not disclose output format, error behavior, or edge cases like invalid bases, which would be valuable for a paid utility tool.

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

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

The description is compact and front-loaded: the core purpose and argument format appear in the first sentence, followed by a brief example and target audience. The cost note is a separate line and does not clutter the main explanation. Every sentence adds value.

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

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

Given the tool's simple nature, one parameter, and no output schema, the description covers the essential input format, base range, and cost. It does not explicitly state the return value format, but for a base conversion tool, the output is strongly implied by the example and purpose. Overall, it is complete enough for an agent to invoke correctly.

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

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

Schema description coverage is 0% and the only parameter 'arg' has no schema description. The description compensates well by explaining the exact expected format with an example, '255-10-16', which gives the parameter meaning beyond the bare schema. It could be more precise about delimiters or validation, but the provided format is sufficient.

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

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

The description clearly states the tool performs number base conversion, names the exact argument format 'number-fromBase-toBase', gives a concrete example, and specifies bases 2-36. This distinguishes it from other calc_* tools like calc_binary or calc_hex_text.

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

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

The phrase 'For dev/encoding agents' provides a target audience but no explicit when-to-use vs alternatives. It does not mention when not to use this tool or how it differs from sibling calculators like calc_binary, calc_hex_text, or calc_convert. The example format gives implicit usage but no clear guidance on choosing this tool.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the encode/decode behavior, the argument pattern, and per-call cost, but omits return format, error handling, or authentication needs. This is adequate for a simple transform but leaves notable gaps.

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

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

The description is three short sentences, front-loaded with the core operation and format, and contains no filler. Each sentence earns its place.

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

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

For a single-parameter, no-output-schema calculator, the description provides the essential operation, format, audience, and cost. It is sufficiently complete for a simple transform, though a note about the return value would make it fully comprehensive.

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

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

The schema has only one string parameter with no description, but the description compensates by explicitly showing the expected pattern `{encode|decode}/{text}`. This gives the agent the exact syntax needed to call the tool correctly.

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 core operation as 'Base64 encode or decode' and provides the exact argument format. It distinguishes itself from sibling calc_* tools by naming the specific Base64 operation.

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

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

It names the intended audience ('dev, data-pipeline, and API agents') and gives a compact usage pattern, providing clear context. However, it does not explicitly mention alternative tools or when not to use it, so it stops short of a perfect score.

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

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

No annotations are provided, so the description carries full responsibility. It discloses the cost range and the unusual path-style invocation format, but does not describe return values, error handling, or side effects. Some transparency is present but significant gaps remain.

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

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

The core description is front-loaded and efficient, delivering the action and call format in one sentence. However, it is truncated mid-sentence and includes a separate cost line that, while useful, adds slight structural untidiness.

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 single-parameter tool with no annotations or output schema, the description adequately covers the invocation format and cost. It lacks examples or return-value expectations, and the truncation leaves the use-case sentence incomplete, so the picture is not fully rounded.

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

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

The schema has one undocumented string parameter (arg) with 0% description coverage, but the description fully explains its required format: '{encode|decode}/{text}'. This compensates completely for the schema deficiency by providing essential parameter syntax.

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 text to/from binary and shows the encode/decode call format. It is specific about the verb and resource but doesn't explicitly distinguish from sibling conversion tools like calc_base or calc_base64.

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 a direct call format and mentions intended use cases (dev agents, data-encoding pipelines, coding-education) but they are truncated. It lacks explicit guidance on when to use this tool versus alternatives or when not to use it.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions input format and cost but does not describe output structure, return category values, error handling, or how the system selector works, leaving significant gaps.

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

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

The description is brief but contains an incomplete sentence ('For health, fitness, and in') and extraneous cost information. It lacks clear structuring and finishes abruptly, limiting its 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?

Given no annotations, no output schema, and a single ambiguous parameter, the description is insufficient. It omits return value details, category classifications, and edge cases, making it incomplete for reliable tool 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 schema provides no description for the single 'arg' parameter (0% coverage). The description partially compensates by specifying the format 'weight-height[-system]' and allowed units, but does not fully define acceptable system values or exact string syntax.

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 computes Body Mass Index with unit options (imperial lb/in or metric kg/cm) and includes category. Though it lacks an explicit verb, 'Body Mass Index for...' implies calculation and distinguishes it from sibling calculators like calc_bmr.

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

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

No explicit guidance on when to use this tool versus alternatives. The truncated phrase 'For health, fitness, and in' suggests potential use cases but does not provide complete context, exclusions, or comparison to sibling tools.

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

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

No annotations are provided, so the description must carry the full burden. It mentions cost per call and the formula, but the description is truncated ('Fo') and gives no information about return format, units, or potential 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 extremely short but appears truncated ('Fo') and awkwardly pairs technical info with cost data. It reads as incomplete rather than intentionally concise.

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?

Without an output schema or annotations, the description should clearly explain what the tool accepts and returns. It only provides a partial input template and pricing, leaving the output format and behavior unspecified.

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

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

The schema provides zero description for the single 'arg' parameter. The description partially compensates by hinting at the expected structure: 'weightKg-heightCm-age-sex[-activity]', but it does not specify separators, optionality, or unit conventions.

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

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

The description clearly states that the tool calculates BMR and TDEE using the Mifflin-St Jeor equation, with a hint at the required input fields. This distinguishes it from sibling calc tools like calc_bmi or calc_bac.

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

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

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no mention of typical use cases. It only states what the tool computes.

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

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

No annotations are provided, so the description carries the full burden. It adds useful behavioral context: the output is in m^2, uses a specific formula, is an estimate, and includes cost per call. However, it does not disclose constraints, error behavior, or exact return format beyond the unit.

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

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

The description is two sentences with no fluff, front-loading the core purpose. The cost line is extra but relevant operational information. Every sentence earns its place.

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

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

As a simple calculator with one parameter and no output schema, the description should clarify the exact input format and return value. It gives the output unit (m^2) and mentions 'estimate', but does not specify whether the return is a bare number or an object, nor how to construct the arg string. This is adequate but has clear gaps.

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

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

Schema description coverage is 0%, and the only parameter 'arg' has no schema description. The description hints that the arg string contains 'heightCm-weightKg', but does not define the exact format, delimiters, or required structure. This is a marginal compensation for the lack of schema information, but it leaves ambiguity.

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

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

The description clearly states a specific verb+resource: calculating Body Surface Area using the Mosteller formula. It also specifies the input format ('heightCm-weightKg'), the output unit (m^2), and that the result is an estimate. This differentiates it from sibling tools like calc_bmi and calc_bmr.

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 explicitly says 'For medical and dosing agents,' providing a clear intended use case. It does not mention alternatives or exclusions, but the context is sufficient to guide an agent on when to invoke this tool.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not specify the return format, input validation, rounding behavior, or side effects. The cost line adds context but does not address the tool's operational behavior.

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

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

The description is concise and front-loaded with the core action. The second sentence fragment ('For devops agents...') is incomplete, and the cost line is useful but not essential. Overall, it is reasonably well-structured for its brevity.

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

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

For a simple one-parameter tool with no output schema, the description provides the essential purpose and input identification. However, it lacks details on the return value format, edge cases, and a complete usage scenario. The cost context is extra but does not fill these gaps.

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

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

The only parameter (arg) is described as a raw bytes value, which adds meaning beyond the schema's generic 'arg' string type. However, it lacks details on expected format (e.g., numeric string, examples, range). Since schema coverage is 0%, the description partially compensates but not fully.

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 function: 'Formats a raw bytes value into human-readable KB/MB/GB/TB.' It uses a specific verb and resource, making it distinct from sibling tools like calc_convert or calc_unit by focusing on byte formatting.

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 implied usage context ('For devops agents, storage-monitoring bots, file-processing') but does not explicitly state when to use this tool over alternatives or provide exclusions. No alternative tools are mentioned, so guidance is minimal.

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

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

With no annotations provided, the description carries the full burden. It discloses the outputs and a cost range, which is useful behavioral context. However, it does not explicitly state that the operation is read-only, how invalid inputs are handled, or whether there are any rate limits. The truncated text ('usable hos') also slightly undermines completeness.

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 relatively short and front-loaded with the tool's purpose, but it suffers from truncation (the output list ends mid-word) and includes cost information in a way that disrupts the flow. The single-sentence structure with an embedded example is acceptable but not optimally formatted.

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

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

Given no output schema and no annotations, the description must explain return values and behavior. It lists the key outputs and gives an example, but it omits details like the exact output format, error handling for invalid CIDRs, and handling of IPv6 inputs. The truncation also leaves the output list incomplete, reducing completeness.

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

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

The schema only defines a single parameter 'arg' with no description (0% coverage). The description compensates by using an example ('192.168.1.0/24') that clearly implies the argument is a CIDR notation string, covering IPv4 and IPv6. This adds meaning beyond the bare schema.

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

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

The description clearly identifies the tool as an IPv4/IPv6 subnet (CIDR) calculator and provides a concrete example ('192.168.1.0/24') with expected outputs (network, broadcast, netmask, address count, usable hosts). This specific resource and operation distinguish it from the many sibling calc_* 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 usage by describing the input format and output, but it does not explicitly state when to choose this tool over alternatives or mention any exclusions. The example provides context, but there is no direct guidance on use cases or prerequisites.

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

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

With no annotations, the description carries the full transparency burden. It discloses accepted input formats and output color models, and even mentions cost, but it does not cover invalid input behavior, normalization, rounding, or the exact structure of the returned data.

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 poorly structured; the second sentence 'For design, theming, and' appears truncated and incomplete, detracting from clarity. The cost line is extra information but is placed awkwardly, making the overall description feel disjointed.

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 converter with no output schema, the description adequately conveys what inputs are accepted and what output types are returned. However, it lacks details about return representation and error cases, and the incomplete sentence reduces completeness.

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

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

The single schema parameter 'arg' has no schema description, but the tool description compensates by listing three concrete accepted formats (hex, 'rgb-255-136-0', 'r,g,b'). This adds meaningful meaning beyond the bare schema, though it leaves some syntax details ambiguous.

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 a color converter and specifies input formats (hex, 'rgb-255-136-0', or 'r,g,b') and outputs (hex, rgb, hsl). This distinguishes it from sibling color tools like lookup_color_contrast or lookup_color_palette, which serve different purposes.

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

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

The phrase 'For design, theming, and' implies a use case, but it is truncated and incomplete. There is no explicit mention of when to use this tool versus alternatives or when not to use it, leaving only an implicit 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?

Since no annotations are provided, the description carries the full burden. It discloses the input format and the dual output (nPr and nCr) as well as the cost. However, it omits input constraints (e.g., integers required, n≥r), error behavior, and whether the operation is purely read-only. It adds value but leaves gaps.

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

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

The description is extremely concise: the first sentence explains the core function and input format, and the second sentence adds cost information. No wasteful or redundant wording.

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

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

The tool is simple with one parameter and no output schema, but the description lacks details on parameter constraints (integer only, n≥r), return value format, and error handling. These omissions make it less complete for autonomous agents, though it still covers the basic function and syntax.

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's only parameter 'arg' has no description and 0% coverage, so the description must compensate. It explains the expected syntax 'n-r', which is essential for using the tool. Although it doesn't detail all edge cases, it provides the critical meaning needed to invoke the tool correctly.

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 it computes permutations and combinations, specifies the output as nPr and nCr, and gives the input format 'n-r'. This distinguishes it from the many sibling calc_* tools by focusing on combinatorics.

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

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

The description says 'For statistics, probability, and math agents,' providing clear usage context. It does not explicitly mention alternatives or exclusions, but the context is sufficient for an agent to know when to select this tool over other calculators.

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

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

With no annotations, the description carries the transparency burden. It discloses the calculation type, input format, and returned metrics (future value, total interest), and mentions cost. However, it does not disclose error behavior, edge cases (e.g., zero/negative rates), or whether the result is human-readable, which would be useful for a calculator 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 two concise sentences that front-load the tool's purpose, then add the input format and return values, and finally the cost. No wasted words or repetitive 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 calculator tool with one parameter and no output schema, the description covers the essential details: input format, outputs, and cost. It doesn't mention potential limitations or error handling, but for this tool's complexity, the level of detail is adequate.

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

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

The schema only provides a single generic string parameter ('arg') with 0% coverage. The description compensates by specifying the expected format 'principal-rate-years[-timesPerYear]', assigning meaning to the parameter beyond the schema. It doesn't explicitly define each component (e.g., principal is a number), but the format is clear enough for usage.

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 a compound-interest/future-value calculator and specifies the input format ('principal-rate-years[-timesPerYear]') and outputs (future value, total interest). This distinguishes it from vague calculator tools and provides a specific verb-resource pairing.

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 the input syntax but does not explain when to use this tool versus siblings like calc_future_value, calc_apy, or calc_loan. No exclusions or alternative recommendations are provided, leaving the agent to infer the appropriate use case from context.

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

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

With no annotations, the description carries the transparency burden. It discloses the data source (latest ECB reference rate), the output (converted amount), and the cost per call. It doesn't mention error handling or supported currencies, but for a simple non-destructive converter this is adequate.

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

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

Two sentences, with the essential operation and input format front-loaded in the first sentence and the cost in the second. No redundant or vague language.

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

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

For a simple calculator with one string parameter and no output schema, the description covers purpose, input format, output, rate source, and cost. The example clarifies expected usage, and the missing currency list is not critical given the ECB rate reference.

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 only 'arg' with no description (0% coverage). The description fully compensates by explaining the format 'amount-FROM-TO' and giving a concrete example, making the parameter's meaning unambiguous.

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 this is a currency converter and provides the exact input format ('amount-FROM-TO') with an example (100-USD-EUR). It distinguishes itself from sibling tools like calc_unit and lookup_currency by specifying the ECB reference rate.

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 an explicit usage example and clarifies the purpose, so an agent knows when to invoke it. It doesn't explicitly name alternatives, but the specificity of 'currency converter' and the ECB rate source makes the intended context 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?

With no annotations, the description carries the burden of disclosure. It adds cost information and the required input format, but does not describe the output format, units, or error handling. For a simple calculator, this is adequate but not comprehensive.

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

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

The description is concise: two sentences front-loading purpose and usage, with cost appended. No redundant 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 tool with no output schema or annotations, the description covers purpose, call format, and cost, but omits the output unit/format (e.g., seconds, minutes). This is a notable gap given the tool's function.

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 only has an opaque string parameter 'arg' with no description. The description compensates by specifying the exact expected format 'sizeGB-speedMbps', clarifying the meaning of the parameter. However, it does not fully define units (e.g., GB vs GiB, Mbps vs MB/s).

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 it computes transfer time for a given file size and link speed, with a specific call format 'sizeGB-speedMbps'. This distinguishes it from sibling calc_* tools, which perform other calculations.

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

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

The description implies usage for network agents and devops, and gives a call format, but it does not explicitly state when to use this tool over alternatives or include any exclusions. Usage context is present but not fully developed.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It gives the input format and cost but does not explain the return value structure, how date order is handled (e.g., negative differences), or whether all units (days, weeks, years) are returned together. This leaves significant gaps for an agent trying to interpret results.

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 exceptionally concise: two sentences, front-loaded with purpose and format, and includes cost as a relevant datum. No filler or redundant repetition of the tool name.

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

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

For a simple calculator, the description covers purpose, input format, and use cases, which is good. However, with no output schema or annotations, it doesn't explicitly state what the result looks like (e.g., a number, an object with days/weeks/years) or whether the order of dates matters. This is an implied completeness gap.

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

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

The schema has a single 'arg' string with no description (0% coverage), but the description fully compensates by specifying the exact required format: 'YYYY-MM-DD_YYYY-MM-DD'. This is everything the agent needs to construct the parameter correctly.

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 function: 'Days/weeks/years between two dates' with a specific input format. This distinguishes it from sibling tools like calc_due_date or calc_time_duration, which likely handle related but different calculations.

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

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

It identifies target use cases ('For scheduling, billing, logistics agents'), providing clear context for when to apply the tool. It doesn't explicitly name alternatives or exclusions, but the use-case framing is helpful and 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?

With no annotations, the description carries the full burden of behavioral disclosure. It reveals the formula and cost but does not explain how the 'arg' string parameter maps to 'powerRatio', what happens with invalid inputs, or the return format. The truncation and parameter mismatch are significant gaps for a tool with no structured metadata.

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 not concise because the primary sentence is cut off mid-word ('te'), making it structurally incomplete. The cost sentence is additional but not central to the tool's usage. The truncation significantly harms the overall quality.

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 calculator tool, but the description is incomplete due to truncation and does not fully define input-output behavior. It lacks an explicit link between the schema parameter and the described 'powerRatio', and with no output schema or annotations, the description must provide more context than it does.

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 0%, and the only parameter 'arg' is a context-free string. The description introduces the term 'powerRatio' and the formula but never explicitly states that 'arg' should contain the power ratio or in what format (numeric string, expression, etc.). This leaves the agent to guess the parameter mapping, which the description fails to compensate for.

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 decibels via the formula 10*log10(ratio) for a given 'powerRatio', which distinguishes it from sibling calc_* tools like calc_log. However, the sentence is truncated ('...te') and the parameter name in the description ('powerRatio') does not match the schema parameter ('arg'), slightly reducing clarity.

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 mentions target audiences ('audio-engineering agents, RF/signal analysis bots') but is cut off and does not explicitly state when to use this tool versus other calculator tools like calc_log or calc_convert. No alternatives or exclusions are given, so usage context is implied rather than 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?

With no annotations, the description carries the full burden. It discloses the calculation formula and cost range, which is helpful, but fails to describe the exact argument format (only a cryptic 'mass-volume' phrase), return value structure, or error handling behavior, leaving significant gaps.

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

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

The description is extremely concise, with the core function and formula front-loaded in the first sentence, followed by a relevant cost line. Every sentence adds value without 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?

Despite being a simple tool, the lack of annotations and output schema means the description must fully explain the input and output contract. It gives the formula but omits critical details like how to format the mass and volume values, making the overall tool behavior inadequately specified.

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

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

The schema has only one string parameter 'arg' with no description (0% coverage), so the description must compensate. The hint 'call as mass-volume' is vague and does not specify the exact input format, units, or delimiters needed to construct a valid call.

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

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

The description explicitly states the tool computes density (mass/volume), providing the formula and a clear call syntax hint. This distinguishes it from the many calculator siblings by its unique subject and application domain (materials science, shipping, engineering).

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

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

It mentions specific use cases (materials-science agents, shipping/freight bots, engineering) which gives clear context for when to use it. It does not explicitly name alternative tools or state when not to use it, but the intended audience serves as sufficient guidance.

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

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

No annotations are provided, so the description carries the full transparency burden. It discloses the input format, output, and cost, but does not explicitly state read-only behavior, error handling, or unit consistency requirements. These are minor omissions for a simple calculator, but the description isn't fully transparent on safety and failure modes.

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

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

The description is concise and front-loaded: the first sentence states the formula, input, and output; the second gives the target audience; and a short cost note follows. No wasted words, and the structure is clear with line breaks separating the main description from cost details.

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

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

For a one-parameter tool with no output schema and no annotations, the description covers the core functionality, input format, and return values. It lacks examples or explicit mention of unit consistency, but for a simple calculator, this is nearly complete. The cost information also adds operational 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 has a single 'arg' string with no description (0% coverage). The description fully compensates by explaining the exact expected format ('c1-v1-c2'), which is essential for correct usage. This adds significant meaning beyond the schema.

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

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

The description clearly states the tool calculates solution dilution using the formula C1*V1=C2*V2, specifies the input format 'c1-v1-c2', and the output ('final volume + solvent to add'). This specific verb+resource+scope distinguishes it from sibling calc_* tools like calc_molarity or calc_ph.

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 usage context: 'For lab, chemistry, and brewing agents.' While it doesn't explicitly state when not to use the tool or name alternative tools for different calculation types, the target audience and use case are clear, so it earns a 4.

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

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

No annotations are provided, so the description carries the burden. It discloses support for stacked discounts and per-call cost, which is useful, but it doesn't describe output structure, rounding behavior, or error handling. For a simple calculator this is adequate but not rich.

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

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

Two short sentences front-load the key information: output metrics, input format, stacked discount support, use case, and cost. Every word earns its place, with no filler or redundancy.

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

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

For a simple one-parameter calculator with no output schema, the description provides the input format, computed metrics, and a cost note, which is mostly sufficient. It lacks an explicit return schema and some specifics like currency formatting or rounding, but overall it is complete enough for the tool's simplicity.

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

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

The input schema provides only a bare 'arg' string with 0% description coverage. The description fully compensates by specifying the exact input format 'price-percentOff[-percentOff2…]', which is essential for correct invocation. This adds indispensable meaning beyond the schema.

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

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

The description specifies the tool's outputs (sale price, savings, effective %) and input format (price-percentOff with stacked discounts), clearly indicating what the tool does. However, it lacks an explicit verb like 'Calculate' and doesn't sharply distinguish itself from similar siblings like calc_savings or calc_percentage.

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

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

The phrase 'For e-commerce' provides a usage context, and the input format implies when to use it (price discount calculations). But it doesn't explicitly contrast with sibling calculators or state when not to use this tool, so guidance is implied rather than 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?

No annotations are provided, so the description bears the full burden of behavioral disclosure. It usefully discloses a per-call cost on Base, which adds context beyond a simple 'calculate' statement. However, it omits critical behavioral details such as the output unit (kilometers vs miles), return format, and error handling, leaving the agent to guess at the result shape.

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

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

The description is concise and front-loaded with the purpose, followed by input format and a cost note. Every sentence earns its place. The only issue is the example appears truncated (ends at '-118.2' without closing the coordinate), which slightly detracts from an otherwise tight structure.

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 single-parameter tool with no output schema and no annotations. The description covers what it does and how to pass input, but it fails to mention the output unit or response format, which is a meaningful gap for a distance calculator. The cost disclosure adds context, but overall completeness is average.

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 only a single string parameter 'arg' with no description (0% coverage), so the description must fully compensate. It does so by specifying the exact expected format: 'lat1,lon1,lat2,lon2' and giving a concrete example. This is exemplary parameter documentation that directly tells the agent how to construct the argument.

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

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

The description clearly states a specific verb and resource: 'Great-circle distance between two lat/lon points'. It distinguishes itself from sibling calc_* tools by naming a unique calculation and provides an example format, leaving no ambiguity about what the tool does.

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

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

The description clearly implies when to use the tool (whenever great-circle distance is needed) and specifies the exact input format via comma-separated coordinates and an example. It does not explicitly name alternatives but none of the sibling calc_* tools overlap with this specific function, so the context is clear and no exclusions are needed.

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

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

No annotations are provided, so the description carries the full burden. It mentions Naegele's rule and cost but does not disclose output format, error handling, or any side effects. The truncated sentence omits important behavioral details.

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

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

The description is one sentence but appears truncated, ending mid-phrase "Naegele'". While brief, the incomplete structure undermines clarity, and the cost information is appended without clear separation.

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

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

For a simple calculator, the description gives the key input and output concept, but lacks an output schema and annotations. The truncation leaves the explanation incomplete, and no return format is described. Given the large number of sibling calculator tools, more contextual detail would aid selection.

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

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

The single parameter 'arg' has zero schema description coverage. The description partially compensates by indicating the input is an LMP date in YYYY-MM-DD format, but it does not explicitly map 'arg' to that format or explain validation 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 identifies the tool's function: calculating pregnancy due date, gestational age, and trimester from the last menstrual period. It distinguishes itself from sibling calc_* tools by specifying the medical calculation context, though it lacks a formal verb structure and is truncated.