GoCreative Agent API

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 the model, standard tier, cost range, and that no API key is needed. This adequately informs an agent about the tool's behavior, though it omits latency or token limits.

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

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

The description is concise with four key sentences: purpose, payment/no API key, use cases, example with cost. No extraneous information, well-structured for quick comprehension.

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 does not explain the return format (the answer). It covers core aspects (input, cost, model) but slightly lacks details on response structure and error handling. For a simple LLM tool, it is largely complete.

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

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

Schema description coverage is 0% for the single parameter 'prompt', but the description adds a concrete example and explains that it accepts any prompt. This compensates for the missing schema description, though it does not detail constraints like max length.

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 is an LLM completion endpoint for sending prompts and receiving answers. It specifies the model (DeepSeek V3.1) and differentiates itself from siblings (ai_pro, ai_ultra) by calling itself 'standard tier', making its role distinct.

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 explicit use cases (summarization, extraction, classification, drafting, reasoning) and cost information, implying when it's appropriate to use this over premium tiers. However, it does not explicitly state when not to use it or directly compare to ai_pro/ai_ultra, but the context signals its position.

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. The description discloses cost ($0.005–$0.05 USDC per call) and states no API key or signup is needed. However, it does not address data privacy, prompt length limits, latency, error behavior, or whether the tool is read-only. Key behavioral aspects 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 three sentences long with no wasted words. The first sentence states the core purpose, the second provides usage guidance, and the third includes an example. It is well-structured and 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 simple schema (1 required param) and absence of output schema and annotations, the description covers the essential aspects: what it does, when to use, input example, and cost. It could be slightly more detailed on output format, but overall it is adequate 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?

There is one parameter (prompt) with 0% schema description coverage. The description provides an example call to illustrate the parameter format, which adds some semantics. However, it does not specify constraints like max length, encoding, or required format beyond the example.

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 LLM completion using a frontier-tier model (Claude 4.5 Sonnet) for high-end reasoning, analysis, synthesis, and drafting. The verb-resource combination is specific. However, it does not differentiate from siblings like ai_ask or ai_ultra, which are listed in the sibling tools.

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

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

The description explicitly says 'Use when the task is hard enough to need the smartest model.' This gives a clear condition for use but does not mention when not to use or provide alternatives. Sibling tools exist (e.g., ai_ask, ai_ultra) but are not referenced.

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 that it is an LLM completion (generative), costs money (USDC per call), and provides a price range. However, it does not mention potential behavioral traits like streaming, rate limits, or what happens on error. The cost disclosure is useful 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 succinct with two sentences plus an example and cost info. It is front-loaded with the main purpose. A bit more structure (e.g., separating use case from cost) could improve clarity, but overall 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?

Given the tool's simplicity (single parameter) and the presence of many sibling tools, the description covers the essential aspects: purpose, model tier, use case, cost, and an example. It does not explain the return value, but there is no output schema, so the agent can infer typical LLM response.

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

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

The only parameter is 'prompt,' which has no schema description (0% coverage). The description provides an example call that illustrates usage but does not formally describe the parameter's expected format, constraints, or best practices. The example adds some meaning but is not a full explanation.

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 'LLM completion' with a specific model (Claude Opus 4.6) and tier (ultra). It distinguishes from siblings by labeling it as the top-end reasoning model for hardest tasks, and explicitly contrasts with other AI tools like ai_ask and ai_pro via cost and capability hints.

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 says 'Use when model quality dominates cost' and describes the use case as 'deep multi-step analysis and high-stakes drafting.' It does not explicitly name alternative tools or when not to use, but the tier naming provides implicit guidance. Lacks explicit 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 provided, so description must cover behavioral traits. It mentions cost but fails to disclose authentication requirements, rate limits, behavior for invalid input, or output format. Lacks essential 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?

Description is short and to the point, but the cut-off word 'mon' indicates potential typo. Otherwise, efficient use of space.

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

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

For a simple single-parameter tool, the description provides basic purpose and cost. However, given no output schema or annotations, it lacks details on return structure, error behavior, and parameter constraints.

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?

Parameter schema has 0% description coverage. Description adds that 'arg' can be an ASIN or URL, which provides some semantic meaning, but does not specify format or how to differentiate. Insufficient for 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?

Description clearly states it retrieves full product data by ASIN or URL, listing specific fields. However, the text cuts off at 'mon', slightly reducing clarity. Distinguishes from siblings like search or price 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?

Description implies use for full product details, but does not explicitly state when to use versus siblings like amazon_products_search or amazon_products_price. No explicit alternatives or conditions provided.

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

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

No annotations provided, so description carries burden. It includes cost per call ($0.005-$0.05) which is helpful, but does not disclose whether it is read-only, any rate limits, or authentication needs. Implicitly a read operation.

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

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

Description is brief and front-loaded with purpose. The truncation slightly detracts, but overall it is concise. Cost information is relevant but could be separated.

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?

Description lists return fields (price, list price, stock status, rating) despite no output schema, which helps. However, lacks details on error handling, invalid inputs, or pagination. Adequate for a simple 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 has only 'arg' with no description (0% coverage). The description compensates by specifying that input can be an ASIN or URL, adding meaning beyond the schema. Could further clarify expected format.

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 Amazon Price Check API that returns current price, list price, stock status, and rating using ASIN or URL. The truncated text still conveys purpose, and it distinguishes from sibling tools like amazon_products_asin and amazon_products_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 guidance on when to use this tool versus alternatives. It does not mention exclusive conditions, prerequisites, or context where it should be preferred over related Amazon 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 the description must cover behavioral traits. It mentions cost but omits critical aspects like rate limits, pagination, caching, authentication requirements, or error handling. This leaves significant uncertainty for an AI agent.

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 with two sentences: a functional summary and cost. It is front-loaded but has a minor truncation issue. No unnecessary information, but could be slightly more polished.

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 complexity of an e-commerce search tool and no output schema, the description lacks return structure or output details. It does not explain how results are organized or what to expect from the 'arg' parameter beyond 'keyword'. Incomplete for effective use.

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 description identifies 'arg' as a keyword, adding basic meaning beyond the schema (which has 0% coverage). However, it lacks format details (e.g., multiple words, encoding). With low schema coverage, 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 'search Amazon products by keyword,' specifying the action and resource. It distinguishes from sibling tools like amazon_products_asin (search by ASIN) and scrape_amazon (probably more general). Minor truncation ('offers coun') but overall purpose is clear.

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

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

No explicit guidance on when to use this tool versus alternatives. While the description implies keyword searches, it does not mention exclusions or alternative tools (e.g., when to use amazon_products_asin instead).

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

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

No annotations are provided, but the description discloses cost ($0.005–$0.05 per call) which adds transparency. It does not mention other behavioral traits like read-only nature, rate limits, or data retention, but the name implies non-destructive analysis.

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 sentences long, no wasted words. It front-loads the main purpose, includes an example, and provides cost information efficiently.

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

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

The tool has one parameter, no output schema, and no annotations. While it explains the input and cost, it does not describe the return format or structure of the audit results, which is important for an agent to process the 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?

Only one parameter with 0% schema coverage. The description adds meaning by specifying the 'owner/repo' format (e.g., 'vercel/next.js'), which is not evident from the schema's title 'Owner Repo'.

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 audits a GitHub repo for security signals like CVEs, last commit age, license, and contributor count. It uses a specific verb and resource, and the purpose is distinct from sibling tools like lookup_github or enrich_github.

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 says to use for OSS supply-chain risk scoring and provides an example call. However, it does not explicitly mention when not to use or compare to alternatives like lookup_github_releases.

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 disclose behavioral traits. It mentions bulk capability and cost but lacks details on error handling, behavior if more than 50 symbols are sent, rate limits, or output format. The description is incomplete (cut off), further limiting 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 very concise and front-loads key information (what, limit, cost). However, it is cut off mid-sentence, which harms structure 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?

With no output schema, the description partially covers return values (prices and % change). It mentions cost and use case, but lacks full context on handling invalid symbols, pagination, or detailed output structure. Adequate for a simple bulk 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 has one parameter with 0% description coverage. The description adds critical semantic meaning: comma-separated crypto symbols with a limit of 50. This helps the agent format 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 tool provides live USD prices and 24h % change for up to 50 crypto symbols in a single call. It uses a specific verb ('BULK') and resource ('crypto symbols'), and distinguishes itself from sibling tools like lookup_crypto by emphasizing bulk retrieval.

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 'trading bots, portfolios' but does not explicitly state when to use this tool versus alternatives (e.g., lookup_crypto for a single symbol). No contraindications or prerequisites are provided.

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

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

No annotations are provided, so the description must carry behavioral disclosure. It mentions cost per call but does not cover rate limits, authentication, error handling, or behavior on invalid domains. The cut-off sentence 'for devops agents, email' is incomplete and confusing.

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 includes unnecessary or incomplete text ('for devops agents, email'), which detracts from clarity. It front-loads the main purpose, but the second sentence is cut off and unhelpful.

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, no annotations, and only one parameter, the description should provide more context about return format, error responses, domain validation, and whether the tool accepts all TLDs. It fails to do so.

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 only one parameter (arg) and 0% schema coverage, the description partially compensates by stating input is comma-separated domains, but it does not explicitly name the parameter or specify required format (e.g., full domain or with protocol).

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 resolves A/AAAA/MX/TXT/NS DNS records for up to 25 domains in one call, using comma-separated input. It differentiates itself from sibling tools like lookup_dns by emphasizing bulk 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?

The description mentions 'for devops agents' and implies usage when multiple domains need resolution via the 'BULK' prefix, but lacks explicit guidance on when not to use this tool versus alternatives like single-domain 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?

Discloses syntax + MX checks, deliverable vs invalid flagging, and cost. Lacks details on error handling, rate limits, or behavior when exceeding 50 emails.

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 concise sentences: first explains functionality and scope, second adds cost. 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?

Missing output schema and does not describe response format (e.g., per-email results). Incomplete for an agent to understand return value.

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 coverage, the description adds critical context: 'comma-separated' and 'up to 50 emails' for the single parameter, compensating for schema gaps.

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 'validates up to 50 emails in one call' using syntax and MX checks, clearly distinguishing it from sibling tools like lookup_email_validate (single email).

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 implies when to use (bulk validation) but does not explicitly state when not to use or compare to alternatives like lookup_email_validate.

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 for behavioral disclosure. It mentions cost and the limit of 20 repos, but lacks details on idempotency, error behavior, and whether it is read-only. Some transparency is present 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 mostly concise with two sentences and a cost line, front-loading the key idea with 'BULK:'. However, there is a typo ('F ') at the end, which slightly detracts from professionalism and conciseness.

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 bulk metadata tool with one parameter and no output schema or annotations, the description covers the input format, what metadata is returned, the limit, and cost. It is sufficiently complete for the tool's complexity, though error handling and output format are not mentioned.

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 no schema description (0% coverage), but the description adds significant meaning by specifying the format 'owner/repo,owner/repo'. This clarifies how to input multiple repos, though it could be more precise (e.g., comma-separated, 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 explicitly states it is a bulk tool for GitHub repos, fetching specific metadata fields (stars, language, license, dates, forks) for up to 20 repos. The 'BULK:' prefix and format instruction clearly differentiate it from single-repo tools like enrich_github or lookup_github.

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 when to use (for bulk metadata of up to 20 repos) and provides the input format. However, it does not explicitly state when not to use or mention alternative tools, though the 'BULK' label implies appropriate use cases.

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 the batch size limit (50 IPs) and cost, but lacks details on data freshness, error handling for invalid IPs, whether it is read-only (likely), or any rate limits. With no annotations, description carries full burden and falls short.

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

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

Two sentences, front-loaded with 'BULK:' and geolocation details. Includes cost information. Slightly marred by apparent truncation, but effectively concise with minimal 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?

Given no output schema, the description should explain the return format (e.g., JSON structure) and error handling. It mentions geolocation fields (country, city, ISP, lat/lon) but not how they are returned or what happens on failure. Incomplete for reliable agent use.

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' in the schema has no description (0% coverage). The description adds that it should be 'comma-separated' IPs and mentions the limit of 50, adding meaningful guidance. However, it does not specify IP format (IPv4/IPv6) or whitespace handling.

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 'BULK: geolocation (country, city, ISP, lat/lon) for up to 50 IPs', using a specific verb and resource. Distinguishes from single-IP siblings like 'ip' or 'lookup_ip' by emphasizing bulk capability and comma-separated input.

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?

Implicitly suggests use for multiple IP geolocation via 'BULK' and 'up to 50 IPs', but does not explicitly state when to use this tool versus alternatives or when not to use it. The snippet 'For fraud-detection agen...' hints at specific use case but is 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?

No annotations are provided. The description adds behavioral context by disclosing the cost per call ($0.005–$0.05) and the comma-separated input format. However, it does not specify rate limits, error handling, or behavior for invalid inputs.

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 two sentences. The first sentence covers the core functionality and output, the second adds cost information. No unnecessary 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, no annotations), the description provides adequate context: input format, output content, and cost. It lacks details on result ordering or error behavior, but is sufficient for a bulk 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?

The schema has one parameter 'arg' with no description (0% coverage). The description significantly adds value by explaining that 'arg' should be a comma-separated list of up to 20 domain names, which is essential for correct 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 states it performs WHOIS lookups for up to 20 domains in bulk, specifying the returned data (registrar, created/expiry dates, nameserver). It effectively distinguishes itself from siblings like lookup_whois by highlighting the bulk capability.

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 multiple WHOIS lookups are needed ('up to 20 domains in one call'), but does not explicitly state when not to use it or mention alternatives such as lookup_whois for single domains.

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 burden. It discloses the data sources and cost but does not explain whether the operation is read-only, any authentication requirements, rate limits, or side effects. The behavioral implications are implied but not stated.

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 (two sentences plus example and cost), front-loaded with purpose, and every sentence contributes meaning. No unnecessary 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 description covers purpose, data types, and cost but omits output details (no output schema provided). For a tool with no output schema, the description should at least hint at what the response contains. It also lacks prerequisites or limitations, 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?

With 0% schema description coverage and a single parameter 'domain', the description adds value by stating 'for any domain' and providing an example. However, it does not elaborate on input format expectations (e.g., protocol, subdomain handling) beyond the example.

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 fuses domain intelligence (DNS/WHOIS/TLS) with company firmographics for any domain, targeting sales prospecting, KYB, and enrichment. It identifies the specific data types and use cases, though it doesn't explicitly differentiate from sibling bundle tools like bundle_domain_360 or bundle_kyb_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 mentions applicable use cases (sales prospecting, KYB, enrichment) but provides no guidance on when not to use this tool or alternatives. Among many sibling 360 bundles, there is no explicit comparison or exclusion criteria.

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 lists returned data types and discloses cost ($0.005-$0.05 USDC on Base per call). It does not mention rate limits, permissions, or response format, but for a read-only bundle tool, the provided info is sufficient to inform behavioral 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?

Description is three focused sentences plus example and cost info. It is front-loaded with the main purpose and formatted for quick scanning. No superfluous 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?

Given no output schema, description lists output components but not structure or sample. It provides cost, which is helpful. For a composite tool, it covers essential aspects, though details on output shape or pagination could improve completeness.

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

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

Schema coverage is 0%, but description fully explains the required 'coin' parameter as a CoinGecko coin id with example 'bitcoin'. This adds essential meaning that the schema lacks, ensuring correct parameter 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?

Description clearly states the tool provides a full crypto snapshot in one call, listing constituent data types (market data, spot price, Fear & Greed, global context, trending) and the target coin identifier. It distinguishes from siblings by emphasizing aggregation, making it clear what this composite tool delivers.

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 suitability for trading, research, and portfolio agents, and implies consolidation of multiple calls into one. However, it does not explicitly state when not to use it or provide direct alternatives (e.g., individual crypto_* tools). The example and cost information aid usage understanding.

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) and states it returns 'account profile + top repositories,' which implies a read-only operation. However, it does not explicitly confirm non-destructiveness, mention rate limits, authentication needs, or the exact format of the response. The description is moderately transparent but lacks 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 concise (3 lines) and front-loaded with purpose and audience. It includes an example call and cost, which are valuable. Every sentence serves a purpose. A slightly more structured format could improve readability, but it is already 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?

Given the tool's simplicity (one parameter), lack of output schema, and no annotations, the description covers the basic purpose, audience, and cost. However, it does not describe the return value structure or any pagination/limits, which an AI agent might need to process the output correctly. The description is minimally viable but incomplete.

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 0% coverage (no descriptions), so the description must compensate. It explains that 'username' refers to a GitHub username and provides an example ('torvalds'). This adds meaning beyond the schema's bare title ('Username'), but it does not clarify constraints (e.g., case-sensitivity, required format). The compensation is partial.

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 'Developer/GitHub intelligence: account profile + top repositories for any username.' It specifies the target audience (recruiting, dev-rel, technical-sourcing agents), making the purpose clear. However, it does not explicitly differentiate itself from sibling tools like lookup_github_user or enrich_github, which offer overlapping GitHub data, so it loses some points.

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 usage context ('For recruiting, dev-rel, and technical-sourcing agents') and includes an example call. However, it lacks explicit guidance on when not to use this tool (e.g., for simple user lookup vs. the more comprehensive bundle) and does not mention alternatives. This is adequate but not thorough.

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 fully disclose behavioral traits. It mentions cost and example input, but does not state whether the tool is read-only, what happens on invalid input, rate limits, or output format. The lack of output schema exacerbates the 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 concise: two sentences plus an example and cost information. It front-loads the core purpose and uses clear language with no fluff.

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

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

The tool has no output schema, so the description should explain what the response contains. While it lists categories (clearances, recalls, adverse events), it does not describe the response structure, format, or whether it returns data for a single device or multiple. This leaves agents guessing about how to use the 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?

The schema has only one parameter 'name' with no description (0% coverage). The description adds that it's for 'any device or manufacturer' and gives an example ('insulin pump'), partially clarifying the parameter's meaning. However, it is ambiguous whether manufacturer names alone are valid inputs.

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

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

The description clearly states the tool provides an 'FDA medical-device risk dossier' including 510(k) clearances, recalls, and adverse events, specifying the domain (medical devices) and target users. This effectively distinguishes it from sibling tools like bundle_company_360 or bundle_drug_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 mentions it is 'for any device or manufacturer' and targets medtech, procurement, and compliance agents, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it differentiate from similar tools like leads_fda_devices or leads_fda_recalls.

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 cost ($0.005-$0.05 USDC per call) and that it's a single call returning multiple data types. No annotations provided, so description carries burden; lacks info on error handling, rate limits, or authentication.

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 with example and cost; front-loaded purpose; 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?

Lists key data types covered, but lacks detail on return format or field names. Without output schema, more specificity would improve completeness for a complex 360 dossier.

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 'name' has schema without description, but example call uses 'Stripe' to clarify it's a company name. Adds some meaning beyond type string, but limited 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 it's a corporate due-diligence dossier that returns multiple data types (firmographics, OFAC sanctions, entity risk score, KYB registry, legal/case-law exposure) in one call, distinguishing it from other bundle_360 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?

Explicitly targets investment, KYB, compliance, and corporate-intelligence agents, and indicates it consolidates multiple checks into one call. Does not explicitly state when not to use, but sibling context implies 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?

The description includes cost information ($0.005–$0.05 per call) which is a behavioral trait. However, it does not disclose any destructive actions, rate limits, or authentication requirements. Since no annotations are provided, the description carries the full burden, and it only partially covers 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 very concise: three sentences plus example and cost. It is front-loaded with purpose, and every sentence adds value. No unnecessary 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?

Given no output schema and no annotations, the description explains the data returned (WHOIS, DNS, TLS) and use cases. However, it does not describe the output format or structure, which could leave the agent uncertain about parsing the response.

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

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

There is only one parameter (domain) with no schema descriptions (0% coverage). The description provides an example ('stripe.com') but does not specify required format or constraints. This adds marginal 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 it returns WHOIS registration, DNS records, and TLS certificate for any domain, with use cases like security review and vendor due diligence. This distinguishes it from sibling tools like lookup_whois, lookup_dns, and lookup_ssl_cert by bundling them.

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

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

The description mentions use cases (security review, vendor due diligence, monitoring) but does not explicitly state when not to use it or compare to alternatives. The agent must infer that for individual lookups, separate tools might be preferred.

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 cover behavior. It mentions the data sources (FDA, clinical trials) and cost, but does not describe response format, size limits, or whether it is read-only. Moderate 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?

Very concise: three sentences plus example and cost. Front-loaded with purpose. No wasted text.

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

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

For a simple one-parameter tool with no output schema, the description covers main purpose, example, and cost. However, it lacks any description of the output structure or data fields, 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?

Only one parameter 'name' with no schema description. The description gives an example ('metformin') but lacks details on expected format (brand/generic, case sensitivity). With 0% schema coverage, more parameter guidance is needed.

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

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

Description clearly states it produces a drug dossier with FDA label, approval history, and clinical trials. The purpose is specific and distinguishes from sibling tools by focusing on drugs and data sources.

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 an example call and cost information but no guidance on when to use this tool vs. alternatives (e.g., lookup_drug_label, leads_fda_drugs). No when-not-to-use or context for selection.

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

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

No annotations provided, but description discloses cost range, that it runs on Base, and that it returns validation and enrichment data. Does not explicitly state read-only behavior or data retention, but overall good 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 efficient sentences covering purpose, use cases, an example, and cost. No fluff, 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?

No output schema, so the response format is undefined. While the purpose is clear, the description does not explain what fields or structure the returned data will have. Minimal completeness for a simple 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 has 0% coverage (no description for the 'email' parameter). Description only mentions 'any email address' and provides an example, but does not add details like format, domain validation, or restrictions.

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 provides 'deliverability validation + provider/domain enrichment' for any email, and lists specific use cases (lead-gen, CRM hygiene, fraud-screening). This distinguishes it from sibling tools like enrich_email or lookup_email_validate.

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 context for use (lead-gen, CRM hygiene, fraud-screening) and includes an example call, but does not specify when to avoid using it or mention 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 full burden. It mentions cost but fails to disclose whether the tool is read-only, requires authentication, has rate limits, or what it returns.

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 plus cost) and front-loaded with the purpose. However, it leans toward marketing language rather than technical clarity.

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

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

Given the single parameter, no output schema, and no annotations, the description is highly incomplete. An agent cannot determine how to use the tool correctly or what to expect in return.

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 no schema description, and the tool description does not explain what value to provide (e.g., sector name, query). Schema coverage is 0%, and the description adds no parameter guidance.

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 bundle of R&D/grant-funded buying-intent data, including recent NIH grant awards, and distinguishes it from other bundle_* tools by the grant focus.

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

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

No guidance on when to use this tool vs alternatives like signal_grant_radar or leads_nih_grants. The description only states what it is, not when it should be preferred.

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 ($0.005-$0.05 USDC) but does not mention authentication, rate limits, error handling, or data fusion details beyond the incomplete 'fused into'.

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

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

Two sentences, front-loaded with purpose, but the first sentence ends abruptly with 'fused into' suggesting missing content. Concise but incomplete.

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?

Description is incomplete (missing fusion detail) and does not describe output format or fields. For a bundle tool with no output schema and no annotations, more information is needed for effective agent use.

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% (no param description). The description adds that the single 'arg' parameter expects a 'lat,lon' format, but lacks specifics on decimal format, precision, or delimiters. Adds moderate value beyond 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 states the tool combines recent nearby earthquakes from USGS and active NWS weather alerts for any lat,lon location. Specific verb 'bundle' and resource 'Hazard 360' differentiate it 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 implies usage for fetching combined hazard data given a lat,lon pair. It does not explicitly state when to use this over separate earthquake or weather alert tools, but the context is clear from the sibling list and the 'premium bundle' phrase.

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; description mentions it's a premium bundle with cost but does not state whether it's read-only, destructive, or behavior on invalid input. Lacks explicit safety or side-effect 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?

Two sentences: first describes functionality, second gives use case and cost. No wasted words, well-structured.

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

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

For a simple one-parameter tool, description covers purpose, use case, and cost. Lacks return format or error handling, but acceptable given no output schema.

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

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

Schema has one parameter 'arg' with 0% coverage; description adds that arg is any IP address, providing essential meaning. However, lacks format details (e.g., IPv4 or IPv6).

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 network/ASN details plus geolocation for any IP address, fused into one call. Distinguishes itself from simpler IP lookup tools by being a premium bundle.

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

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

Mentions 'for fraud-screening' as a use case and implies it's for comprehensive data in one call. Does not explicitly contrast with sibling IP lookup tools, but context is 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 must convey behavioral traits. It states the tool bundles multiple checks in one call and mentions cost, but does not disclose potential side effects, rate limits, error behavior, or prerequisites. This is 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?

The description is concise with two sentences plus an example and cost note. It front-loads the purpose and use cases, though the example and cost could be integrated more smoothly. Overall, it is well-structured and efficient.

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

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

Given the tool's complexity (multiple data sources) and lack of output schema, the description could be more complete. It lists components but omits details on return format, pagination, error handling, and behavior for invalid inputs. The cost disclosure is a plus.

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 provides an example call with a company name. It does not explain the expected format, variants, or constraints of the 'name' parameter, leaving the agent with minimal guidance 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 provides a KYB/counterparty-risk verdict for a company in a single call, listing specific components like sanctions screening, entity risk score, and legal exposure. This distinguishes it from sibling bundle tools (e.g., bundle_company_360, bundle_risk_360) 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 description explicitly lists use cases: customer/vendor onboarding, merchant underwriting, compliance, and investment due diligence. However, it does not provide exclusions or mention alternative tools, leaving the agent to infer 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?

No annotations provided. The description only mentions cost and that data is 'fused', but does not disclose behavioral traits such as rate limits, idempotency, or whether it destroys state. The cost range is helpful but insufficient 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?

Two sentences with no redundancy. The first sentence efficiently conveys the tool's purpose and components; the second adds cost info. Every sentence earns its place.

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

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

For a single-parameter tool with no output schema and no annotations, the description covers the core purpose but lacks return format details, error handling, or examples. It is minimally viable but not 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 single parameter 'arg' has no schema description (0% coverage). The description implies arg is a ticker symbol ('any ticker'), which adds meaning, but lacks format or validation hints. Baseline 3 is appropriate given the minimal 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?

The description clearly states the tool returns a premium bundle with live stock quote, insider transactions, and 8-K events for a ticker. It identifies the resource (ticker) and the content, but the verb 'Market 360' is not an action verb, 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?

No guidance on when to use this tool versus alternatives like bundle_company_360 or other bundle tools. The description does not mention prerequisites or context-specific usage, leaving the agent without 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 must disclose behavioral traits. It mentions 'premium bundle' and 'in ONE call', but does not specify if the tool is read-only, has side effects (e.g., creating records), or requires authentication. The cost range is useful but does not cover behavioral aspects.

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 appears truncated ('sanct...'), which is detrimental. Including the cost is useful, but overall it is under-specified and not properly terminated.

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 a bundle likely returning multiple data points, but no output schema is provided and the description does not mention return values. It lacks explanation of what 'arg' accepts and what results to expect, making it incomplete for an agent to use 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 input schema has one required parameter 'arg' of type string with 0% coverage. The description does not explain what 'arg' should contain (e.g., a name, company name, or ID). Given the low schema coverage, the description should provide guidance but fails to do so.

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 it is a 'counterparty ONBOARDING gate' and mentions OFAC/SDN sanctions, clearly indicating it performs onboarding and sanction checks for persons or companies. It is distinct from sibling tools like bundle_company_360 which focus on companies only, but the description is cut off ('sanct...') 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?

No guidance is provided on when to use this tool vs the many sibling tools, such as bundle_company_360 or bundle_kyb_360. There are no prerequisites, success criteria, or when-not-to-use hints, leaving the agent to infer usage context.

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

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

No annotations are provided, so the description must carry the full burden. It mentions cost ($0.005–$0.05 USDC on Base per call) which is helpful, but fails to disclose safety, idempotency, or other behavioral traits. The description is insufficient 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 truncated and incomplete, reducing its conciseness. While it starts with a clear label, the cutoff undermines its utility.

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 parameter, no output schema, and many sibling tools, the description is severely lacking. It does not explain return values, prerequisites, or what 'firmographic enrichment' entails. The description is inadequate for effective tool use.

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 parameter 'arg' with no description and 0% schema coverage. The description mentions 'any company' but does not clarify the expected format (e.g., name, domain, ID). The agent has no guidance on how to fill the parameter.

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 mentions B2B prospect vetting and firmographic enrichment, but it is cut off and does not clearly differentiate from sibling tools like bundle_company_360 or bundle_kyb_360. The purpose is partially clear but lacks specificity.

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 phrase 'in ONE call' implies efficiency but provides no context for decision-making relative to siblings.

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

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

No annotations are provided, so the description must cover behavioral traits. It mentions cost and data sources but does not disclose rate limits, data freshness, authentication requirements, or whether changes are made. The description is adequate 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 two sentences plus an example and cost, all front-loaded with the core purpose. No redundant information; 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?

For a bundled tool with a single parameter and no output schema, the description covers the key aspects: purpose, included signals, example usage, and cost. It does not describe return format, but the tool name implies a comprehensive report, which is sufficient.

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

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

Schema coverage is 0% (no property descriptions), so the description must compensate. It explains that 'term' is a sector or term and provides an example ('medical devices'). This adds meaningful context beyond the type string alone.

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 specific verb+resource combination: provides regulatory and legal landscape including Federal Register, case-law, FDA device signals, and federal grant signals. It distinguishes itself from sibling bundle_* tools (e.g., bundle_company_360, bundle_domain_360) by focusing on regulatory content.

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 targets specific user roles (policy, legal, compliance, risk-monitoring agents) and provides an example call. It does not explicitly compare to alternatives or state when not to use, but the specificity implies usage context.

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

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

No annotations are provided, so the description must disclose behavioral traits. It mentions cost and example but does not state whether the operation is read-only, requires authentication, has rate limits, what happens on errors (e.g., missing repo), or any side effects. The description lacks sufficient transparency for safe invocation.

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: three sentences covering purpose, target audience, example, and cost. Every sentence adds value and is front-loaded. No extraneous information is present.

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

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

For a simple 2-param tool with no output schema, the description adequately indicates inputs and general output ('repository profile + latest releases'). However, it does not explain the structure or content of the profile/releases, nor error handling or limitations. This leaves some ambiguity for the agent.

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

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

Schema description coverage is 0%, so the description must compensate. It explicitly names 'owner' and 'repo' in the example call and states 'for any owner/repo', adding context beyond the schema's mere titles. However, it does not describe parameter constraints, types, or formatting in detail. Still, the example provides practical guidance.

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 'GitHub repo intelligence: repository profile + latest releases' for any owner/repo, with an example call. It differentiates from sibling bundle_* tools by specifying GitHub repos, and from single-purpose GitHub tools by offering a combined profile and releases. The target audience ('dev-tools, security, and OSS-monitoring agents') further clarifies its 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 mentions target agents but does not provide explicit guidance on when to use this tool versus alternatives like lookup_github, lookup_github_releases, or enrich_github. There is no when-not-to-use or comparison with sibling tools, leaving the agent to infer appropriate usage from tool names and purpose.

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 cover behavioral traits. It mentions cost per call and provides an example, but fails to disclose whether the operation is read-only, any required permissions, rate limits, or error states. For a compliance tool, this lack of transparency is a significant gap.

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

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

The description is extremely concise: three sentences covering purpose, target users, example, and cost. Every sentence adds value, and the structure front-loads the core functionality.

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 complexity (combines multiple risk checks), the description covers the basics. However, without an output schema, an agent may not know the structure of the returned data. For a compliance-critical tool, more detail on output fields would improve completeness.

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

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

Schema coverage is 0%, so the description must explain the 'name' parameter. It provides an example ('Acme Corp') but no semantic detail about name format, fingerprint, or what names are valid. This leaves ambiguity for an AI 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 performs OFAC sanctions screening, entity risk scoring, and KYB/vendor dossier compilation for any company. It uses specific verbs (screen, score, dossier) and explicitly names the three components, distinguishing it from sibling tools that offer individual functions.

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 target users: 'For compliance, onboarding, and vendor-risk agents.' This provides context but does not exclude use cases or compare with separate risk_* tools that perform similar but narrower tasks.

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 a cost per call ($0.005–$0.05) but provides no details on rate limits, authentication needs, return format, pagination, or what happens with empty results. Critical behavioral traits for a paid API 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 three sentences with no fluff. It front-loads the core purpose, includes a concrete example, and states cost separately. Every sentence adds value and fits in a compact space.

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

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

Given the tool's simplicity (1 param, no output schema, no annotations), the description provides a reasonable overview and example. However, it fails to specify the output structure (e.g., list of company objects with what fields) and does not differentiate enough from similar sibling tools, leaving the agent uncertain about the exact response format and tool selection 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 only parameter 'keyword' has no schema description (0% coverage). The tool description clarifies it expects an industry or keyword and provides an example ('fintech'), adding meaning beyond the bare schema. However, it lacks guidance on format, allowed values, or case sensitivity, so it is minimally 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 returns 'Sales-intent signals by industry/keyword' listing specific signal types (funding, contracts, grants, FDA review). It specifies the resource (companies) and verb (bundle/summarize), distinguishing it from sibling tools like signal_funding_radar (which focuses only on funding) or leads_federal_contracts (which provides raw contract 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 says the tool is for 'lead-gen, prospecting, and sales agents,' providing a clear use case. However, it does not explicitly state when not to use it or compare it to the many related sibling tools (e.g., signal_govcon_radar, signal_grant_radar, leads_funding). This omission reduces guidance for selecting this tool over 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 convey behavioral traits. It states that the tool returns company facts and recent SEC filings, and it mentions a cost range ($0.005–$0.05), which is helpful. However, it does not disclose response structure, rate limits, authentication requirements, or whether the operation is read-only.

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

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

The description is concise with three sentences covering purpose, use cases, an example, and cost. It is well-structured and front-loaded. No unnecessary information is present.

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 moderate complexity and lack of output schema, the description provides a high-level overview but lacks detail on the exact structure of the returned data. It does not specify whether the response includes both financial facts and a filings list, or how they are combined. This may leave agents uncertain about the output format.

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 is 'ticker', and the description adds that it accepts any ticker or CIK, which goes beyond the schema's simple 'Ticker' label. The example call with 'AAPL' reinforces this. This added context helps agents understand the parameter's acceptable values.

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 SEC corporate-financials intelligence, including company facts (XBRL financials) and recent SEC filings for any ticker/CIK. It mentions specific use cases (equity research, investment analysis, financial agents) and provides an example call. However, it does not explicitly differentiate from similar sibling tools like bundle_company_360 or separate SEC 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 indicates suitable use cases (equity research, investment analysis, financial agents) and mentions cost, which influences usage decisions. But it lacks explicit 'when not to use' guidance or alternatives, such as pointing to more specific tools (e.g., finance_sec_financials) for only financials.

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 only covers auto-detection and check-digit validation, omitting behavior on invalid input, return values, or other 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?

Very concise at two sentences, but the second sentence is truncated ('runs the correct check-di'), which reduces clarity and professionalism.

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?

Missing details on return values and error handling for a simple tool; no output schema and incomplete description leave 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 description adds meaning by specifying expected input lengths (12, 9, 7) for the single arg, compensating for 0% schema coverage, but lacks further details.

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

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

The description clearly states that the tool auto-detects whether input is an ISIN, CUSIP, or SEDOL and runs the correct check, distinguishing it from individual verify tools like verify_isin.

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?

Implicitly suggests use when identifier type is unknown, but lacks explicit guidance on when not to use or comparison with sibling tools like verify_isin, verify_cusip, verify_sedol.

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

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

Without annotations, the description carries the full burden. It mentions cost and that it's a single call, but fails to disclose whether it is read-only, required authentication, rate limits, or any side effects. The term 'vetting' suggests assessment but provides no 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, including a cost note, but appears truncated and lacks complete sentences. It could be more structured and complete without being verbose.

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 only one parameter and no output schema, the description should clarify what 'arg' should contain (e.g., a company name or identifier) and what the result looks like. It fails to do so, making the tool under-specified.

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

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

The input schema has one parameter 'arg' with 0% description coverage, and the description does not mention the parameter at all, providing no guidance on what value to pass.

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 vendor risk vetting, distinguishing it as a bundled service for supplier and third-party risk. However, the cut-off text leaves ambiguity about the exact output, and sibling tools like 'bundle_company_360' or 'risk_vendor' may overlap.

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 usage guidance is provided. The description does not indicate when to use this tool over alternatives such as 'bundle_kyb_360' or 'risk_vendor', nor does it specify prerequisites or exclusion cases.

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 reveals the output in years, months, and days, and states it calculates 'as of today'. Since no annotations are provided, the description carries full burden for behavioral disclosure. It does not explicitly state read-only behavior, but that is implied for a calculator. Cost disclosure adds 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 with the main purpose. It is efficient but appears cut off after 'and', which slightly harms completeness. The cost line is placed at the end, which is acceptable but unusual.

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 could better clarify the exact return structure. It mentions the units but not whether the result is an object or simple text. The single parameter is well-described in terms of input, but output details are inferred rather than explicit.

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 compensate. It specifies the input format ('YYYY-MM-DD') and hints at the parameter's role. However, it does not explain validation, error handling, or constraints beyond the format, leaving some 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 explicitly states that the tool calculates exact age in years, total months, and total days from a birthdate. The verb is implied (calculate), and the resource is clearly age. Among many sibling calculators, this one uniquely computes age, so it distinguishes itself well.

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 use cases like verification and eligibility, but does not specify when not to use this tool or contrast it with sibling tools such as 'lookup_age_calculator'. The cost hint is present but no explicit alternatives or exclusions are given.

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

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

No annotations are provided, so the description must carry the full burden. It discloses the cost range per call, which is a useful behavioral trait. However, it does not mention idempotency, error handling, rate limits, or any side effects, 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 very concise, with only three lines, and each sentence adds value: purpose, target audience, and cost. It is front-loaded and avoids unnecessary text.

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

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

Given the minimal schema (0% coverage), no output schema, and no annotations, the description is incomplete. It fails to explain the exact input format, provide examples, or describe the output, making it difficult for an AI agent to use correctly without additional 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 one string parameter with no description, resulting in 0% schema coverage. The description includes 'apr-compoundsPerYear' which hints at the expected format, but it is cryptic and does not provide clear format details or examples, insufficiently 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 the conversion from APR to APY, which is the core purpose. It also specifies the target audience ('fintech and savings agents'), helping distinguish from sibling calc tools. However, it lacks an explicit verb like 'convert' or 'calculate'.

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 a target audience ('fintech and savings agents'), implying the tool is intended for financial use cases. However, it does not offer guidance on when to use this tool versus alternatives (e.g., calc_compound) or any when-not 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, the description carries full burden. It discloses cost and returns (simplified ratio, decimal ratio, orientation) but does not detail error handling, input validation, or edge cases. 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 extremely concise: one sentence defining purpose and output, plus a cost note. Front-loaded with key information, 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?

Given the simple tool with one parameter and no output schema or annotations, the description covers input format, output types, and use case. Minor gaps: no mention of error cases or whether negative values are allowed, but largely complete.

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

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

The input schema provides no description for 'arg'. The description adds meaning by specifying the expected format 'WIDTH-HEIGHT' with an example (1920-1080). This compensates for 0% schema coverage well, though could be more explicit about separators or valid characters.

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 aspect ratio from a 'WIDTH-HEIGHT' string, returning simplified ratio, decimal ratio, and orientation. It specifies the use case 'For media, video,' distinguishing it 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?

The description implies usage for media/video but provides no explicit guidance on when not to use this tool or alternatives. No differentiation from sibling calculators beyond the specific domain.

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 notes that the calculation is an 'estimate only' and lists cost. However, it does not disclose any behavioral traits like rate limits, authentication needs, or data persistence, though as a calculator such traits may be less critical.

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 plus a cost line. The first sentence captures the core purpose and input format efficiently, and the cost information is valuable for cost-aware agents. No 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?

For a simple calculator with one parameter and no output schema, the description covers input format and purpose. It lacks output format (e.g., BAC value as percentage), but this is arguably inferable. Cost is included. Overall 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 has a single parameter 'arg' with no description (0% coverage). The description compensates by explaining the expected format: 'weightKg-drinks-hours[-sex]', which 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 a blood-alcohol estimate using the Widmark formula, specifies the input format as 'weightKg-drinks-hours[-sex]', and states it is an estimate only. This distinguishes it from sibling calculators like calc_bmi or 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 mentions 'For health, hospitality, and safety' giving some context, but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusion criteria or sibling differentiation provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost per call ($0.005-$0.05 USDC on Base), which is useful. However, it does not describe error behavior, return format, or any side effects. For a simple conversion tool, this is acceptable but minimally 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?

Two sentences: the first explains the purpose and syntax, the second states the cost. No unnecessary words, front-loaded with the core purpose. Every sentence earns its place.

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

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

For a simple conversion tool with one parameter, the description is mostly complete. It covers input format, base range, and cost. It could mention the output format (e.g., returns the converted number as a string) but is not strictly necessary given low complexity. Slight gap, but overall 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 has one parameter 'arg' with no description (0% coverage). The description fully compensates by explaining the exact format: 'number-fromBase-toBase' with an example. This adds complete 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 it performs number base conversion, specifies the input format 'number-fromBase-toBase', supports bases 2-36, and gives an example (255-10-16). This distinguishes it from sibling calc tools like calc_convert (unit conversion) and other calculators.

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 'For dev/encoding agents,' giving a clear context of use. It implies this is for base conversion rather than other calculations, but does not explicitly state when not to use or list alternatives. Still, context is 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 provided; description discloses the required input format and cost but does not explain error handling, output format, or behavior on invalid input. 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?

Extremely concise: two sentences immediately convey the purpose, usage pattern, audience, and cost. No unnecessary 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?

Given the tool's simplicity (one parameter, no output schema), the description covers the core usage. However, it omits any mention of return value or error conditions, which would be necessary for complete understanding.

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 provides essential meaning for the single parameter, specifying the expected pattern. This compensates for the schema's lack of documentation.

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 does Base64 encode or decode, and provides a pattern '{encode|decode}/{text}'. However, it does not explicitly differentiate from the sibling 'lookup_base64', which likely exists for decoding.

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

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

Specifies target audience 'For dev, data-pipeline, and API agents' and mentions cost. But lacks when-not-to-use guidance 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 exist, and the description does not disclose behavioral traits such as read-only status, error handling, or output format. Cost is mentioned but not 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 brief and front-loaded, but the second sentence appears truncated, reducing clarity. The cost information is included but may not be essential.

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

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

For a simple tool with one parameter and no output schema, the description lacks completeness—it does not describe the return value, error cases, or edge behaviors.

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 description clarifies the expected format of the single parameter (e.g., 'encode/hello'), adding value beyond the bare schema, but does not fully specify all possible inputs.

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

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

The description clearly states it converts text to/from binary, with a specific calling convention, distinguishing it from sibling tools like calc_base64 and 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?

Only the calling format is provided; no 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?

No annotations are present, so the description must disclose behavioral traits. It mentions cost but does not explain what happens on invalid input, the exact format required, or any side effects. This is insufficient for an unannotated 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 relatively short and front-loaded with the core purpose, but it is cut off mid-sentence and includes cost information that could be considered extraneous. It could be more structurally sound by completing the thought and separating cost.

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

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

Given the lack of output schema and annotations, the description should provide complete context for the string-based input. It does not specify valid values, examples, or error handling. The sibling list includes many similar calc tools, but no differentiation is provided.

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

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

The single parameter 'arg' has no schema description (0% coverage). The description hints at the format 'weight-height[-system]' and gives example units (lb/in, kg/cm) but does not specify the exact structure, delimiters, or order. This leaves significant ambiguity 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 it calculates BMI with weight, height, and system (imperial or metric), and returns a category. It distinguishes itself from other calc_* tools by specifying the resource. However, the description is cut off ('and in') 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 mentions 'For health, fitness, and in...' but is incomplete and provides no guidance on when to use this tool versus alternatives like calc_bmr or calc_base. No exclusions or context for selection are given.

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

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

No annotations provided, so description must disclose behaviors. It reveals cost ($0.005–$0.05 per call) but omits whether it is read-only, what side effects exist, or data persistence. The cost info is useful, but overall behavioral transparency is low.

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 concise but truncated ('Fo...'), indicating incompleteness. The cost note is appended but not integrated. While few sentences are used, the truncation harms structural integrity.

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 provides the core formula and input pattern. However, it lacks output format, error handling, or limitations. Given the low complexity, it is moderately complete but not fully sufficient.

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

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

Schema has one string parameter 'arg' with no description. The description adds a pattern: 'weightKg-heightCm-age-sex[-activity]', which clarifies expected format. However, it does not explain individual components (e.g., units for weight, valid sex values). Schema coverage is 0%, so description provides partial but not complete semantics.

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

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

The description states it computes basal metabolic rate and TDEE using the Mifflin-St Jeor formula, and provides an input pattern. However, the description is truncated ('Fo...'), which partially obscures the purpose. Among siblings like calc_bmi, the name and description give enough differentiation, but truncation 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?

No guidance on when to use this tool vs. alternatives (e.g., calc_bmi, calc_age). No mention of prerequisites or exclusions. The description solely explains what the tool does without contextual usage advice.

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

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

Without annotations, the description must disclose behavioral traits. It notes the result is an 'estimate' and includes cost, but fails to mention idempotency, permissions, or side effects. The cost info is helpful but insufficient.

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, with only two substantive sentences and a cost note. It is efficiently front-loaded but could be expanded slightly for clarity without losing conciseness.

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 the output unit (m^2) and purpose, but the input format is insufficiently specified. It covers core intent but leaves gaps for correct invocation.

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

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

With 0% schema coverage, the description must compensate. It hints the input format is 'heightCm-weightKg' but does not specify the expected delimiter or units (e.g., '170-70' vs '170cm-70kg'). This ambiguity undermines parameter 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 the tool calculates body surface area using the Mosteller formula from height and weight, and specifies the purpose for medical and dosing agents. It effectively distinguishes from sibling calc_ tools by naming a specific medical metric.

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 applicability to medical and dosing agents but offers no guidance on when to use this versus related tools like calc_bmi or calc_bmr. No exclusions or alternatives are provided.

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

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

No annotations are provided, so the description bears the full burden. It only mentions cost, which is a behavioral trait, but fails to disclose whether the tool is read-only, what happens with invalid input, or any side effects. 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 description is two sentences plus a cost note, all front-loaded. It is concise and to the point, though the cost line might be better placed in a separate field. No waste.

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 description does not specify the output format or examples. Given no output schema, the agent must infer that output is a human-readable string. The input format is also vague, making the description incomplete for reliable automation.

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 for the single 'arg' parameter. The description adds 'raw bytes value' but does not specify the expected format (e.g., integer string, with or without unit). This is insufficient for an agent to correctly construct the 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 formats bytes into human-readable KB/MB/GB/TB, with a specific verb and resource. The sibling tools include many calc_* tools, but calc_bytes is uniquely dedicated to byte formatting, making its 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 description mentions target audiences (devops agents, storage-monitoring bots) but does not provide when-not-to-use guidance or alternatives like calc_unit or calc_convert. The context is implied but not 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 provided, so description carries full burden. Discloses cost per call ($0.005–$0.05), which is a behavioral trait. Implicitly indicates read-only calculation. Could mention any error handling or constraints.

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

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

Two sentences, no wasted words. Front-loads purpose, gives example, mentions outputs and cost. Excellent conciseness.

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, description covers input format, output fields, and cost. Could add validation notes or error handling, but sufficient for agent use.

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 0% description coverage for parameter 'arg'. Description compensates by providing an example ('192.168.1.0/24') and explaining input format (CIDR notation). Effectively tells agent what to provide.

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 explicitly states it's a CIDR subnet calculator for IPv4/IPv6, provides an example input, and lists outputs (network, broadcast, netmask, address count, usable hosts). Clearly distinguishes from 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?

Usage is implied through the tool name and description (CIDR calculations). No explicit 'when to use' or 'when not to use' guidance. Example provides some context but no alternatives 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?

With no annotations, the description describes the core behavior: converting color formats and returning three representations. It does not disclose any side effects, authentication needs, or limitations (e.g., invalid input handling). The cost note is extra but not behavioral. Minor gaps prevent a 5.

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

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

The description is short but incomplete (trails off with 'and'). The cost line is tangential and adds noise. A more complete and focused description would be better. The structure is acceptable but the incompleteness reduces effectiveness.

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 converter, the description covers inputs, outputs, and intended use. No output schema exists, so the description's mention of returned formats is essential. Some details like error handling or format nuances are missing, but it's largely complete.

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

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

Schema coverage is 0%, so the description must add meaning. It explains the accepted string formats: hex, 'rgb-255-136-0', or 'r,g,b'. This provides crucial context beyond the schema's bare JSON type. However, it lacks details like whether '#' is required for hex or case sensitivity, preventing a perfect score.

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

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

The description clearly states it is a color converter, lists accepted input formats (hex, 'rgb-255-136-0', 'r,g,b'), and outputs (hex, rgb, hsl). The verb 'converts' combined with format details makes the purpose highly specific and distinct among sibling tools.

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

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

The description mentions 'For design, theming, and' suggesting contexts for use, but it trails off and provides no explicit guidance on when to use this tool versus alternatives. No exclusion criteria or when-not-to-use information is given.

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

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

No annotations are provided, so the description must cover behavior. It discloses input format and output (nPr/nCr), but lacks details on error handling, valid input constraints, or output structure. Minimal but adequate for a simple calculator.

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 plus a cost note. It is front-loaded with purpose, concise, and 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?

For a simple tool with no output schema or annotations, the description is reasonably complete regarding purpose and input format. However, it doesn't specify the exact return format or error behavior, leaving some gaps for an agent to infer.

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

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

The schema has 0% description coverage for the 'arg' parameter. The description explains the format ('n-r') and what it returns, adding essential meaning beyond the schema's bare 'Arg' label.

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 calculates permutations and combinations, specifies input format 'n-r', and output nPr and nCr. It differentiates from other calc_* tools by being specifically for 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 indicates it's for 'statistics, probability, and math agents', providing clear usage context. It doesn't explicitly exclude alternatives, but the target audience is well-defined.

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 burden. It mentions the input format and return values (future value, total interest) but lacks details on determinism, error handling, or data source. The cost information adds some 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 with two sentences: one for purpose and usage pattern, one for cost. No extraneous information, 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?

For a simple calculator with one parameter and no output schema, the description covers the essential purpose and return values. However, the parameter syntax could be more explicit to avoid user confusion about input formatting.

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 no description (0% coverage). The description hints at the expected format 'principal-rate-years[-timesPerYear]' but does not specify delimiters, data types, or constraints, leaving significant 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 identifies the tool as a 'Compound-interest / future-value calculator' and provides a usage pattern ('principal-rate-years[-timesPerYear]'). This distinguishes it from sibling calc_* tools like calc_loan or calc_salary.

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 compound interest calculations but does not explicitly state when to use it over alternatives, nor does it provide exclusions or context for other financial calculations.

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 transparently states the data source (ECB reference rate), the output (converted amount), and the cost range ($0.005–$0.05). It does not disclose error handling or rate limits, but for a simple conversion tool, 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?

The description is two sentences, concise and front-loaded with the core purpose and format. The second sentence adds cost info without unnecessary words. Every clause adds value, and there is no redundancy.

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

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

For a simple one-parameter tool with no output schema, the description is largely complete: it explains the input format, the data source, and the cost. It could optionally mention the return type (e.g., a number), but that is not essential for an agent to use it correctly.

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

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

The input schema has a single required parameter 'arg' with no description (0% coverage). The description adds meaning by specifying the expected format: 'amount-FROM-TO' with an example. This compensates for the missing schema documentation, although it does not detail valid currency codes 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 it is a currency converter with a specific format 'amount-FROM-TO' and an example '100-USD-EUR'. It distinguishes itself from sibling tools like calc_unit (general unit conversion) and lookup_currency_historical (historical rates) by specifying it uses the latest 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 implies usage for currency conversion using ECB rates but does not explicitly state when to use this tool versus alternatives like lookup_currency_historical or calc_unit. No exclusions or prerequisites are mentioned, making the 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?

No annotations provided, so description carries full burden. It mentions cost but fails to disclose read-only nature, side effects, authentication, or rate limits. Average for a simple calculator.

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?

Short, front-loaded with purpose and hint. Cost line is extra but not disruptive. Could be slightly more structured, but no wasted sentences.

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 purpose, call format, audience, and cost. For a simple one-parameter tool with no output schema, this is adequate. Could mention expected output, but not essential.

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 0% coverage (just 'Arg'), but description adds meaning with 'call as 'sizeGB-speedMbps'', compensating for lack of schema documentation.

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 computes transfer time given file size and link speed, with explicit call format hint. Distinct from other calc tools among siblings.

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

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

Provides target audience ('for network agents, devops bot') and call format, but lacks explicit when-to-use guidance or comparison to alternatives. No exclusions 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 provided. Description fails to disclose behavioral traits such as read-only nature, authentication requirements, or side effects beyond the cost, leaving the agent with limited understanding of the tool's 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?

Extremely concise: two sentences and a cost line, front-loaded with the core purpose, with zero extraneous information.

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

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

No output schema, yet description does not clarify return format (e.g., whether it returns days only or separate values for weeks/years). Also lacks details on error handling or edge cases, making it incomplete for a one-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?

With 0% schema description coverage, the description adds the format constraint 'YYYY-MM-DD_YYYY-MM-DD', which is critical for correct invocation. However, it does not explain the separator or any other parameter nuances.

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 the tool calculates date differences in days, weeks, or years, specifies the input format 'YYYY-MM-DD_YYYY-MM-DD', and distinguishes from sibling calc_* tools like calc_age and calc_due_date.

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 mentions use cases (scheduling, billing, logistics) but lacks guidance on when not to use or when to prefer alternative date-related tools like calc_age or calc_due_date.

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 description must cover behavioral traits. It mentions formula but does not disclose edge cases (e.g., negative ratio, input validation) or return format.

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

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

Description is short but truncated, and includes cost info that could be separate. Front-loaded key info but ends abruptly.

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?

Simple tool but lacks output format description and parameter details. No output schema to compensate.

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 description adds meaning by stating the argument is a 'powerRatio'. However, it doesn't specify expected format (numeric string) and parameter name mismatch reduces clarity.

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 formula and domain (audio-engineering, RF/signal analysis). However, it refers to parameter as 'powerRatio' while schema names it 'arg', causing confusion. Also truncated.

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 target audience ('audio-engineering agents, RF/signal analysis bots') but no explicit when-not or alternatives among siblings.

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

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

No annotations provided, so description is the sole source. It discloses pricing ($0.005–$0.05 USDC per call) which is useful. However, it does not state that the tool is idempotent, what side effects exist, or whether authentication is needed. Being a computation, it's likely safe, but transparency is minimal.

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

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

Two sentences plus cost info – no fluff. The main purpose is in the first sentence. Could be slightly more structured (e.g., separate cost line) but overall 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?

Given the tool's simplicity (one parameter, no output schema), the description covers the core function and pricing. It lacks mention of return format or units, but for a density tool those may be inferred. The sibling set is huge, but the description provides enough context for basic use.

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 a string with 0% schema description coverage. The description suggests a format ('mass-volume') but does not specify units, delimiters, or expected value ranges. It adds some meaning but does not fully 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?

Clearly states it computes density (mass/volume) with a formatting hint 'call as mass-volume'. Provides target audience (materials-science, shipping, engineering). However, it does not differentiate from numerous sibling calc_* tools (e.g., calc_bmi, calc_force) that also compute derived quantities.

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?

Gives a formatting hint but no explicit guidance on when to use this tool vs alternatives. No mention of prerequisites, data sources, or limitations. The sibling set includes many calculators, but the description does not help an agent decide when to pick density over others.

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, and the description does not disclose behavioral traits such as whether the tool is stateless or has side effects. It implies a pure calculation but only mentions cost, leaving the agent uncertain about safety or data persistence.

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 (three sentences) but somewhat cryptic and lacks structure (no examples or clear separation of input/output). The cost line adds value but is not essential for tool usage.

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 output schema and annotations, the description should provide more details on the output format, units, and error handling. It only vaguely mentions 'final volume + solvent to add', which is insufficient for precise use.

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 no schema description, but the description adds meaning by specifying the input format 'c1-v1-c2' and indicating output. However, it lacks examples, units, or clarification on delimiters, which could hinder 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 solution dilution calculations using the C1*V1=C2*V2 formula, specifying the input format 'c1-v1-c2' and output as final volume and solvent to add. It distinguishes itself from sibling calc_* tools by focusing on a specific formula and 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?

The description mentions use in 'lab, chemistry, and brewing agents' but provides no guidance on when to use this tool versus alternatives like calc_molarity or other calculators. No when-not-to-use or comparison with 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 discloses the cost per call and the input format, but does not explicitly state side effects (none expected) or error handling. It is adequate but lacks detail on behavior for invalid inputs or edge cases.

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 short sentences plus cost note), front-loaded with the core purpose, and contains no unnecessary words or repetition.

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

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

While it tells what the tool returns (sale price, savings, effective %), it does not specify the output format (e.g., JSON object keys) or mention validation. Given no output schema, this is a notable gap, but for a simple calculator it is minimally sufficient.

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

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

Schema coverage is 0%, but the description explains the single parameter 'arg' must be in the format 'price-percentOff[-percentOff2…]', adding essential context beyond the schema. It could benefit from examples or value 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?

The description clearly states the tool computes 'Sale price, savings, and effective %' for a discount format, distinguishing it from sibling calculators like calc_percentage or calc_tip. It specifies the exact input pattern (price-percentOff[-percentOff2…]) and explicitly mentions e-commerce use.

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 (e.g., calc_percentage). The description implies it's for e-commerce discounts but does not provide when-not-to-use or compare with 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?

With no annotations, the description must disclose behavior. It mentions the great-circle formula and input format but omits output format, units (km/miles), error handling, and edge cases (e.g., invalid coordinates). 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 extremely concise: one sentence for the core behavior plus a cost note. No unnecessary words, and the most critical information (format) 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 lack of output schema, the description should clarify return values (e.g., distance in km). It also lacks usage guidelines. For a simple tool, it's partially complete but misses key contextual details like output and error handling.

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

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

The single 'arg' parameter lacks schema description (0% coverage). The description compensates well by specifying the exact comma-separated format and providing an example, which adds essential meaning beyond the schema's type definition.

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 calculates the great-circle distance between two lat/lon points, which is a specific verb and resource. It distinguishes itself from sibling calc_ tools (e.g., calc_age, calc_bmi) by being the only one for geographic distance.

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 computing distance from coordinates but lacks explicit guidance on when to use or alternatives. For example, it doesn't mention that lookup_geocode could provide coordinates or that this tool is for direct coordinate distance.

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 states that the tool returns due date, current gestational age, and trimester, giving some behavioral insight. However, it does not disclose error handling, input validation, or other behavioral traits. No annotations are present, so the description partially compensates but is incomplete.

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 includes extraneous cost information. It is truncated, which may confuse. It is front-loaded with purpose but could be more concise by omitting cost (which might belong elsewhere) and completing the sentence.

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

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

The description covers the main output fields (due date, gestational age, trimester) and input format. However, without output schema or annotations, it lacks details on potential errors, range limitations, or exact output structure. It is minimally adequate for a straightforward calculator.

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 no schema description (0% coverage). The tool description specifies that the input is a date in YYYY-MM-DD format representing the last menstrual period. This adds critical meaning beyond the bare schema, helping the agent format 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 indicates the tool calculates pregnancy due date, gestational age, and trimester from the last menstrual period date. It is specific to pregnancy, distinguishing it from sibling calculators like 'calc_age' or 'calc_bmi'. However, the description is cut off, 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, caveats, or context for usage. Among many sibling calculators, the description does not help differentiate usage 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?

No annotations provided, so description carries full burden. It mentions safety and supported operations, but lacks details on error handling, input limits, or return format. Adequate for a simple tool 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 short and to the point, but includes cost information which may be extraneous. Front-loads key functionality. However, the truncated ending reduces structure 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?

Given the tool's simplicity and lack of output schema, the description should at least mention what is returned (e.g., numeric result). It does not, leaving return behavior unspecified. Incomplete for a holistic understanding.

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 coverage, the description compensates by explaining the parameter 'arg' is the math expression. However, it does not provide examples or format specifics, relying on the user's 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 is a math expression evaluator supporting common operators and functions. It distinguishes itself from specific calc_ siblings by being general-purpose. However, the description appears truncated, 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 use for evaluating math expressions but does not explicitly compare to alternatives like calc_bmi or calc_age. No guidance on when not to use it 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 full burden but only states core computation and call format. It does not disclose behavioral traits like whether it's read-only, idempotent, error handling, or return format.

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

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

The description is brief and front-loaded with the main functionality. The cost information is extra but potentially useful. Every sentence adds value, though the cost line could be separated.

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 an output schema, the description should elaborate on return values. It only states 'in newtons', but doesn't describe the response structure, success/error states, or pagination. Incomplete for a tool with no output schema.

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

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

The schema has 0% coverage for the single parameter 'arg'. The description adds meaning by specifying the expected format 'massKg-accelMs2', but does not fully clarify that it's a hyphen-separated string of mass and acceleration, leaving 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 it computes force using F=ma in newtons, and provides the call format. It distinguishes from general calc tools by specifying the physics formula, but doesn't explicitly differentiate from other physics calculators like calc_kinetic_energy.

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 intended users ('physics-solver agents, engineering-design bots, sim') implying when to use, but does not provide explicit guidance on when not to use or alternatives among the many sibling physics 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?

No annotations exist, so the description must fully disclose behavior. It states the input format and outputs but omits details like whether the tool is read-only, error handling, or if it modifies any state. The cost note is useful but does not cover 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 concise with two sentences and a cost line. Each part adds value: core function, target audience, and cost. 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?

Given the tool's simplicity (single parameter, no output schema), the description covers the essential purpose and input format. It lacks details on return format or error conditions, but the core use case is well communicated.

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 parameter description (0% coverage). The description clarifies that the 'arg' should be a fraction formatted as 'numerator-denominator', adding meaningful context beyond the schema. However, it does not specify constraints like integer vs. decimal inputs.

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

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

The description clearly specifies the action: 'Simplify a fraction' with outputs 'lowest terms + decimal + percent'. It also identifies the target audience ('education and engineering agents'), making the tool distinct among many calc_ siblings.

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

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

The description mentions 'For education and engineering agents' as a usage context, but does not provide explicit guidance on when to use this tool versus other calc_ tools or alternative approaches. No exclusions or when-not-to-use are stated.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only states it calculates a cost and mentions a cost per call (pricing). It does not disclose whether the tool is read-only, idempotent, or requires authentication. Since annotations are absent, the description fails to adequately describe behavioral aspects.

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 (three sentences) but truncated, ending with 'and fleet ag' which suggests incomplete thought. The first sentence is front-loaded with purpose and format. Despite conciseness, the truncation harms structure and completeness. Not all sentences earn their place due to the incomplete final word.

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 low complexity (one parameter, no output schema), the description provides the basic purpose and input format hint. However, it omits return value details, error handling, unit conversion constants, and valid input ranges. The truncation further reduces completeness. An agent would lack sufficient context for reliable use.

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 string parameter 'arg' with no description (0% coverage). The description adds meaning by indicating the expected format: 'distance-efficiency-pricePerUnit' with unit options (mi+mpg+$/gal or km+km/L+$/L). This provides partial semantics, but the format is vague and not fully structured. Baseline for zero coverage is low, so this extra info raises the score to 3.

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

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

The description states 'Trip fuel cost for distance-efficiency-pricePerUnit' which clearly indicates the tool calculates fuel cost based on distance, efficiency, and price. It also specifies the unit formats (mi+mpg+$/gal or km+km/L+$/L). However, the description is truncated ('and fleet ag') and does not fully complete the sentence, 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 mentions 'For logistics, travel, and fleet agents' but provides no explicit guidance on when to use this tool versus alternatives. Among sibling tools, there are many other calc_* tools (e.g., calc_distance, calc_convert) but no differentiation or when-not-to-use advice. The context is too broad to be helpful.

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

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

No annotations are provided, so the description carries the full burden. It does not disclose whether the tool is read-only, requires authentication, has rate limits, or any side effects. The only extra info is the cost, which is not behavioral.

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

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

The description is brief with two sentences plus a cost line. It is front-loaded with purpose. The cost line, while informative, is slightly extraneous for core tool selection but doesn't harm conciseness.

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 a single string parameter with no schema description and no output schema, the description should explain what the tool returns. It only hints at input format but says nothing about output type (e.g., number, string). The context is incomplete for reliable 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 description adds a format hint 'principal-annualRatePct-years[-compoundsPerYear]' for the single string parameter 'arg', which has no schema description. This provides some meaning but is ambiguous (e.g., are these fields separated by hyphens? what data types?). Better structured guidance would improve it.

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

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

The description clearly states 'Future value of an investment' and provides a format hint for the input string, distinguishing it from other calc_* tools like calc_compound or calc_present_value. However, it could be more explicit by saying 'Calculate the future value' rather than just 'Future value'.

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 investment and savings agents' but gives no specific guidance on when to use this tool versus alternatives like calc_compound or calc_loan. No exclusions or context for 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?

No annotations are provided, so the description must disclose behavioral traits. It mentions return values (area, perimeter, shape metrics) and cost, but lacks details on error handling, input validation, or safety. With no annotations, this is insufficient for a complete behavioral profile.

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 core functionality, but the phrase 'For C' appears truncated, indicating poor structure. It includes cost information which is extraneous for selection. Adequate but 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?

Given the tool's simplicity (one required parameter, no output schema), the description covers core functionality and cost. However, it lacks details on output format, error behavior, or valid input values, leaving gaps that are not filled by annotations or schema. Reasonably complete for a basic calculator but not fully.

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 parameter 'arg' with 0% documentation coverage. The description adds partial meaning by providing format examples (e.g., 'circle-R'), but does not fully specify allowed shapes, case sensitivity, or expected syntax. It helps but is incomplete.

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 2D geometry metrics (area, perimeter, shape metrics) for specific shapes encoded in the 'arg' string, like 'circle-R', 'square-S', etc. This distinguishes it from other calculator tools among siblings, though 'For C' appears truncated, 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?

No explicit guidance on when to use this tool vs alternatives. The description implies usage for geometry calculations but does not provide when-not-to-use or compare to other calc tools. Absence of usage context limits decision-making.

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 input format and cost, but with no annotations, it carries full burden for behavioral disclosure. It does not mention error handling, response format, or side effects, leaving gaps in 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 two concise sentences: one for functionality, one for cost. Every sentence serves a purpose with no unnecessary detail. Slightly front-loaded.

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

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

For a simple calculator with one parameter and no output schema, the description covers input format and output type (GPA). It is adequate for an agent to understand the tool's role, though an example 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?

Since schema coverage is 0%, the description adds significant value by explaining that the 'arg' parameter is a comma-separated grade list with optional credits per grade. This provides 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 states the tool calculates GPA on a 4.0 scale from a comma-separated grade list, with optional weighted GPA via credits. This verb+resource combination is specific and differentiates it from sibling calculator tools.