Toolcall

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

With no annotations, the description fully discloses behavior: it runs prompts across multiple AI engines (one web-grounded), returns a JSON report, and mentions payment ($0.25 USDC per call). It clarifies the tool is query-only (no side effects) and includes the payment mechanism. Minor missing details (e.g., number of engines) but the core transparency is strong.

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 purpose, a list of output elements, and a one-sentence payment note. Every sentence carries essential information, and the key action ('See what AI models say...') 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 tool with no output schema, the description provides a thorough list of report contents (mention, sentiment, etc.) and notes the payment method. It covers inputs, outputs, and constraints well. The only gap is the lack of explanation for 'web-grounded' and how many AI engines are queried, but overall it gives enough context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so each parameter already has a clear description in the input schema. The tool description does not add new semantics beyond repeating 'domain improves accuracy' and 'category for best-of prompts' which are already in the schema. Baseline 3 applies because the description adds no extra param-level value.

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

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

The description starts with a specific verb+resource: 'See what AI models say about a brand right now.' It enumerates the exact outputs (mention, position, sentiment, etc.), making the tool's function unmistakable. Among 30 diverse sibling tools (chem-compat, company-verify, etc.), this is clearly unique.

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 when-to-use or when-not-to-use guidance is given. The description implies use for brand reputation analysis but does not compare to sibling tools like 'company-verify' or 'web-search' which might offer overlapping data. The agent must infer usage context from the tool's name and description alone.

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

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

With no annotations provided, the description carries full burden. It discloses the output format (JSON with verdict, reason, effects, confidence), the data sources (PubChem GHS, CAMEO reactivity), and even includes pricing information. It does not mention limitations, error handling, or authentication needs, but the core behavioral traits are well covered.

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 the core purpose front-loaded as a question. It efficiently covers input, output, data sources, and pricing. The pricing note is somewhat extraneous but relevant for cost-aware agents. One sentence could be dropped without losing essential information, but overall it is 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?

Given the tool's simplicity (two string inputs, no output schema), the description is largely complete. It explains the verdict categories and expected effects, and mentions data provenance. Missing details include error handling for invalid chemical names and behavior when no data is found, but these are minor for a tool of this scope.

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

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

Schema description coverage is 100% (both 'a' and 'b' have descriptions in the schema). The tool description adds only a restatement: 'Pass two CAS numbers or names.' This does not provide meaningful additional context beyond the schema, so the baseline score of 3 applies.

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

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

The description uses a clear verb ('determine if safely mixed or stored') and specifies the exact resource (chemical compatibility between two substances). It lists specific output fields (verdict, reason, effects, confidence), making it highly distinguishable from sibling tools like 'chem-safety' which likely covers broader safety topics.

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

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

The description clearly states the usage context: when you have two chemicals and need to check mixing/storage safety. It tells the user to pass two CAS numbers or names. However, it does not explicitly mention when not to use this tool or suggest alternatives (e.g., 'chem-safety' for single-chemical hazards).

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 discloses live PubChem sourcing, JSON response, and cost ($0.03 USDC per call). Does not cover error handling or rate limits, but sufficient for a read-only query.

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?

Four concise sentences with no wasted words. Front-loaded with main function, followed by specifics and cost. Optimal length.

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 description explains return values (GHS, first-aid, fire-fighting, etc.) and format (JSON). Covers essential elements for a data retrieval tool. Slightly lacks detail on structure.

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

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

Schema coverage is 100% with descriptions for both parameters. Description adds minor context ('by CAS number or name' and example), but does not significantly enhance beyond 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 provides a 'Full safety profile for any chemical by CAS number or name', listing specific data categories (GHS, first-aid, fire-fighting, etc.). It distinguishes from siblings like 'chem-compat' and 'hs-classify'.

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 specifies inputs (CAS number or name) and context (safety data). Lacks explicit 'when not to use' or alternative tool references, but sibling context provides 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?

Discloses the paid nature ($0.02 per call via x402 on Base) and data sources. Without annotations, it covers cost, query methods, and response format, but lacks details on rate limits or error handling.

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

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

The description is dense and informative but could be more concise. It front-loads the main purpose but includes several details that make it lengthy.

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, it describes the data returned for each query type (e.g., status, SIC codes for UK). It covers main use cases and data sources, but misses error handling and edge cases.

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

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

Adds meaning beyond the schema by explaining the purpose of each parameter and their interactions (e.g., name search with country=GB). The schema already covers descriptions, but the description provides practical examples and data source context.

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 verifies a business via multiple official registries (EU VAT VIES, GLEIF LEI, UK Companies House) and lists specific query methods, distinguishing it from 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?

Describes how to query using different parameters but does not explicitly state when to use this tool over alternatives or provide exclusions for specific 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?

With no annotations provided, the description fully discloses behavioral traits: data source (public RPC), freshness (within 15 seconds), return format (JSON), and cost ($0.002 USDC per call). This provides complete transparency about 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?

The description is two sentences: one for functionality and one for cost/details. It is appropriately sized, front-loaded with the core purpose, and every sentence adds value. 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 (1 parameter, no output schema), the description covers the essentials: what it returns, data source, freshness, and cost. It lacks explicit detail on the JSON structure, but the output is straightforward. A more complete description could specify the response fields, but not necessary for a gas price tool.

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

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

The input schema has high coverage (100%) with a description for the 'chain' parameter. The description repeats the permissible values (Base or Ethereum) but adds no additional semantics beyond the schema. Baseline of 3 is appropriate.

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

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

Clearly states it retrieves current gas price and latest block for Base or Ethereum mainnet. The verb 'Current' and resource 'gas price and latest block' make the action explicit. No sibling tool does this, so differentiation is inherent.

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?

Implies usage when needing gas prices for these chains, but does not provide explicit when-to-use vs. alternatives. No guidance on prerequisites or exclusions. However, no competing sibling exists, so the lack of explicit alternatives is less critical.

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

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

With no annotations provided, the description carries the full burden. It discloses data freshness ('fresh within 60 seconds'), response format ('JSON'), and payment details ($0.003 per call via x402 on Base). This is thorough and beyond minimal expectations.

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

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

The description is a single, well-structured paragraph of three sentences. Every sentence adds essential information: purpose, usage, and logistics. No word is wasted.

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

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

For a simple two-parameter tool without output schema, the description covers functionality, query method, freshness, response type, and cost. It does not detail error handling or full response structure, but given the tool's simplicity, this is comprehensive.

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

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

Schema coverage is 100%, but the description adds value by providing examples ('e.g. bitcoin,ethereum') and clarifying defaults ('default usd') and constraints ('max 25'). This goes beyond the schema's descriptions.

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

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

The description clearly specifies 'Live spot prices for cryptocurrencies with 24h change.' It uses a specific verb ('query') and identifies the resource (cryptocurrency spot prices via CoinGecko IDs). It distinguishes from the sibling 'crypto-gas' tool by focusing on spot prices and including 24h change 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 explains how to query (by CoinGecko IDs and fiat vs currency) and provides examples and constraints (max 25 IDs). It does not explicitly state when not to use, but the specificity and lack of direct alternatives make the context clear.

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

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

No annotations are provided, but the description discloses behavioral traits like the cost ($0.008 USDC per call via x402) and that the client pays automatically. It also notes the output is a JSON verdict, adding value beyond the schema.

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

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

The description is a single well-structured sentence that front-loads the main purpose. It is concise and informative, with no wasted words. Slightly more structure could improve scannability.

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 one parameter and no output schema, the description fully explains the checks performed, the output format (JSON verdict), and pricing. It is complete for the tool's complexity.

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

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

There is only one parameter (domain) with full schema coverage. The description provides an example ('e.g. example.com') but does not add significant meaning beyond the schema's description. Baseline 3 is appropriate.

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

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

The description clearly states it performs a due-diligence check on a domain, listing specific checks like registration age, DNS, SSL, and risk flags. It uses a specific verb ('check') and resource ('domain'), and distinguishes itself from sibling tools like web-search or brand-visibility.

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

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

The description provides clear context on what the tool does and mentions it returns a JSON verdict. It does not explicitly state when to use vs. alternatives, but the purpose is distinct enough that an agent can infer appropriate usage.

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

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

With no annotations, the description carries the full burden. It discloses paid pricing ($0.004 USDC per call via x402) and output type (JSON verdict with risk level). However, it does not cover error handling, rate limits, or guarantees about deliverability accuracy.

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, front-loading the core functionality and adding pricing as a suffix. Every sentence adds value; no wasted words.

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

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

For a simple one-parameter tool with no output schema, the description covers purpose, features, output summary, and pricing. It lacks error handling details but is largely complete for agent selection.

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

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

Schema coverage is 100% for the single parameter 'email'. The description does not add meaning beyond the schema's 'Email address to validate' – baseline is appropriate.

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

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

The description clearly specifies the verb 'verify' and resource 'email', listing concrete checks (syntax, MX records, disposable domains, role-account, free-provider) and use cases (signup fraud prevention, list hygiene). It distinguishes from siblings by focusing on deliverability verification without sending 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?

The description mentions use cases but does not explicitly state when to use this tool versus siblings like domain-check or company-verify. No exclusions or alternatives are provided; usage context is implied by the listed features.

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 present, so description carries full burden. Discloses data source (ECB), update frequency (daily), output format (JSON), and pricing model. Lacks details on rate limits, error handling, or authorization needs, but sufficient for a simple read-only data 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?

Description is efficient with key information front-loaded, but the use of multiple short fragments could be structured more cohesively. 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 low complexity (2 optional params, no annotations, no output schema), the description covers source, update schedule, output type, and pricing. Minor gaps include error handling and return value structure, but overall 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 description coverage is 100%, and the description merely restates the parameters without adding new semantics. The baseline of 3 is appropriate as no additional value is provided beyond the schema.

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

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

The description clearly identifies the resource (foreign exchange rates from ECB), the action (reference rates), and the optional filters (base currency and symbols). It distinguishes from sibling tools like crypto-spot which deal with crypto rates.

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

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

Provides context about daily updates and pricing, but does not explicitly guide when to use this tool versus alternatives like crypto-spot or other finance tools. No exclusions or when-not-to-use guidance.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost ($0.01 USDC per call) and coverage (100+ countries), which are important behavioral traits. However, it does not mention rate limits, error handling, or authentication requirements, but the cost disclosure is significant.

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

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

The description is two sentences with no wasted words. The first sentence states the core purpose, and the second provides usage details, return format, and pricing, all front-loaded and efficient.

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

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

Given no output schema, the description sufficiently describes the key response fields (business day, weekend, holiday name, next business day). It also covers scope (100+ countries), pricing, and how to use optional parameters, making it complete for the tool's complexity.

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

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

The input schema already describes all 3 parameters (100% coverage). The description adds value by explaining how the optional date parameter enables business-day checking and what the response includes (holiday name, weekend/public holiday status, next business day), which is not in the schema.

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

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

The description clearly states the tool provides public holidays for any country and year, plus optional business-day checking. It uses a specific verb ('Pass') and resource ('public holidays'), and is distinct from sibling tools like 'local-search' or 'web-search'.

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

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

The description explicitly tells when and how to use the tool: Pass country= (ISO alpha-2) and year=; add date= for business-day check. It also notes the optional parameters and what the response contains, providing clear guidance without needing to mention alternatives.

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

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

No annotations are provided, so the description carries full burden. It discloses the tool uses expert LLM grounded in live official tariff schedule and returns confidence and reasoning. It also mentions payment cost. Although read-only behavior is not explicitly stated, the nature of classification implies it.

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 plus a pricing note, with all crucial information front-loaded. Every sentence adds value, 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?

The description explains all output fields (HTS code, HS6, description path, duty rates, confidence, reasoning, alternates) and format, compensating for the lack of output schema. It also covers source, pricing, and input requirements, making it complete for an AI 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?

Both parameters are described in the schema (100% coverage). The description adds value by explaining the default for 'dest' (US) and that duty rates shown are US MFN, providing semantic context 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 classifies products to HS/HTS customs codes from plain-English descriptions. It specifies the source (USITC HTS), output fields (10-digit HTS, HS6, description path, duty rates, confidence, reasoning, alternates), and format (JSON), making the purpose distinct from sibling tools.

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

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

The description implies usage when needing HS/HTS classification for a product. It does not explicitly state when not to use or list alternatives, but the context is clear for the intended use. The pricing note adds practical guidance.

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

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

With no annotations provided, the description carries full burden. It discloses that the call is paid ($0.02 per call), lists the data sources (Greenhouse, Lever, Ashby, SmartRecruiters for company, Remotive and Arbeitnow for query), and specifies the response includes title, location, department, type, and apply URL. It does not mention rate limits or authentication, but those are not critical for this type of tool.

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

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

The description is a single informative paragraph that front-loads the purpose, explains parameters, and ends with pricing. Every sentence is useful, though it could be slightly more concise by separating parameter details more clearly.

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

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

Given the tool has no output schema, the description adequately covers the response fields (title, location, department, type, apply URL). It explains the two modes, sources, and pricing. It does not mention pagination or error handling, but these are minor omissions for a simple search tool.

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

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

Schema coverage is 75% (query, company, location have descriptions; limit does not). The description adds value by explaining that company returns from official ATS boards and query searches remote-heavy APIs, and that location is a substring filter. However, it does not describe the limit parameter's effect (e.g., number of results). Overall, it adds some meaning beyond the schema but leaves a 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 tool provides live job postings without scraping, with two distinct modes (company and query) that are well-explained. It distinguishes itself from the 29 sibling tools by focusing specifically on job search.

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

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

The description explains when to use company mode (employer's official ATS) vs query mode (remote-heavy APIs) and notes the optional location filter. It also mentions pricing. However, it does not explicitly state when not to use the tool or compare to alternatives.

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

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

With no annotations, the description discloses data sources (CourtListener), pricing ($0.05 USDC via x402), and response format (JSON), but omits rate limits or authentication 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?

Two sentences with front-loaded information; the first packs key details, the second lists filters. Minor redundancy with schema indicating optional nature.

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 adequately specifies return content (case names, courts, etc.), geographic focus, and cost, providing a good overall picture.

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

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

Schema coverage is 100%, so baseline is 3. The description reiterates 'optional court and filed-after filters' and 'company or person name' but does not add significant new meaning beyond the schema descriptions.

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

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

The description clearly states the tool provides 'US litigation footprint of a company or person in one call' and enumerates the types of data returned (federal dockets, bankruptcy filings, court opinions), making the purpose specific and distinct from sibling tools.

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

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

The description implies usage for US litigation lookups with optional filters, but does not explicitly state when not to use it or mention alternative tools among the diverse sibling list.

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

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

Discloses data source (OpenStreetMap), automatic geocoding, response fields, and payment mechanism ($0.02 USDC via x402). No annotation present, so description carries full burden; it covers key aspects but omits rate limits or error handling.

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

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

Two sentences with zero waste: purpose, parameters, output, and cost all covered efficiently. Front-loaded with primary function.

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, but description lists return fields. Covers essential inputs and outputs, plus payment context. Could mention result limits or pagination, 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?

Schema covers all 5 parameters (100%), and the description adds value by specifying usage patterns (what+city or lat/lon+radius), listing example categories, and clarifying 'what' can be a name keyword.

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 finds local businesses by category and location, listing specific examples and output fields. It distinguishes itself from all sibling tools, none of which offer local 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?

Explains parameter combinations (what+city or lat/lon+radius) and that city is geocoded. Lacks explicit when-not-to-use guidance, but no direct alternatives exist 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?

With no annotations, the description carries the behavioral disclosure burden. It transparently states the tool aggregates live data, returns JSON, and costs $0.04 per call via x402 on Base. It does not mention rate limits or auth requirements, but the cost model is well-disclosed.

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

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

The description is a single paragraph that front-loads the core purpose. It includes necessary context (pricing, data sources) without excessive fluff. The pricing note, while important, slightly extends length, but overall it is well-structured.

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

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

The tool returns complex FDA data without an output schema. The description lists the data fields (GUDID, class, etc.) but does not specify the JSON structure or key names. This is adequate for a known API, but more detail would improve completeness for an AI 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 coverage is 100%, so the baseline is 3. The description restates that either name or UDI can be used, but adds little beyond the schema's own parameter descriptions. It does not provide formatting examples or additional 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 specifies the tool's purpose: 'FDA intelligence on any medical device by name or UDI'. It lists the exact data points returned (GUDID, device class, regulation, 510(k), PMA, recalls, MAUDE count), which distinguishes it from sibling tools like 'med-drug' for drugs.

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

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

The description provides context for usage: 'by name or UDI' and 'aggregated live from openFDA'. While it does not explicitly state when not to use, the sibling list implies the tool is for medical devices (not drugs). The pricing note adds practical guidance for cost-aware usage.

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 fully carries behavioral disclosure. It reveals the tool aggregates live data from openFDA, returns JSON, and costs money. It does not cover rate limits, authentication, or error handling for missing drugs, but the listed outputs (label facts, shortage, recalls, adverse events) give sufficient transparency for expected behavior.

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

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

The description is three sentences, front-loading the purpose and then listing outputs. It includes cost information which is relevant and necessary. It is efficient without being overly verbose, though could be slightly more concise by removing redundant phrasing like 'in one call'.

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

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

No output schema exists, so description must convey return format. It states 'JSON response' and enumerates key data fields (label facts, manufacturer, shortage, recalls, FAERS count). For a tool with two optional parameters and moderate complexity, this covers the primary outputs. Missing details like pagination or empty results, but it is largely complete.

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

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

Input schema coverage is 100% with both parameters ('name' and 'ndc') described. The description adds context that either brand/generic name or NDC can be used, but does not provide additional semantics beyond the schema. Baseline of 3 is appropriate as schema already defines the parameters adequately.

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

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

The description specifies the tool provides 'Complete FDA intelligence' on drugs by brand name, generic name, or NDC, listing specific data points (label facts, manufacturer, shortage, recalls, FAERS count). It clearly distinguishes from sibling tools like 'med-device' (medical devices) and 'recall-check' (recalls only) by offering a comprehensive drug information aggregation.

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

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

The description states the tool aggregates live openFDA data in one call and notes the cost ($0.04 per call). It implies use for comprehensive drug info but lacks explicit when-not-to-use guidance or comparisons to alternatives. The context is clear enough for an agent to select it over more specific 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 cost ($0.01 per call via x402), JSON response, deduplication, and broad news index. It adds transparency about payment but does not cover 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?

The description is a single, well-structured sentence front-loading the main purpose, with no wasted words. Cost info is appended naturally.

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 adequately lists return fields (titles, source, link, publish date) and mentions deduplication and configurable window. Could add response format details but sufficient for a simple search 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 covers 100% of parameters with descriptions. The tool description adds minimal information beyond schema (e.g., default days=7), earning the baseline score.

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

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

The description clearly states the tool returns recent news headlines for any query, with deduplicated titles, source, link, and publish date. It distinguishes from siblings like web-search by focusing specifically on news.

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 news queries and mentions configurable time window, but does not explicitly state when to use this tool over alternatives like web-search.

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 the pricing model and that the response is JSON. It doesn't explicitly confirm the tool is read-only or mention rate limits or auth, but the output description implies no side effects.

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

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

The description is a single compact paragraph with no wasted words. It front-loads the core purpose and includes essential details like pricing in a concise manner.

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?

It fully describes the output format (vulnerabilities, deprecation, license, latest version, repo health, verdict) and includes pricing info. Without an output schema, this level of detail is sufficient for an agent to understand what the tool returns.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description reiterates the ecosystem list and mentions optional version, adding slight clarity but largely overlapping with 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 starts with a clear question 'Is this dependency safe to use?' and enumerates all outputs: vulnerabilities, deprecation, license, latest version, repo health, and a verdict. It lists supported ecosystems, making the tool's purpose unmistakable.

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

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

It says when to use: 'Pass a package name... and optional version.' It also notes it's a paid call ($0.01 via x402) and that the client pays automatically. It doesn't explicitly state when not to use or suggest alternatives, but the context is clear enough.

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 behavior. It does: it mentions the paid call cost ($0.04 USDC via x402 on Base), optional country parameter, and that the response is JSON. It doesn't cover rate limits or error handling, but the pricing detail is critical for agent decision-making.

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), front-loaded with the core purpose, and every sentence provides necessary information without redundancy. No wasted words.

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

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

For a two-parameter tool with no output schema, the description covers purpose, parameter details, pricing, and the scope of response data. It lacks structured output details but the enumerated data points give sufficient 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?

Schema coverage is 100% with both parameters described. The description adds an example for mpn (LM358MX/NOPB) and clarifies 'country= for regional pricing,' but the schema already says 'ISO country for regional pricing, default US.' Thus, the description adds minimal value beyond the schema.

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

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

The description starts with a clear verb+resource: 'Everything about an electronic component from its manufacturer part number.' It enumerates specific data points (live stock, price breaks, lifecycle, datasheet, alternates) and distinguishes from sibling tools like brand-visibility or chem-compat, none of which 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?

The statement 'One call instead of checking each distributor' explicitly tells the agent when to use this tool. It does not mention when not to use it or alternatives, but the tool's unique coverage of multiple distributors implies its specific role.

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 payment, size limit, and output format (page count, full text as JSON). Missing details on authentication (assumes public), rate limits, or handling of corrupted/scanned PDFs.

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 plus a payment note; every sentence adds value. The purpose is front-loaded, and there is zero 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?

Given no output schema, the description usefully states the return values (page count, full text as JSON). It could mention output structure or limitations (e.g., no OCR for images), but for a simple one-param tool this is mostly sufficient.

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

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

Schema coverage is 100% (one parameter with description). The tool description repeats the 'max 10 MB' constraint but adds no new meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the verb ('Extract clean text') and the resource ('public PDF URL'), with specific examples (papers, filings, reports). No sibling tool duplicates this function, so it is well-distinguished.

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 includes the cost ($0.01 USDC) and file size limit (10 MB), which are usage guidelines. However, it does not mention when not to use this tool, such as for non-public PDFs or scanned documents, nor suggests alternatives like reader for other document types.

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 the burden. It explicitly states the tool returns a JSON response with many data fields, covers food, general products, and cosmetics, and mentions the paid nature. It does not discuss limitations or side effects, but the scope is well-defined.

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

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

The description is a single paragraph that front-loads the core purpose and enumerates data fields efficiently. It includes cost information as a bonus. Slightly dense but concise for the amount of 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 one required parameter and no output schema, the description provides a comprehensive list of returned fields, scope, format, and cost. It is sufficient for an agent to understand what the tool offers. Lacks details on error handling or pagination, but acceptable for this tool type.

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

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

Schema coverage is 100% with a single parameter described as 'EAN/UPC barcode, 8-14 digits'. The description reinforces this but adds no new constraints or format details. Baseline 3 since scheme covers the parameter adequately.

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 returns full product truth from an EAN/UPC barcode, listing many specific data fields. It distinguishes itself from sibling tools, which cover other domains like brand visibility or recalls.

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 it is for barcode lookups, but does not mention exclusions or when to consider sibling tools like recall-check.

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 discloses that the page is rendered with a real browser, stripped to main content, and returns JSON. It does not cover failure cases or rate limits, but provides significant insight.

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 that are front-loaded with the main purpose. Every word adds value; no redundancy. Efficient and clear.

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 simplicity of the tool (2 params, no output schema, no annotations), the description covers the core functionality, pricing, and a use case. It could mention potential limitations (e.g., authentication, large pages) but is generally complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema (e.g., 'JSON response' is implicit). The parameter descriptions in the schema already cover the semantics adequately.

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 action ('Turn... into clean readable markdown'), the resource ('any public web page'), and the output (title, byline). It distinguishes itself from sibling tools by mentioning handling of JavaScript-heavy pages and rendering with a real browser.

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 specifies the tool is 'Ideal for feeding pages to LLMs' and mentions the cost ($0.006 per call). While it doesn't explicitly list when not to use or provide alternatives, the use case is clear and 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?

Despite no annotations, description discloses tool behavior: queries multiple agencies, returns unified JSON with specific fields, and mentions payment mechanism and cost ($0.02 USDC via x402). No contradictions.

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

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

Single paragraph that front-loads purpose, then lists parameters and pricing. Dense but efficient, 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?

With no annotations, no output schema, and 4 optional parameters, the description sufficiently covers what the tool does, input options, output format, and cost. Could mention that no parameters is allowed (all optional) but implied.

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

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

Schema coverage is 100% with descriptions for all parameters. The description reinforces each parameter's purpose (e.g., 'Query by vin=, upc=, query= (product name), or brand=') and adds context on output format, going beyond 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?

Description clearly states tool 'checks product recalls and safety alerts across US federal agencies', names specific agencies (NHTSA, CPSC, FDA) and query types (VIN, UPC, product name, brand), distinguishing it from sibling tools like 'chem-safety' or 'med-drug'.

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 lists query parameters (vin=, upc=, query=, brand=) and mentions unified JSON output with hazard, remedy, date, links. However, it does not explicitly state when NOT to use this tool or alternative tools for similar queries.

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 discloses fuzzy matching, match score, entry details, daily refresh, and paid cost ($0.02 USDC). It does not cover rate limits or auth requirements, but gives substantial behavioral clarity for a read-like 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?

The description is a single, well-structured paragraph that conveys all essential information without redundancy. Each sentence adds value, and the main purpose is front-loaded.

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

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

Despite no output schema, the description details the response contents (match score, entry details, list freshness). Combined with parameter info and cost, it gives the agent a complete picture for invoking and interpreting results.

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

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

Schema coverage is 100%, so baseline is 3. The description adds context about fuzzy matching and the scope of names (person, company, vessel), enhancing 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 explicitly states the tool checks names against four major sanctions lists, with the verb 'check' and specific resource 'sanctions lists'. It clearly distinguishes from sibling tools by specifying the sanctions screening 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 indicates use for 'compliance and counterparty checks', providing clear context. It does not explicitly list alternatives or exclusions, but the purpose is distinct enough for appropriate 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?

With no annotations, the description reveals it uses a real browser, returns base64 PNG, and includes payment details. However, it omits error handling, timeouts, or rate 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?

Two sentences: first covers purpose and key features, second covers return format and payment. 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?

Without an output schema, the description covers return format (base64 PNG in JSON) and payment. Missing failure behavior, but otherwise sufficient for a straightforward screenshot tool.

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

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

Schema coverage is 100%, but the description adds clarity: 'view-port or full-page' for the 'full' parameter and a default width of 1280, enhancing schema meaning.

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

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

The description explicitly states the tool takes a 'screenshot' of 'any public web page' using a 'real Chrome browser', clearly distinguishing it from sibling tools like web-search or reader.

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 visual capture of public pages and mentions paid cost, but does not specify when to use versus alternatives like 'brand-visibility' or 'reader'.

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 the source (SEC), output format (JSON), specific metrics, historical depth, and cost ($0.03). It does not cover rate limits or error handling, but the core behaviors are well communicated.

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

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

The description is a single, efficient paragraph of 4 sentences. It front-loads the purpose and method, lists metrics concisely, and includes relevant cost info. 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?

Despite no output schema or annotations, the description provides sufficient context: data source, specific financial fields, historical range, and pricing. It covers all essential aspects for a single-parameter tool.

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

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

The schema covers 100% of parameters with a description. The description adds 'Pass ticker=' but does not add new meaning beyond the schema. Baseline 3 applies because schema coverage is high.

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 retrieves key financials for any US-listed company by ticker from SEC EDGAR XBRL filings, listing specific metrics. The purpose is unambiguous and differentiates from sibling tools like crypto or search tools.

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

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

The description implies usage by saying 'Pass ticker=', but does not explicitly state when to use this tool vs alternatives or provide exclusion criteria. Usage context is clear but lacks directiveness.

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 method (signature-based from live homepage), output format (JSON), and cost ($0.02 USDC per call). No annotations provided, so description carries full burden. Could mention rate limits or error handling.

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

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

The description is informative but slightly verbose with many examples. It front-loads purpose and adds value in each sentence. Could be trimmed without losing meaning, but not excessive.

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 omits details on the JSON response structure (e.g., keys, data format). For a tool with only one parameter and no annotations, this gap is notable but not critical.

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

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

The only parameter 'url' is described as 'URL or domain to fingerprint' in the schema, and the description adds that it accepts both forms, enhancing the schema's meaning. Since schema coverage is 100%, baseline is 3, but the extra context raises 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 the tool detects website technologies across many categories (CMS, JS frameworks, analytics, etc.), with specific examples. This distinctively identifies the tool's purpose 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?

The description instructs to pass 'url= or domain=' and mentions it's signature-based from the live homepage. However, it doesn't explicitly state when not to use this tool or compare with alternatives like web-search.

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 fully discloses behavior: returns JSON with specific fields and timeline links. It also reveals the paid pricing model ($0.02/call via x402). However, it does not mention rate limits or authentication requirements.

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

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

The description is concise: two sentences covering output details, use cases, and pricing. Information is front-loaded 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?

For a simple two-parameter tool, the description covers output format, use cases, and pricing. It could mention that the limit parameter controls max snapshots, but overall it 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?

Both parameters have schema descriptions (100% coverage). The description adds no new meaning to parameters but provides output context (snapshots, timeline). Baseline 3 is appropriate as the schema already documents parameters adequately.

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 retrieves archived history from the Internet Archive Wayback Machine, listing specific data points (first-seen, last-seen, snapshots, distinct versions, timeline of snapshot links). This distinguishes it from sibling tools like web-search (current pages) and screenshot (visual captures).

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 mentions use cases: domain due-diligence, brand history, and change tracking. While it does not name alternative tools, the context makes it clear this is for historical data, not current information.

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

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

With no annotations, the description discloses output format (JSON with answer and results), payment model, and that it has live web access. It does not mention rate limits or potential latency, but covers key 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 two sentences and conveys essential information efficiently. The inclusion of payment details is relevant but slightly extends beyond core purpose.

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

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

For a simple one-parameter tool with no output schema or annotations, the description provides sufficient context on input, output, and cost. It covers the main usage scenario adequately.

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 covers the single parameter with full description. The description adds minimal beyond 'pass a query', so it meets the baseline of 3 given 100% schema coverage.

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

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

The description clearly states it performs web search, returning a concise web-grounded answer and top results. It distinguishes from sibling tools like local-search and news-search by being a general web search.

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

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

The description indicates it's for general web queries and mentions no API key needed, but doesn't explicitly state when not to use or provide alternatives. The context of sibling tools implies when specialized search is better.

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 states the output is a JSON response and mentions a payment requirement ($0.01 per call via x402), which is useful. However, it does not disclose if the video must be public or have captions, nor does it describe the response structure beyond 'plain-text'. This leaves some behavioral ambiguity.

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

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

The description is highly concise: three short sentences (plus a parenthetical about cost) that immediately state the tool's purpose, input, ideal use, output format, and cost. No extraneous words or redundant information.

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

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

Given the tool has only one parameter with complete schema coverage and no output schema, the description provides adequate context: input format, output type (JSON), cost, and use case. It could be improved by mentioning that the video must be public and have captions, but overall it is sufficient for an agent to invoke the tool correctly.

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

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

Schema coverage is 100% with one parameter 'v' described as 'YouTube video id or full URL'. The description repeats this information ('Accepts a video id or URL') without adding new semantics, so it does not improve on the schema. Baseline 3 is appropriate.

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

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

Description clearly states the tool returns a full plain-text transcript of a YouTube video, de-duplicated and cleaned from captions. It specifies the resource (YouTube video) and the action (transcript retrieval), and distinguishes it from siblings which are unrelated (e.g., web-search, pdf-extract).

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 accepting a video id or URL, which is a clear input guideline. It also states 'Ideal for feeding video content to LLMs,' indicating a primary use case. No explicit exclusions or alternatives are needed given the unique nature of the tool among siblings.

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