Toolcall

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 that multiple AI engines are used (one web-grounded), the output is a JSON report, and there is a per-call cost of $0.25 USDC via x402. However, it does not cover potential errors, timeouts, rate limits, or what happens if the brand is not mentioned.

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

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

The description is two sentences that are front-loaded with the core action ('See what AI models say about a brand right now') and then list capabilities concisely. It is informative 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?

There is no output schema, so the description compensates by listing the report contents (mention, position, sentiment, descriptors, gaps, recommendations). Combined with the three described parameters and cost disclosure, it provides a fairly complete picture, though exact JSON structure is omitted.

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 schema already describes parameters. The description adds value by explaining that 'domain' improves accuracy and 'category' is used for best-of prompts, providing 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 it runs category and reputation prompts across multiple AI engines to assess brand visibility, including mention detection, competitor position, sentiment, descriptors, gaps, and recommendations. It specifies a concrete verb and resource, distinguishing it from sibling tools like web-search or news-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 implies usage for brand analysis via AI model outputs but provides no explicit guidance on when to use this tool versus alternatives (e.g., web-search or news-search). No exclusions or when-not-to-use scenarios are mentioned.

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

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

Discloses critical behavioral traits: data sources (PubChem GHS, CAMEO), pricing ($0.03 USDC via x402), and automatic client payment. No annotations exist, so the description carries full burden. Missing rate limits or error handling details, but still 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?

Three sentences: opens with the core question, specifies inputs/outputs, ends with pricing. No wasted words, information-dense and well-structured.

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

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

Covers all essential aspects for a chem-compatibility tool: purpose, inputs, outputs (verdict, reason, effects, confidence), data sources, and cost. No output schema exists, but the description explicitly lists return fields.

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 value by clarifying that inputs can be 'CAS numbers or names', reinforcing the schema. No enums or additional constraints 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?

The description opens with a clear, specific question: 'Can chemical A be safely mixed or stored with chemical B?' followed by inputs and outputs. It differentiates from sibling 'chem-safety' by focusing on compatibility rather than general safety.

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 states required inputs ('Pass two CAS numbers or names') and describes the JSON response structure. Lacks explicit when-not-to-use or alternatives, but the narrow scope makes usage straightforward.

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 data source (PubChem live), response format (JSON), and payment information ($0.03 USDC per call). However, with no annotations, it fails to clarify that at least one parameter is required despite the schema allowing both to be optional.

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

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

The description is front-loaded with the main purpose and lists contents concisely. It is slightly verbose with the full enumeration but remains clear and well-structured.

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

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

The description covers all major aspects of the tool: input parameters, output contents, data source, and pricing. Given no output schema, it provides sufficient context for an agent to judge suitability.

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

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

Schema coverage is 100%, and the description adds 'by CAS number or name' which reinforces the two optional parameters. No additional format constraints or examples are provided 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 a full safety profile for chemicals by CAS number or name, listing all included components (GHS classification, first-aid, etc.). It distinguishes itself from siblings like chem-compat by specifying the exact safety data provided.

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 when needing a chemical safety profile but does not explicitly state when to use this tool vs alternatives. No when-not-to-use guidance or comparison to siblings like chem-compat is 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?

Without annotations, the description carries full burden. It discloses data sources (VIES, GLEIF, Companies House), returned fields (status, jurisdiction, etc.), and payment method. However, it does not mention rate limits, auth requirements, or error handling, which prevents a perfect score.

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

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

The description is front-loaded with the core purpose, then details query options, then payment info. Every sentence adds value without redundancy. Despite length, it is well-structured and concise for the content delivered.

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 5 parameters, no required ones, and no output schema, the description provides enough context about inputs and expected outputs (JSON with fields). It lacks detail on error responses or pagination but covers the main use cases 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?

Schema description coverage is 100%, so baseline is 3. The description adds value by grouping parameters into query modes, providing examples (e.g., DE811569869), and indicating combinations (name+country). This exceeds the baseline.

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 verifies a business in one call, listing specific checks (EU VAT, LEI, Companies House). The verb 'verify' and resource 'business' are explicit, and it distinguishes from siblings which all handle different domains.

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

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

Provides clear usage guidelines by enumerating query parameters (vat=, lei=, name=, number=, country=) and their contexts. It lacks explicit when-not-to-use or alternatives, but the sibling list shows no direct competitors, making the guidance sufficient.

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

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

With no annotations, the description carries full burden. It discloses that data is fresh within 15 seconds, returns JSON, and costs $0.002 USDC per call via x402. This is good behavioral context, though further details like rate limits or failure modes are absent.

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

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

Two sentences plus a bracketed cost note convey purpose, freshness, output format, and payment. Every sentence is informative and front-loaded, with 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?

For a simple single-parameter tool with no output schema, the description covers purpose, freshness, output type (JSON), and cost. It is sufficiently complete, though an example or note on units (e.g., Gwei) could further aid.

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 enum parameter 'chain' already described. The description adds no additional semantic detail beyond confirming the chains (Base, Ethereum). Baseline of 3 is appropriate.

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

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

The description explicitly states the tool retrieves current gas price and latest block for Base or Ethereum mainnet. It uses specific verbs and resources, clearly differentiating from sibling tools which are mostly unrelated.

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 querying gas prices and blocks from public RPC with freshness guarantees, but provides no explicit guidance on when to avoid this tool or mention alternatives. Usage context is implied but not formally structured.

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 burden. Discloses data freshness, JSON response, and payment cost. Lacks explicit read-only indication but implies non-destructive behavior. Adequately transparent for a simple query 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?

Three sentences, no wasted words. Front-loaded with purpose, then usage details. 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?

Covers key aspects: purpose, parameters, freshness, payment, and response format (JSON). Missing return structure details, but acceptable for a simple price tool without 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 covers both parameters with descriptions (100% coverage). Description adds an example for 'ids' and clarifies 'vs' as a currency. Adds marginal value beyond schema, earning baseline of 3.

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

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

The description clearly states 'Live spot prices for cryptocurrencies with 24h change', specifying the verb (get/live) and resource (cryptocurrency prices). It distinguishes itself from siblings like 'crypto-gas' by focusing on spot prices.

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

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

Provides clear usage instructions: query by CoinGecko ids (comma-separated, max 25) and a fiat vs currency. Includes freshness (within 60 seconds) and pricing. Does not explicitly contrast with alternatives, 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?

Discloses cost and payment method ($0.008 USDC via x402) which is a key behavioral trait. No annotations exist, so description covers most relevant aspects, though rate limits or error handling not mentioned.

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

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

Concise: three sentences cover purpose, output format, and cost. No superfluous words. Front-loaded with key capabilities.

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

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

For a tool with one parameter and no annotations, description covers purpose, checks, output format, and cost. Missing details like error handling or response structure, but not critical for 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?

Single parameter 'domain' with schema description. Schema coverage is 100%, so description adds minimal extra value (example only). 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 it performs due-diligence checks on domains, listing specific checks (registration age, DNS, SPF/DMARC, SSL, risk flags). Differentiates from siblings, none of which cover domain analysis.

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 'One call, one JSON verdict' indicating simplicity. No explicit when-not or alternatives, but sibling tools are unrelated, so 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 are provided, so the description carries the full burden. It transparently states the tool performs checks without sending an email, returns a JSON verdict with bounce/deliverability risk level, and discloses the payment method (x402 on Base). This sufficiently covers behavioral aspects for a read-only verification 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 concise, with every sentence adding value. It front-loads the primary purpose, lists specific checks, states output format, provides use cases, and mentions cost. No redundant or irrelevant 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 one parameter and no output schema, the description is complete. It explains what the tool does, the types of checks, the output format (JSON verdict with risk level), appropriate use cases, and cost. No missing information for a tool of this complexity.

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

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

Schema coverage is 100% for the single parameter 'email' with a description of 'Email address to validate'. The overall description adds context about the verification checks but does not elaborate on input format or constraints beyond what the schema provides. Baseline score of 3 is appropriate as the schema already describes 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?

The description clearly states the tool's purpose: email verification and deliverability check without sending. It lists specific checks (syntax, MX records, disposable-domain detection, role-account, free-provider flags) and distinguishes itself from siblings (no other email validation tool). The verb 'validate' and resource 'email' are specific.

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 use cases: signup fraud prevention, list hygiene, lead qualification. It mentions the cost ($0.004 per call) which is a practical guideline. However, it does not explicitly state when not to use it or compare to alternatives, but the context among siblings makes it clear this is the only email validation tool.

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

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

With no annotations provided, the description effectively discloses the daily update schedule, JSON response format, and cost per call. This adds value beyond standard 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 two sentences long, each sentence adding critical information: function and key parameters, then cost and payment mechanism. 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 data retrieval tool with no output schema, the description covers the essential aspects: data source, update frequency, filtering, and cost. Minor omission of error handling is acceptable given its simplicity.

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

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

Schema description coverage is 100% and includes clear parameter descriptions. The tool description echoes the schema without adding new semantic meaning, so baseline score is appropriate.

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

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

The description clearly states the tool provides foreign exchange reference rates from the European Central Bank, and distinguishes itself from sibling tools like crypto-spot by specifying the data source and commercial nature.

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

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

The description explains what the tool does but does not provide explicit guidance on when to use it over alternative financial tools. Context signals show many siblings, making such guidance beneficial.

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

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

No annotations exist, so the description carries full burden. It discloses cost ($0.01 per call), coverage (100+ countries), response format (JSON), and behavior with the date parameter (returns business day info). However, it does not mention error handling or explicit read-only nature.

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

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

Two concise sentences plus a brief pricing note. Front-loaded with purpose, then usage. No redundancy or 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?

Despite no output schema, the description fully explains the return values (business day, weekend/public holiday, holiday name, next business day). Covers 100+ countries and JSON format. Complete for a simple lookup 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% with descriptions for all 3 parameters. The description adds context beyond schema: explains ISO alpha-2, default year, and what the date parameter returns (business day check, next business day). This enhances usability.

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 public holidays for any country and year, with optional business-day checking. It specifies what each parameter does and what the response includes, distinguishing it from sibling tools which are unrelated.

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

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

The description provides clear usage instructions: pass country and year, optionally add date for business-day check. It implicitly differentiates from siblings as the only holiday tool, but lacks explicit guidance on when not to use it or alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It details the return fields (HTS code, duty rates, confidence, reasoning) and mentions the payment mechanism ($0.04 USDC per call). It does not cover failure modes or prerequisites, but overall offers solid transparency.

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

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

The description is concise with two sentences: the first states the core purpose, and the second adds essential details about functionality, return values, and pricing. There is no waste, and it is well 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 lacking an output schema, the description lists all key return components (code, path, duty rates, confidence, reasoning, alternates). It also clarifies pricing and source. It could be more complete by addressing error handling or edge cases, but is largely sufficient.

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

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

Schema description coverage is 100%, so the schema already documents both parameters. The description does not add additional semantic information beyond what is in the schema, resulting in a baseline score of 3.

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

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

The description clearly states 'Classify any product to its HS / HTS customs code from a plain-English description', specifying the verb (classify) and resource (HS/HTS customs code). It distinguishes itself from sibling tools by focusing on customs classification, unlike other tools such as product-barcode or part-lookup.

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

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

The description provides clear context for when to use the tool (classifying products from plain English descriptions) and mentions the source (live USITC schedule). However, it lacks explicit guidance on when not to use it or alternatives for other classification needs.

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 correctly identifies sources (Greenhouse, Lever, Remotive, etc.) and output format (JSON). It does not detail failure modes, rate limits, data freshness, or behavior when no results are found.

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

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

The description is a single dense paragraph that efficiently conveys purpose, modes, and pricing. It could be slightly more structured with bullet points, but it is concise 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 no output schema, the description mentions JSON response but lacks details on response structure, pagination (limit), or error cases. It covers core functionality but is incomplete for advanced use without schema support.

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% (limit missing description). The description adds value by explaining the behavior of company, query, and location parameters with source context. The limit parameter's semantics are only in the schema (range), not in the description.

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

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

The description clearly states it fetches live job postings from official ATS boards and remote job APIs. It distinguishes between company-specific searches (company=) and keyword searches (query=), making the purpose unambiguous and distinct from siblings like web-search or news-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?

Guidance is provided on when to use company= vs query=, and the optional location filter is mentioned. It also includes pricing information. However, explicit when-not-to-use scenarios or alternatives among siblings are not 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 provided, the description fully carries the burden. It discloses the data source (Free Law Project), US focus, JSON response, and payment information. It does not mention rate limits, error handling, or behavior on no results, but the disclosure is thorough for a query 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 concise and well-structured, with key information front-loaded. Each sentence adds value: what the tool does, data types included, filters available, data source, geographic focus, output format, and pricing. No extraneous 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?

The description covers the main return elements (docket details, bankruptcy chapter, opinions with citations) and notes JSON output. It lacks an example of the response structure and does not address error handling or empty results, but given the tool's simplicity and the presence of output schema absence, it is sufficiently complete for an agent.

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

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

The input schema has 100% description coverage, giving a baseline of 3. The description adds value by explaining the filters as 'optional court and filed-after filters' and providing format examples (e.g., 'cand, nysd' for court, 'YYYY-MM-DD' for filedAfter), which aids agent understanding 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's purpose: providing a comprehensive US litigation footprint (federal dockets, bankruptcy, opinions) for a company or person. It distinguishes itself from sibling tools by specializing in legal data, with no other sibling offering legal case information.

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 legal research but does not explicitly state when to use this tool over alternatives or when not to use it. It mentions optional filters and US focus, but lacks explicit guidance on context or exclusions.

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

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

Given no annotations, the description covers key behaviors: source (OpenStreetMap), output fields, automatic geocoding, and pricing ($0.02 per call). It lacks info on rate limits or error handling, but the transparency is solid for a search 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 concise and well-structured. It leads with the main action, lists key features, and includes pricing without unnecessary detail. 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 tool with no output schema, the description adequately covers inputs, outputs, and pricing. It could mention result count limits or pagination, but overall it provides enough context for an agent to use the tool 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 description adds significant value beyond the 100% schema coverage. It explains usage patterns (e.g., 'Pass what= plus city='), provides default radius (5000m), and gives examples for the 'what' parameter (pharmacy, plumber). This greatly aids 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?

The description clearly states the tool's purpose: 'Find local businesses and places by category and location'. It lists specific examples (pharmacies, restaurants, etc.) and mentions over 40 categories. This distinctly separates it from sibling tools like brand-visibility or chem-compat.

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 the tool (location-based business search) and provides clear instructions on parameter combinations ('what= plus city= or lat=&lon= with a radius'). It does not explicitly state when not to use it, but the context is sufficiently 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. The description mentions payment cost ($0.02 USDC per call via x402) and that data is aggregated live from openFDA, but does not disclose other behavioral traits like authentication, 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 a single sentence with key information, but includes payment details which are not core to tool usage. It is efficient but could be structured 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?

No output schema exists, so the description should detail return format. It lists data types but not structure or field names, leaving some ambiguity. Edge cases or errors are not addressed.

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 schema already describes both parameters (udi and name). The description only reiterates 'by name or UDI', adding no new semantic detail 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 'FDA intelligence on any medical device by name or UDI', listing specific data types (GUDID identity, device class, regulation, clearances, approvals, recalls, adverse-event counts). It distinguishes from sibling tools like 'med-drug' and 'recall-check'.

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 medical device queries via name or UDI, but does not explicitly state when to use vs. alternatives like 'recall-check' or 'med-drug'. No direct when-not or alternative guidance is 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 exist, so the description carries the burden. It discloses that data is aggregated live from openFDA, returns JSON, and includes pricing ($0.02 per call via x402). It does not describe auth needs or potential side effects, but the paid model implies usage cost.

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

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

The description is concise, with the main purpose in the first sentence and details following. The pricing note is useful but adds length. 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?

The tool has no output schema, but the description lists all included data fields. Input parameters are covered. However, it does not explicitly state that at least one of 'name' or 'ndc' is required. Overall, it is mostly complete for a tool of this complexity.

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

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

Schema coverage is 100% with both parameters described. The description adds that the tool works by brand name, generic name, or NDC, matching the schema. However, it provides no additional detail beyond what the schema already conveys.

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 complete FDA intelligence for any drug by name or NDC, listing specific data points (label facts, manufacturer, shortage, recalls, adverse events). It is distinct from siblings like 'med-device' and 'recall-check'.

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 use the tool (by brand name, generic name, or NDC) but does not provide explicit when-to-use or when-not-to-use guidance relative to sibling tools, nor does it mention prerequisites or that at least one parameter is required.

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 compensates well by disclosing the paid nature ($0.01 per call via x402), response format (JSON), deduplication, and configurable window. However, it does not 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?

Two sentences plus a brief pricing note. No redundant information; all content is functional 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?

The description explains the response includes deduplicated titles, source, link, and publish date, which is sufficient for a search tool. No output schema exists, so this level of detail is adequate. Minor gap: no mention of pagination or result limits.

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

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

Schema coverage is 100%, and the description adds context for the days parameter ('Window configurable 1-30 days') but does not significantly enhance the meaning of the q parameter beyond its schema description. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves recent news headlines for any query, with deduplication, source, link, and publish date. This distinguishes it from siblings like web-search (broad web) and job-search (jobs).

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 a configurable time window and pricing, but does not explicitly specify when to use this tool versus alternatives like web-search, nor when not to use it. Implicit usage is clear, but no exclusions or alternatives are given.

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

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

With no annotations, the description carries the full burden. It discloses that the tool returns JSON, costs $0.01 USDC per call via x402 (paid automatically by client), and implies no side effects. This is sufficient transparency, though behavior on missing packages is not specified.

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: purpose first, then input format, output details, and pricing. Every piece of information is useful and front-loaded. No redundancy or filler.

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

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

Despite having no output schema, the description thoroughly lists the response contents (vulnerabilities with CVSS, fixed-in version, deprecation, license, latest version, repo health, verdict). For a 3-parameter tool, this is complete and leaves no major gaps for an agent to invoke correctly.

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

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

Schema coverage is 100%, so the schema already describes parameters. The description adds value by listing supported ecosystems (mirroring the enum) and clarifying that version is optional, but it doesn't go beyond what the schema provides. 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 tool's purpose (assess dependency safety) with specific verb 'check' and resource 'package'. It lists exactly what it returns (vulnerabilities, deprecation, license, etc.), and this unique function distinguishes it from all 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 tells the agent to pass a package name and optional version, and specifies supported ecosystems. While it doesn't explicitly state when not to use the tool, the distinct domain (package security) compared to siblings makes the usage context clear.

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

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

With no annotations, the description carries full burden. It discloses the tool is paid ($0.04 per call via x402 on Base), returns JSON, and provides live data from specific distributors. It does not mention error handling, rate limits, or authentication beyond payment. The disclosure of cost and payment method 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 three sentences long, front-loading the main purpose. It efficiently lists data items and includes payment info. Slightly dense but each sentence adds value. Could be slightly tighter, but 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?

No output schema exists, so description should inform of return content. It lists many fields (stock, pricing, lifecycle, datasheet, etc.) and mentions JSON format. For a 2-parameter tool with no annotations, this is sufficient. Missing structure details but 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 coverage is 100%, so baseline is 3. The description adds value by explaining that 'country' is for regional pricing and provides an example MPN ('LM358MX/NOPB'). This goes beyond the schema descriptions, earning a 4.

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 comprehensive data about electronic components from multiple distributors, including stock, pricing, lifecycle status, and alternates. It uses a specific verb ('lookup') and resource ('electronic component from MPN'), and distinguishes itself from all siblings which are unrelated (e.g., brand-visibility, chem-safety, 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 implies when to use: instead of checking multiple distributor sites individually. It mentions optional country parameter for regional pricing. However, it does not explicitly state when not to use or provide alternatives. The lack of exclusions lowers it from 5.

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

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

No annotations provided, so description carries full burden. Discloses return format (page count, full text JSON), cost ($0.005 USDC via x402 on Base), and client-automated payment. 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?

Two sentences with no waste. First sentence states action and constraints; second adds return info and cost. Front-loaded and efficient.

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

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

For a single-parameter tool with no output schema, the description covers return format and cost fully. Missing output schema is compensated. Could mention file format (PDF) but name implies it.

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 100% coverage for the single parameter 'url' (including max 10 MB). Description adds no new semantic detail beyond what schema provides, meeting the baseline for high 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?

Clearly states 'Extract clean text from any public PDF URL' – a specific verb-resource pair. Differentiates from siblings like 'reader' by focusing solely on PDFs and text extraction.

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

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

Provides clear context: public PDF, size limit (10 MB), and payment. While no explicit when-not or alternatives are given, the specialization makes usage self-evident.

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

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

The description discloses the cost ($0.02 USDC per call) and response format (JSON), but lacks details on error handling, rate limits, authentication requirements, or behavior when barcode is not found. With no annotations, more behavioral context would be helpful.

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

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

The description is a single, information-dense paragraph. It could benefit from bullet points for readability, but it is concise and front-loads the core purpose.

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

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

For a lookup tool with no output schema or annotations, the description fairly comprehensively lists the data returned and notes coverage areas and cost. Minor gaps in error behavior and completeness of output naming.

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 clear description of the 'ean' parameter. The description adds context on the return value but no additional constraints or formatting instructions for the parameter itself.

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 function: retrieve comprehensive product information from an EAN/UPC barcode, including name, brand, ingredients, nutrition, scores, packaging, and recalls. It distinguishes itself from siblings like 'recall-check' by offering a broader data set.

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 any barcode lookup, but does not explicitly state when to use this tool versus alternatives like 'brand-visibility' or 'recall-check'. No exclusions or alternative recommendations 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?

With no annotations, the description provides important behavioral details: uses a real browser, strips to main content, outputs JSON, and mentions a payment mechanism ($0.006 USDC via x402). This adds substantial 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 concise, front-loaded with the main action, and every sentence adds value. 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?

Given no output schema, the description explains the return format (clean markdown/text/html with title and byline, JSON response) and covers payment. It lacks error handling details but is reasonably complete 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 coverage is 100%, so parameters ('url', 'format') are well-described in the schema. The description only implicitly mentions formats ('markdown, text/html'), adding no new meaning beyond what the schema provides.

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

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

The description starts with a specific verb 'Turn any public web page' and specifies the resource (web pages) and output (clean readable markdown with title and byline). It clearly distinguishes from siblings like screenshot or pdf-extract by focusing on text extraction.

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 'Ideal for feeding pages to LLMs', implying a use case, but does not explicitly mention when not to use it or provide alternatives among 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?

Without annotations, the description discloses output format (unified JSON with hazard, remedy, date, links) and pricing ($0.01 USDC per call). It lacks explicit read-only or rate limit info but sufficiently 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?

Three sentences efficiently convey purpose, query methods, and pricing. The first sentence is dense but front-loaded. No redundant text; every part adds value.

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

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

Given the tool's complexity (multiple agencies, query types) and no output schema, the description covers key aspects: agencies, parameters, output format, and cost. It lacks pagination info but is otherwise 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 description adds context beyond the schema by explaining how parameters correspond to agencies (e.g., VIN for NHTSA, UPC for CPSC). It clarifies that 'query' is for product name and mentions 'auto-decoded' for VIN, though the 'make/model/year' reference is slightly ambiguous.

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

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

The description clearly states the tool checks product recalls and safety alerts across US federal agencies, listing specific agencies (NHTSA, CPSC, FDA) and recall types. It distinguishes from sibling tools like chem-safety or med-device which cover different domains.

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

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

The description provides clear context on when to use the tool (for recalls/safety alerts) and how to query (by VIN, UPC, brand, or keyword). It does not explicitly mention when not to use or alternatives, but the scope 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?

With no annotations, the description fully discloses behavior: fuzzy matching, match scores, entry details, daily refresh, cost, and lists used. 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?

The description is well-structured and front-loaded, but slightly verbose with pricing details. Still efficient and informative.

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

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

Despite no output schema, the description explains return fields and JSON format. It could mention error handling, but overall covers key output and usage 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 description coverage is 100%, so the baseline is 3. The description adds global context but no additional parameter-specific meaning beyond the schema.

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

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

The description clearly states the tool checks names against four major sanctions lists, with specific verb 'check' and resource 'sanctions lists'. It distinguishes from sibling tools which are unrelated.

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. It does not explicitly state when not to use or suggest alternatives, but the context is clear and siblings are unrelated.

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

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

No annotations provided, so description carries full burden. It discloses use of real Chrome browser, base64 PNG return, JSON response, and pricing model. Does not detail rate limits or error handling, but adequate for a screenshot 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?

Two sentences plus a pricing note in brackets. Every sentence adds value, front-loaded with core 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?

Given 3 simple parameters, no output schema, and no annotations, description covers the main behavior, options, and pricing. Lacks details on return format structure, but sufficient for a straightforward 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%, and description adds minimal semantics beyond schema: 'configurable width' matches schema detail. Does not introduce new parameter information, so baseline 3 is appropriate.

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

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

The description clearly states 'Full-fidelity screenshot of any public web page' with specific verb and resource. It distinguishes from sibling tools (none offer screenshots) and adds detail on viewport/full-page and configurable width.

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 usage for public web pages only and mentions payment cost, but lacks explicit alternatives or when-not-to-use guidance. However, 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?

With no annotations, the description discloses data source (SEC EDGAR XBRL), official nature (10-K figures), historical depth (up to 5 years), and pricing ($0.03 USDC). It does not cover error behavior 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?

The description is a single sentence that front-loads the main purpose, but it is somewhat lengthy and contains slight repetition (e.g., two mentions of SEC filings). Still, it is clear 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 simple signature (1 param, no output schema), the description covers what the tool returns, the data source, historical depth, and pricing, leaving little ambiguity for basic usage.

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

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

The schema already provides a description for 'ticker', and the description does not add new meaning beyond repeating usage instructions with an example. Schema coverage is 100%, so baseline 3 is appropriate.

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

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

The description clearly states it provides key financials for US-listed companies by ticker from SEC EDGAR XBRL filings, listing specific metrics and historical range. This distinguishes it from sibling tools like 'company-verify' or '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?

The description instructs to 'Pass ticker=' and specifies US-listed companies, but it lacks explicit when-to-use or when-not-to-use guidance compared 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 carries the full burden. It discloses the signature-based detection method, output format (JSON), pricing ($0.02 USDC per call), and specific info returned (server headers, security posture). It does not mention permissions or rate limits, but overall provides good transparency for a read-only tool.

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

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

The description is relatively long but every sentence adds value, listing categories and examples. It front-loads the main purpose and is 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 the complexity of detecting multiple technologies, the description covers what the tool does, what it returns (JSON with server headers, etc.), and even includes pricing context. Without an output schema, it adequately informs the agent of the expected response.

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

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

Schema coverage is 100% with one parameter 'url' described as 'URL or domain to fingerprint'. The description adds value by clarifying that both 'url=' and 'domain=' forms are acceptable, improving usability beyond the schema's terse description.

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

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

The description explicitly lists what the tool detects (CMS, JS framework, analytics, etc.) and clearly distinguishes it from sibling tools like 'domain-check' or 'brand-visibility' by focusing on technology fingerprinting from a live homepage.

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 use the tool ('Pass url= or domain=') but does not provide explicit guidance on when to choose this tool over alternatives or when not to use it. Usage context is implied by the tool's 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?

Discloses payment model ($0.02 USDC per call via x402 on Base) and response format (JSON). No annotations provided, but description covers cost and automatic payment, which is critical behavioral info.

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, no fluff. Includes essential info (what, use cases, cost). Could be slightly tighter but effective.

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

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

Given no output schema and two simple params, the description is fairly complete: covers purpose, return fields, use cases, and payment. Missing explicit return structure details, but fields are listed.

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 already describes both parameters fully (coverage 100%). Description adds no new meaning beyond what schema provides; it just restates 'URL or domain' and 'Max snapshots'.

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 retrieves archived history from Internet Archive Wayback Machine with specific fields (first-seen, last-seen, total snapshots, distinct versions, timeline). Distinguished from siblings like brand-visibility, web-search, etc.

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

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

Provides clear usage contexts: domain due-diligence, brand history, change tracking. While it does not explicitly state when not to use or alternatives, the guidance is adequate 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?

With no annotations provided, the description carries full disclosure burden. It clearly states the tool is a live web search requiring no API key, returns a JSON response, and involves a cost ($0.008 USDC per call). It does not mention potential rate limits or caching behavior, but for a simple read-only search, the transparency 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 extremely concise, consisting of a single sentence that front-loads the core purpose and follows with key details (pricing, no auth, output format). Every word adds value; no wasted 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 the tool's simplicity (one parameter, no output schema), the description covers all essential aspects: what it does (web search), input (query), output (answer and top results with snippet), authentication (keyless), and pricing. It is self-contained and sufficient for an agent to invoke correctly.

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

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

The input schema already fully describes the single parameter 'q' with a 1-300 character constraint. The description merely restates 'pass a query', adding no new semantic information beyond the schema. With 100% schema coverage, the baseline of 3 is appropriate.

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

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

The description clearly states the tool performs a web search ('Keyless web search for agents'), specifies the action ('pass a query, get a concise web-grounded answer plus the top source results'), and highlights its unique keyless nature. However, it does not explicitly differentiate from sibling tools like news-search or local-search, leaving some ambiguity for the agent about when to prefer this tool over specialized alternatives.

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

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

The description implies usage for general web queries by stating 'pass a query, get a concise web-grounded answer'. It provides no explicit guidance on when not to use the tool or suggests alternative sibling tools for specialized contexts, which would be helpful given the large 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?

With no annotations, the description partially carries the burden: it discloses deduplication, cleaning, JSON response, and payment cost ($0.01 USDC per call). However, it omits error handling, rate limits, or what happens if captions are unavailable.

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 main sentences plus a brief payment note. It is front-loaded with the core purpose and avoids redundancy, though the payment info could be integrated more smoothly.

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 covers purpose, input format, output format (JSON), and cost. It lacks error scenarios but is sufficient for typical 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 description coverage is 100%, and the description merely repeats the schema's parameter note ('Accepts a video id or URL'), adding no additional semantic value.

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

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

The description clearly specifies the tool's function: extracting a full plain-text transcript from YouTube videos, with details on deduplication and cleaning. It uniquely distinguishes itself from sibling tools like 'pdf-extract' or 'reader' by targeting video 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 suggests use for feeding video content to LLMs, providing a clear context. However, it does not explicitly state when not to use this tool or mention alternatives among siblings.

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