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

The description adds valuable behavioral context beyond annotations: it discloses the $0.25 USDC cost per request and payment method (via x402 on Base). Annotations already cover read-only, open-world, idempotent, and non-destructive aspects, so the description appropriately focuses on monetization details not captured in structured fields.

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 perfectly concise with two sentences: the first states the core functionality, the second provides critical cost information. Every word earns its place, and the structure is front-loaded with the main 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 single-parameter tool with rich annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) and no output schema, the description provides good context about what the tool returns (investment analysis report with specific components) and critical cost information. It could mention report format or length for full completeness.

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

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

With 100% schema description coverage, the schema already fully documents the single 'symbol' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, but this is acceptable given the comprehensive 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's purpose: 'Get a Claude AI-written investment analysis report for any cryptocurrency' with specific components ('price action, fundamentals, and outlook'). It distinguishes from siblings like crypto_get_price (price only) and crypto_deep_analysis (different analysis type).

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 about what the tool provides (AI-written investment analysis report), but doesn't explicitly state when to use it versus alternatives like crypto_deep_analysis or finance_ai_report. The cost disclosure helps with usage decisions but doesn't provide comparative guidance.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable context: the monetary cost per request and the payment method ('via x402 on Base'), which isn't captured in annotations. However, it doesn't disclose other behavioral traits like rate limits, response format, or error handling. No contradiction with annotations exists.

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

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

The description is concise and front-loaded: the first sentence states the core functionality, and the second adds critical cost information. Both sentences earn their place by providing essential details without redundancy. It could be slightly improved by integrating usage hints, but it's efficiently structured with zero 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?

Given the tool's complexity (deep market analysis), annotations cover safety and idempotency, and the schema fully describes the single parameter. However, there's no output schema, and the description doesn't explain return values (e.g., analysis format, data structure). The cost disclosure is helpful, but for a tool with no output schema, more context on expected results would enhance completeness. It's adequate but has clear gaps.

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

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

The input schema has 100% description coverage, with the 'symbol' parameter well-documented ('Crypto symbol (e.g., BTC, ETH, SOL)'). The description doesn't add any meaning beyond this—it doesn't explain parameter usage, constraints, or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Get full market analysis including on-chain metrics, whale movements, sentiment breakdown, and risk assessment.' It specifies the verb ('Get') and resource ('full market analysis') with concrete components. However, it doesn't explicitly differentiate from sibling tools like 'crypto_ai_report' or 'crypto_get_signal', which might offer similar analyses.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions a cost ('Costs $0.10 USDC per request via x402 on Base'), which is useful for budgeting, but doesn't specify scenarios where this deep analysis is preferred over simpler tools like 'crypto_get_price' or other crypto/finance siblings. No exclusions or prerequisites are stated.

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

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

The description adds valuable behavioral context beyond annotations: it discloses the cost per request ($0.01 USDC via x402 on Base) and the scope of supported symbols (53 symbols listed). Annotations already indicate it's read-only, non-destructive, idempotent, and open-world, but the description enhances this with practical details like pricing and symbol support, without contradicting annotations.

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

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

The description is front-loaded with key information (price data details, cost, symbol support) in three concise sentences. Each sentence earns its place by providing essential context without redundancy, making it efficient and well-structured for quick understanding.

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

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

Given the tool's moderate complexity (single parameter, no output schema) and rich annotations, the description is mostly complete. It covers purpose, cost, and symbol scope, but lacks details on response format (e.g., structure of returned price data) and error handling, which could be useful for an agent invoking 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 input schema has 100% description coverage, with the 'symbol' parameter well-documented in the schema itself (e.g., type, length, examples). The description adds minimal semantics by listing example symbols (BTC, ETH, SOL, PEPE) and noting support for 53 symbols, but this mostly reinforces what the schema already provides, aligning with the baseline score for high schema coverage.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('real-time cryptocurrency price data'), listing detailed data points like 24h change, volume, market cap, and 52-week range. It distinguishes itself from sibling tools (e.g., 'crypto_ai_report', 'finance_stock_price') by focusing on crypto price data rather than analysis, reports, or other asset types.

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

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

The description provides clear context for usage by specifying the supported symbols (e.g., BTC, ETH) and cost ($0.01 USDC per request), which helps determine when to use it. However, it does not explicitly state when not to use it or name alternatives among sibling tools (e.g., 'crypto_deep_analysis' for more detailed insights), leaving some guidance gaps.

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

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations by disclosing the cost ($0.05 USDC per request via x402 on Base), which is critical for usage decisions. However, it does not mention rate limits, authentication needs, or response format details.

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

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

The description is front-loaded with the core purpose in the first sentence and adds cost information in the second. Both sentences earn their place by providing essential operational and financial context without redundancy or fluff, making it highly efficient.

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

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

Given the tool's complexity (financial analysis with cost implications), annotations cover safety aspects, but there is no output schema. The description compensates by detailing the returned data (signal, confidence, indicators, sentiment) and cost, though it lacks information on response structure or error handling. For a tool with no output schema, this is reasonably complete but not exhaustive.

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

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

Schema description coverage is 100%, with the parameter 'symbol' fully documented in the schema. The description does not add any parameter-specific semantics beyond what the schema provides, such as examples of valid symbols or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the specific action ('Get enriched trading signal') and resource (trading signal data), distinguishing it from siblings like crypto_get_price (price only) or crypto_ai_report (broader analysis). It specifies the signal type (BUY/HOLD/SELL) and additional data (confidence score, momentum indicators, sentiment data), making the purpose explicit and differentiated.

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

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

The description implies usage for obtaining trading signals for cryptocurrencies, but does not explicitly state when to use this tool versus alternatives like crypto_deep_analysis or crypto_ai_report. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool 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?

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it discloses the cost ($0.25 USDC per request) and payment method (via x402 on Base), which are critical behavioral traits not captured in annotations. No contradiction with annotations.

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

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

The description is highly concise and front-loaded: the first sentence covers the core purpose and scope, and the second sentence adds essential cost information. Every sentence earns its place with no wasted words, making it efficient for an AI agent to parse.

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

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

Given the tool's moderate complexity (single parameter, no output schema), the description is mostly complete. It covers purpose, scope, and cost, but lacks details on output format or error handling. With annotations providing safety and idempotency, and schema covering parameters, the description is sufficient but could be slightly enhanced with output expectations.

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

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

Schema description coverage is 100%, with the parameter 'symbol' well-documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without extra value.

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

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

The description clearly states the tool's purpose with specific verbs ('Get AI-powered stock analysis') and resources ('stock'), and distinguishes it from siblings like finance_stock_price or finance_stock_financials by specifying it provides AI-generated analysis covering price action, fundamentals, competitive position, and investment outlook.

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

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

The description implies usage context (when you need AI-powered stock analysis) but does not explicitly state when not to use it or name alternatives. It distinguishes from siblings by content type but lacks explicit guidance on choosing between this and other finance tools like finance_stock_financials or finance_stock_profile.

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

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

Annotations already cover key traits (read-only, non-destructive, idempotent, open-world), but the description adds valuable context beyond this: it discloses the cost per request ('Costs $0.02 USDC per request via x402 on Base'), which is not captured in annotations. No contradiction with annotations.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by cost details in the second. Both sentences earn their place by providing essential information without redundancy or 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 the tool's moderate complexity (3 parameters, no output schema), annotations cover safety and idempotency, and the description adds cost information. However, it lacks details on rate limits, error handling, or return format, leaving minor gaps.

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

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

Schema description coverage is 100%, with clear parameter descriptions in the schema. The description does not add meaning beyond the schema (e.g., it doesn't explain parameter interactions or provide examples), so it meets the baseline of 3.

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

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

The description clearly states the specific action ('Convert an amount from one currency to another') and resource ('using live forex rates'), distinguishing it from sibling tools like finance_forex_rate (which likely provides rates without conversion) and finance_stock_price (which deals with stocks, not forex).

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

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

The description implies usage context (currency conversion with live rates) but does not explicitly state when to use this tool versus alternatives like finance_forex_rate or other finance tools. No exclusions or prerequisites 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?

The description adds valuable behavioral context beyond annotations: it discloses the cost ('Costs $0.01 USDC per request via x402 on Base') and the 5-day history feature. Annotations already cover read-only, open-world, idempotent, and non-destructive traits, so the description effectively supplements with operational details without contradiction.

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 and well-structured, with three sentences that each serve a clear purpose: stating the tool's function, disclosing cost and method, and explaining parameter format. There is no wasted text, and information is front-loaded effectively.

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

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

Given the tool's moderate complexity (single parameter, no output schema), the description is mostly complete. It covers purpose, cost, and parameter format, but lacks details on output (e.g., what data is returned in the 5-day history) and error handling, which could be useful for an agent despite the absence of an output schema.

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

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

The description adds some parameter semantics by explaining the format ('FROM-TO') and providing examples ('e.g., USD-EUR, GBP-JPY, EUR-INR'), which clarifies usage. However, the input schema already has 100% description coverage for the 'pair' parameter, so the description does not significantly enhance beyond what the schema provides, aligning with the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose: 'Get foreign exchange rate between two currencies with 5-day history.' It specifies the verb ('Get'), resource ('foreign exchange rate'), and scope ('5-day history'), and distinguishes it from sibling tools like 'finance_forex_convert' by focusing on rates rather than conversion.

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 usage by mentioning the 5-day history and cost per request, which helps determine when to use it. However, it does not explicitly state when to use this tool versus alternatives like 'finance_forex_convert' or other finance tools, nor does it provide exclusions or prerequisites.

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

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it specifies the exact assets covered (e.g., major indices, commodities, yields) and discloses the cost ($0.05 USDC per request via x402 on Base), which are critical for usage decisions. No contradiction with annotations exists.

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 and front-loaded: the first sentence defines the scope (global market overview with specific examples), and the second sentence adds essential operational details (cost and payment method). Every sentence earns its place with no wasted words or redundancy.

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

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

Given the tool's simplicity (0 parameters, rich annotations covering safety and behavior), the description is reasonably complete. It specifies the assets returned and cost, which are crucial for this type of data-fetching tool. However, without an output schema, it could benefit from mentioning the format or structure of the returned data (e.g., JSON with fields), leaving a minor gap.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, maintaining focus on the tool's purpose and cost. A baseline of 4 is applied for zero-parameter tools, as the description compensates by providing other relevant information.

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 a 'global market overview' and lists specific financial instruments (S&P 500, Dow Jones, NASDAQ, FTSE 100, Nikkei 225, DAX, Gold, Silver, Oil, Treasury yields). It distinguishes from siblings like finance_stock_price or finance_forex_rate by covering multiple asset classes. However, it doesn't explicitly contrast with finance_ai_report or finance_stock_financials, which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this overview is preferable to individual price tools (e.g., finance_stock_price for single stocks) or more specialized tools (e.g., finance_stock_financials for detailed analysis). The cost information is operational but doesn't inform selection 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable behavioral context by disclosing the monetary cost per request ('Costs $0.10 USDC per request via x402 on Base'), which is critical operational information not captured in annotations. It doesn't contradict annotations and provides meaningful additional behavioral 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?

The description is perfectly concise with two sentences that each earn their place: the first states the purpose and scope, the second discloses critical cost information. It's front-loaded with the core functionality and wastes no words. The structure efficiently communicates essential information without redundancy.

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

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

Given the tool's moderate complexity (financial data retrieval), rich annotations covering safety and idempotency, and 100% schema coverage, the description is mostly complete. It adds crucial cost information that annotations don't capture. The main gap is the lack of output schema, but the description compensates by specifying what data types are returned (income statement, balance sheet, cash flow). It could be more complete by mentioning data freshness or format, but it's substantially adequate.

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

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

The input schema has 100% description coverage with a clear parameter description for 'symbol'. The tool description doesn't add any parameter-specific information beyond what the schema already provides. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description, which applies here.

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

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

The description clearly states the specific action ('Get financial statements') and resources ('income statement, balance sheet, and cash flow data'), distinguishing it from sibling tools like finance_stock_price (price only) or finance_stock_profile (company profile). It provides a precise verb+resource combination that leaves no ambiguity about what data is retrieved.

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

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

The description implies usage context through the cost disclosure ('Costs $0.10 USDC per request'), suggesting this should be used when financial statement data is needed and the cost is acceptable. However, it doesn't explicitly state when to use this tool versus alternatives like finance_stock_profile or finance_ai_report, nor does it provide exclusion criteria or prerequisites beyond the cost consideration.

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

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

Annotations already cover read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it discloses the cost ('Costs $0.05 USDC per request via x402 on Base') and specifies the exact number of articles returned ('latest 10'), which are not captured in annotations. No contradiction with annotations exists.

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 and front-loaded, consisting of only two sentences that efficiently convey purpose, output details, and cost. Every word serves a clear purpose, with no redundant or unnecessary information, making it easy for an agent to parse quickly.

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

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

Given the tool's low complexity (1 parameter, no output schema) and rich annotations, the description is mostly complete. It covers purpose, output format, and cost, but lacks details on error handling or response structure. However, for a simple read-only tool with good annotations, this is sufficient for effective agent use.

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

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

Schema description coverage is 100%, with the parameter 'symbol' fully documented in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

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

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

The description clearly states the specific action ('Get latest 10 news articles') and resource ('for any stock'), distinguishing it from sibling tools like finance_stock_price or finance_stock_financials. It specifies the exact output format ('including title, publisher, link, and thumbnail'), making the purpose unambiguous.

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

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

The description implies usage context by specifying 'latest 10 news articles' and mentioning a cost, but it does not explicitly state when to use this tool versus alternatives like finance_ai_report or finance_market_overview. No exclusions or prerequisites are provided, leaving the agent to infer appropriate scenarios.

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

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. The description adds valuable context beyond annotations: it discloses the cost ('$0.02 USDC per request via x402 on Base'), which is critical for usage decisions. It also specifies supported asset types (US stocks, ETFs, indices), but 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 highly concise and front-loaded: the first sentence states the core functionality, the second adds critical cost information, and the third clarifies scope. Every sentence earns its place with no wasted words, and the structure efficiently conveys key details.

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

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

Given the tool's moderate complexity (single parameter, no output schema), the description is mostly complete. It covers purpose, cost, and supported assets, which are essential for an AI agent. However, it lacks details on response format (e.g., what fields are returned) and error handling, which would be helpful despite the absence of an output schema.

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

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

The input schema has 100% description coverage, with the 'symbol' parameter well-documented (ticker examples provided). The description does not add any parameter-specific information beyond what the schema already states. According to guidelines, with high schema coverage (>80%), the baseline score is 3 when no additional param info is provided 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 the tool's purpose with specific details: 'Get real-time stock price' (verb+resource) and lists additional data points (day high/low, volume, 52-week range, market state). It distinguishes from siblings like finance_stock_financials (financial data) and finance_stock_profile (company profile) by focusing on price and market metrics.

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 this tool: for real-time stock price data with specific market metrics. It mentions support for 'all major US stocks, ETFs, and indices,' which helps scope usage. However, it does not explicitly state when NOT to use it or name specific alternatives among siblings (e.g., finance_stock_financials for financial statements).

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

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: the specific cost ($0.03 USDC per request) and payment mechanism (via x402 on Base). This financial transparency is crucial for usage decisions and isn't covered by annotations. No contradiction with annotations exists.

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

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

The description is extremely concise with two sentences: one listing the data returned and one stating the cost. Every word earns its place with no redundancy. The structure is front-loaded with the core functionality followed by important operational details. No wasted words or unnecessary elaboration.

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 rich annotations (read-only, open-world, idempotent, non-destructive) and a simple single-parameter schema with 100% coverage, the description provides good contextual completeness. It specifies what data is returned and includes critical cost information. The main gap is lack of output format details (no output schema), but for a profile retrieval tool with good annotations, this is acceptable.

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

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

Schema description coverage is 100% with the parameter 'symbol' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., no additional constraints or examples). With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

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

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

The description clearly states the tool retrieves a company profile with specific data fields (sector, industry, employees, market cap, P/E ratio, business description). It distinguishes from sibling tools like finance_stock_price (price only) and finance_stock_financials (financial statements), though it doesn't explicitly name these alternatives. The purpose is specific but could be more explicit about differentiation.

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 obtaining comprehensive company data, but provides no explicit guidance on when to use this versus alternatives like finance_stock_financials or finance_stock_news. The cost information suggests when cost considerations matter, but doesn't define specific use cases or exclusions. Usage is implied rather than explicitly stated.

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

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

Annotations cover read-only, open-world, idempotent, and non-destructive hints, so the agent knows this is a safe, repeatable query. The description adds valuable context about cost ('Costs $0.15 USDC per request via x402 on Base'), which isn't captured in annotations. However, it doesn't disclose other behavioral traits like rate limits, response format, or error handling. No contradiction with annotations exists.

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 and front-loaded: two sentences that directly state the tool's function and cost. Every word earns its place, with no redundant information or fluff. It efficiently communicates key details without unnecessary elaboration.

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

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

Given the tool's moderate complexity (AI-powered summarization with a cost), annotations provide good behavioral hints, and the input schema is fully described. However, there's no output schema, and the description doesn't explain return values (e.g., summary format or length). For a paid tool, more context on output or error cases would be helpful, but annotations cover safety aspects 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 input schema has 100% description coverage, with the 'url' parameter fully documented. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., URL format constraints or examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to given the schema's completeness.

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: 'Extract content from any URL and get a Claude AI-powered summary.' It specifies the verb ('extract content' and 'get a summary'), resource ('any URL'), and method ('Claude AI-powered'). However, it doesn't explicitly differentiate from siblings like web_extract_text or web_extract_structured, which may also extract content from URLs but without AI summarization.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions a cost ('$0.15 USDC per request'), which might imply usage for paid summarization, but doesn't specify scenarios where this tool is preferred over free or other extraction tools like web_extract_text. No explicit when/when-not or alternative tool references are included.

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

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

Annotations already indicate this is a safe, read-only, idempotent operation (readOnlyHint: true, destructiveHint: false, idempotentHint: true). The description adds valuable behavioral context beyond annotations by disclosing the cost ('$0.08 USDC per request via x402 on Base'), which is critical for usage decisions. It doesn't describe rate limits or output format, but with annotations covering core traits, this is sufficient.

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

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

The description is extremely concise—two sentences that efficiently convey key information: the batch operation and cost. Every word earns its place, with no redundancy or fluff, and it's front-loaded with the core functionality.

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

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

Given the tool's moderate complexity (batch extraction with cost), annotations cover safety and idempotency, and the schema fully documents parameters. The description adds crucial cost information, which isn't in structured fields. However, without an output schema, it doesn't describe return values (e.g., text format, error handling), leaving a minor gap for a tool with financial implications.

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, with the 'urls' parameter fully documented (array of URIs, min 1, max 5). The description reinforces the batch limit ('up to 5 URLs') but doesn't add significant meaning beyond what the schema provides, such as URL format requirements or extraction behavior. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the action ('Extract text') and resource ('from up to 5 URLs'), making the purpose specific. It distinguishes from siblings like web_extract_metadata or web_extract_structured by focusing on text extraction, but doesn't explicitly differentiate from web_extract_text (which appears to be a single-URL version).

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

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

The description implies usage context through the batch limit ('up to 5 URLs') and cost information, suggesting this is for batch processing. However, it doesn't provide explicit guidance on when to use this versus alternatives like web_extract_text (likely for single URLs) or other extraction tools, nor does it mention prerequisites or exclusions.

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

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond this by disclosing the cost ($0.08 USDC per request via x402 on Base), which is critical for usage decisions. It doesn't contradict annotations, as 'compare' aligns with read-only, and no other behavioral traits like rate limits or auth needs are mentioned.

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

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

The description is appropriately sized with two sentences: the first states the core functionality, and the second adds critical cost information. It is front-loaded with the main purpose and has zero wasted words, making it highly efficient and easy to parse.

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

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

Given the tool's moderate complexity (AI analysis of two pages), rich annotations (covering safety and behavior), and no output schema, the description is mostly complete. It covers purpose and cost, but could benefit from mentioning output format or analysis scope. However, with annotations providing behavioral context, it is largely adequate for agent use.

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

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

Schema description coverage is 100%, with both parameters (url1 and url2) well-documented in the schema. The description does not add any meaning beyond the schema, such as format details or constraints, so it meets the baseline of 3 for high schema coverage without extra param info.

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

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

The description clearly states the tool's purpose with specific verbs ('compare content of two web pages') and resources ('web pages'), and distinguishes it from siblings like web_extract_ai_summary or web_extract_text by specifying AI analysis of similarities and differences. It goes beyond a tautology of the name/title.

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 comparing web pages with AI analysis, but provides no explicit guidance on when to use this tool versus alternatives like web_extract_structured or web_extract_batch. It mentions a cost, which hints at resource considerations, but lacks clear when/when-not directives or named alternatives.

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

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

Annotations already provide safety information (readOnlyHint, non-destructive), but the description adds valuable behavioral context not covered by annotations: the monetary cost per request ($0.03 USDC via x402 on Base). This is critical operational information that helps the agent understand the tool's usage implications.

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

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

The description is extremely concise with just two sentences that each serve distinct purposes: the first explains what the tool does, the second explains the cost. There's zero wasted language, and the most important information (the extraction capability) 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 single-parameter tool with comprehensive annotations and clear purpose, the description provides adequate context. The cost disclosure is particularly valuable. The main gap is the lack of output format information (no output schema exists), but given the tool's relative simplicity and good annotations, this is a minor limitation.

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 with a clear parameter description for 'url'. The description doesn't add any additional parameter semantics beyond what's already in the schema, but with complete schema coverage, a 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 specific action ('extract') and resources ('emails, phone numbers, and social media links') from a specific source ('any web page'). It distinguishes itself from sibling tools like web_extract_text or web_extract_metadata by focusing specifically on contact information 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 implies usage context (extracting contact data from web pages) but doesn't explicitly state when to use this tool versus alternatives like web_extract_structured or web_extract_batch. No guidance is provided on prerequisites, limitations, or exclusions beyond the cost mention.

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

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

Annotations cover read-only, idempotent, non-destructive, and open-world hints, so the agent knows this is a safe, repeatable operation. The description adds valuable context beyond annotations by disclosing the cost ('$0.02 USDC per request via x402 on Base'), which is critical behavioral information not captured in structured fields. No contradictions with annotations.

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

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

The description is extremely concise with two sentences: the first states the purpose and scope, and the second provides critical cost information. Every word earns its place, with no redundancy or fluff, making it highly efficient and front-loaded with essential details.

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

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

Given the tool's simplicity (1 parameter, 100% schema coverage, rich annotations), the description is nearly complete. It covers purpose, cost, and scope. The main gap is the lack of output schema, so return values (e.g., format of extracted metadata) are undocumented, but annotations provide safety context, making it sufficient for basic use.

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

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

The input schema has 100% description coverage, with the 'url' parameter fully documented. The description does not add any parameter-specific details beyond what the schema provides (e.g., URL format constraints or examples). Baseline score of 3 is appropriate as the schema carries the full burden of parameter documentation.

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

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

The description clearly states the specific action ('extract') and resources ('title, author, description, images, links, and headings') from a specific target ('any URL'). It distinguishes itself from siblings like web_extract_text or web_extract_structured by specifying the exact metadata fields extracted, making the purpose unambiguous and differentiated.

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

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

The description implies usage for extracting metadata from URLs, but provides no explicit guidance on when to use this tool versus alternatives like web_extract_structured or web_extract_text. It mentions a cost, which hints at financial considerations, but lacks clear when/when-not scenarios or named alternatives for metadata extraction tasks.

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

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context about the cost ('$0.03 USDC per request via x402 on Base'), which is not captured in annotations and is critical for usage decisions. No contradictions with annotations exist.

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

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

The description is extremely concise with two sentences: the first states the core functionality, and the second provides essential cost information. Every sentence adds value without any redundancy or 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 the simple single-parameter input, comprehensive annotations, and lack of output schema, the description is mostly complete. It covers purpose and cost, but could benefit from mentioning output format (e.g., plain text) or limitations (e.g., PDF size, OCR support) to fully inform usage.

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

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

Schema description coverage is 100%, with the parameter 'url' fully documented in the schema as 'PDF URL to extract text from'. The description adds no additional parameter details beyond what the schema provides, meeting the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Extract text content') and resource ('from any PDF URL'), distinguishing it from siblings like web_extract_metadata or web_extract_structured by focusing on raw text extraction from PDFs specifically.

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 PDF text extraction but provides no explicit guidance on when to use this tool versus alternatives like web_extract_text (for general web content) or web_extract_structured (for structured data). The cost mention hints at financial considerations but doesn't define selection criteria.

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

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by disclosing the cost ('Costs $0.05 USDC per request via x402 on Base'), which is critical behavioral information not captured in structured fields. No contradiction with annotations exists.

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 and front-loaded, with two sentences that efficiently convey purpose, data fields, and cost information. Every sentence earns its place by providing essential information without redundancy or 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 the tool's moderate complexity (single parameter, no output schema), the description is largely complete. It covers purpose, specific data fields, and cost, but lacks details on output format, error handling, or rate limits. With annotations providing safety profile and schema covering parameters, the description is adequate though not exhaustive.

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, with the 'url' parameter fully documented. The description does not add any parameter-specific semantics beyond what the schema provides, such as URL format requirements or extraction limitations. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the specific action ('Extract product information') and resource ('from any product page'), listing the exact data fields retrieved (name, price, reviews, ratings). It distinguishes itself from sibling tools like 'web_extract_metadata' or 'web_extract_structured' by focusing specifically on product data 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 provides clear context for when to use this tool ('from any product page'), but does not explicitly state when not to use it or name specific alternatives. It implies usage for product data extraction versus general web extraction, but lacks explicit exclusions or comparisons to siblings like 'web_extract_batch' or 'web_extract_compare'.

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

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

Annotations cover read-only, open-world, idempotent, and non-destructive hints, so the bar is lower. The description adds cost information ('Costs $0.05 USDC per request via x402 on Base'), which is useful context beyond annotations. However, it doesn't detail rate limits, error handling, or output format, limiting its value. No contradiction with annotations exists.

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 and front-loaded, with two sentences that efficiently convey purpose and cost. Every sentence adds value without waste, making it appropriately sized and well-structured for quick understanding.

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 (extraction from URLs), rich annotations, and no output schema, the description is somewhat complete but has gaps. It covers purpose and cost but lacks details on output format, error cases, or when to use versus siblings. This makes it adequate but with clear room for improvement, scoring a 3.

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 for its single parameter (url), so the baseline is 3. The description adds no additional parameter semantics beyond what the schema provides, such as URL format constraints or examples, keeping it at 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's purpose: 'Full structured extraction: text, tables, JSON-LD, metadata from any URL.' This specifies the verb ('extract') and resource ('structured data'), though it doesn't explicitly differentiate from siblings like web_extract_text or web_extract_metadata. It's clear but lacks sibling differentiation, warranting a 4.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions cost but not context, prerequisites, or comparisons to siblings like web_extract_text or web_extract_metadata. This absence of usage guidance results in a score of 2.

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

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

Annotations already cover key behavioral traits (read-only, open-world, idempotent, non-destructive). The description adds useful context about the cost ($0.01 USDC per request via x402 on Base), which is not captured in annotations. However, it lacks details on rate limits, error handling, or output format, limiting its transparency.

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

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

The description is extremely concise and front-loaded, consisting of two sentences that efficiently convey the core functionality and cost. Every sentence adds value without redundancy, making it easy for an agent to parse and understand.

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) and rich annotations, the description is somewhat complete but lacks critical details. It does not explain the return format (e.g., plain text, structured data) or potential limitations (e.g., URL accessibility, content types), which are important 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 input schema has 100% description coverage, with the 'url' parameter well-documented. The description does not add any semantic details beyond what the schema provides, such as URL validation rules or supported protocols. With high schema coverage, a 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's purpose as extracting clean, readable text from web page URLs. It uses specific verbs ('extract') and resources ('text from any web page URL'), but does not explicitly differentiate from sibling tools like web_extract_structured or web_extract_ai_summary, which might offer similar text extraction with additional processing.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions a cost per request, but does not specify scenarios where this tool is preferred over siblings like web_extract_batch or web_extract_structured, leaving the agent without usage context.

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

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

Annotations already indicate this is a read-only, non-destructive, idempotent operation with open-world data. The description adds valuable context beyond annotations: it discloses the cost ('$0.10 USDC per request') and payment mechanism ('via x402 on Base'), which are critical behavioral traits not covered by annotations. No contradiction with annotations exists.

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

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

The description is extremely concise (two sentences) and front-loaded with the core functionality. Every sentence adds value: the first defines the tool's purpose, and the second provides critical cost information. There is no wasted text or redundancy.

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

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

Given the tool's moderate complexity (two parameters, no output schema), the description is mostly complete. It covers purpose, cost, and payment method, but lacks details on output format (e.g., structure of translated text) or error handling. With annotations providing safety context, it's adequate but could be more 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 description coverage is 100%, with clear descriptions for both parameters (URL and target language). The description does not add any semantic details beyond what the schema provides (e.g., it doesn't explain language code formats or URL constraints). With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the specific action ('Extract text from a URL and translate it to any language'), identifying both the resource (URL text) and the operation (extract + translate). It distinguishes itself from sibling tools like 'web_extract_text' (extract only) and 'web_extract_ai_summary' (extract + summarize).

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

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

The description implies usage context (when you need both extraction and translation of web content) but does not explicitly state when to use this tool versus alternatives like 'web_extract_text' followed by a separate translation tool, or when not to use it (e.g., for non-text content). No explicit alternatives or exclusions are provided.

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