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 describes what the tool returns (category codes, display names, recap frequencies) and implies it's a read-only operation through the 'List' verb, but doesn't mention potential limitations like rate limits, authentication requirements, or error conditions.

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 three sentences that each earn their place: the first states the purpose, the second explains the output's utility, and the third details what information each category includes. No wasted words or redundant information.

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

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

Given that the tool has zero parameters, no annotations, but has an output schema, the description provides good context about what the tool returns and how to use the results. It doesn't need to explain return values since an output schema exists, but it usefully describes the output structure and purpose. The main gap is lack of behavioral constraints disclosure.

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 tool has zero parameters, so the description doesn't need to explain any inputs. The schema description coverage is 100% (though trivial with no parameters), and the description appropriately focuses on output semantics rather than input parameters.

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 ('List all available news categories') and resources ('news categories with their recap timeframes'). It distinguishes from siblings by focusing on category metadata rather than news content, which is handled by tools like get_latest_news and get_news_recap.

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 by stating that it 'Returns category codes that can be used with get_latest_news, get_news_recap, and other tools.' This gives practical guidance on its role in the workflow, though it doesn't explicitly state when NOT to use it or name specific 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 effectively describes key traits: it's a paid service ('premium endpoint', 'Payment is handled via the x402 protocol'), specifies the return content ('complete news data including long_context, short_context, entity analysis'), and notes the output format ('returns the payment endpoint and instructions'). This covers cost, data structure, and response behavior, though it lacks details on rate limits or error handling.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first defines the tool, the second lists returned data, and the third explains payment. There's minimal waste, though the structure could be slightly tighter by integrating payment details more seamlessly.

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 (paid service with AI analysis), no annotations, and no output schema, the description is moderately complete. It covers the premium nature, data returned, and payment mechanism, but lacks details on output format beyond 'payment endpoint and instructions', error cases, or how the AI analysis is generated. For a tool with no structured output, more clarity on the response structure would be beneficial.

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 the baseline is 4. The description adds no parameter-specific information, which is acceptable since there are no parameters to document. It focuses on the tool's functionality and payment aspects instead.

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 enriched news with full AI-generated context and analysis.' It specifies the verb ('Get') and resource ('enriched news'), and distinguishes it from siblings like 'get_latest_news' or 'get_news_item' by emphasizing the 'AI-generated context and analysis' aspect. However, it doesn't explicitly contrast with all siblings (e.g., 'get_news_recap' might also involve 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 implies usage context by mentioning it's a 'premium endpoint' with payment via x402, suggesting it should be used when AI-generated analysis is needed and payment is acceptable. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_news_item' or 'search_news', nor does it provide exclusions or clear prerequisites beyond payment handling.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns real-time news with specific fields (headline, sentiment, categories, sources) and has a limit parameter (1-10), but doesn't mention rate limits, authentication needs, or error behaviors. It adds some behavioral context but leaves gaps.

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

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

The description is well-structured and front-loaded with the core purpose, followed by return details and parameter guidance. Every sentence earns its place: the first states what it does, the second describes returns, the third explains category usage, the fourth references a sibling tool, and the Args section details parameters 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 2 parameters with no schema descriptions and an output schema present, the description is mostly complete. It explains parameter semantics thoroughly and notes the output includes sentiment and categories, but doesn't fully address behavioral aspects like error handling or rate limits. The output schema likely covers return values, so this is adequate.

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

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

With 0% schema description coverage, the description fully compensates by explaining both parameters in detail. It clarifies that 'category' filters by topic with examples and can be omitted, and 'limit' specifies the number of items with range and default. This adds significant meaning beyond the bare schema.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('latest curated crypto news headlines'), distinguishing it from siblings like 'get_news_item' (single item) or 'search_news' (search functionality). It specifies the content type (headlines with sentiment, categories, sources) and real-time 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 provides clear context for when to use this tool (to get latest headlines) and mentions an alternative ('get_categories first to see all available category codes'), but doesn't explicitly differentiate from other siblings like 'get_enriched_news' or 'get_news_recap'. It gives practical guidance on parameter usage.

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

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

With no annotations provided, the description carries full burden. It discloses the return content ('full free-tier details including headline, sentiment, categories, sources, and tweet URL'), which is valuable behavioral context. However, it doesn't mention error handling, rate limits, authentication needs, or whether this is a read-only operation (though implied by 'Get').

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 efficiently structured with a clear purpose statement first, followed by return details and parameter documentation. Every sentence adds essential information with zero wasted words, making it easy 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?

For a single-parameter retrieval tool with no annotations and no output schema, the description provides strong context: purpose, return content, parameter semantics, and prerequisites. The main gap is lack of explicit behavioral details like error cases or rate limits, but it's reasonably complete for this complexity level.

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

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

Schema description coverage is 0%, so the description must compensate. It fully explains the single parameter's purpose ('The news item ID'), source ('returned in results from get_latest_news or search_news'), and format (implied as string). This adds complete semantic meaning beyond the bare schema.

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

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

The description clearly states the specific action ('Get a specific news item') and resource ('by its ID'), distinguishing it from siblings like get_latest_news (list) or search_news (search). It explicitly identifies the target resource and method of retrieval.

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

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

The description provides clear context for when to use this tool ('by its ID') and mentions where the ID comes from ('returned in results from get_latest_news or search_news'), establishing prerequisites. However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings.

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

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

With no annotations provided, the description carries the full burden. It discloses that the output is 'concise narrative summarizing' and mentions update frequencies ('updated hourly'), which adds useful behavioral context. However, it doesn't cover aspects like rate limits, authentication needs, or error conditions.

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 and front-loaded with the core purpose. The parameter explanations are necessary given the 0% schema coverage. However, the formatting with line breaks could be slightly more compact while maintaining readability.

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

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

Given no annotations and no output schema, the description does well on parameters but lacks information about the return format (beyond 'concise narrative'), error handling, or performance characteristics. For a tool with 2 parameters and behavioral complexity, 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?

Schema description coverage is 0%, so the description must compensate fully. It provides detailed semantics for both parameters: category (required, category code, references get_categories, lists popular choices) and timeframe (time window, default '12h', specific recommendations for different categories). This adds substantial meaning beyond the bare schema.

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

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

The description clearly states the tool's purpose with specific verbs ('get', 'summarizing') and resource ('AI-generated news recap/summary for a specific category'). It distinguishes from siblings by focusing on recaps/summaries rather than raw news items, categories, or enriched/search results.

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 ('great for getting up to speed quickly') and references an alternative tool ('Use get_categories to see options'). However, it doesn't explicitly state when not to use it or compare it to other siblings like get_enriched_news or get_latest_news.

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 effectively describes key traits: it's a paid service via x402 protocol using USDC on Base network, returns payment endpoint and instructions, and combines curated news with web search. However, it lacks details on rate limits, error handling, or response format, which are important for a paid 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 well-structured and front-loaded, starting with the core purpose, then detailing the output format and target audience, and ending with payment information. Each sentence adds value, but it could be slightly more concise by integrating some details more tightly.

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 a paid tool with no annotations and no output schema, the description is moderately complete. It covers the purpose, payment method, and target audience but lacks details on the output structure (e.g., format of bullet points), error cases, or example responses, which would enhance completeness for an AI agent.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately does not discuss parameters, focusing instead on the tool's functionality and payment mechanism. This meets the baseline for tools with no parameters.

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 a 24-hour AI-generated summary') and resources ('for any crypto ticker or topic'), distinguishing it from sibling tools like get_categories, get_enriched_news, etc. It explicitly mentions the unique combination of 'Gloria's curated news with real-time web search' and the target audience 'fund managers and trading agents'.

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 obtaining decision-grade bullet-point summaries of crypto tickers/topics, especially for fund managers and trading agents. It does not explicitly state when not to use it or name alternatives among sibling tools, but the specificity of the use case implies differentiation from other news-related tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns 'headlines, sentiment, categories, and sources', which adds some output context, but fails to cover critical aspects like rate limits, authentication needs, error handling, or whether results are paginated. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a brief elaboration and a structured 'Args' section. There's minimal waste, though the separation of the 'Args' block slightly disrupts flow. Every sentence adds value, making it efficient without being overly terse.

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 (2 parameters, no annotations, but with an output schema), the description is partially complete. It covers input semantics well but lacks usage guidelines and behavioral details. The presence of an output schema means the description doesn't need to explain return values, but it should still address when to use this tool over siblings and operational constraints.

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

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

The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains 'query' as a 'Search keyword or phrase' with examples ('ETF', 'SEC', 'Uniswap'), and clarifies 'limit' as 'Number of results to return' with a range (1-5) and default (5). This compensates well for the schema's lack of descriptions, though it doesn't detail query syntax or limit enforcement.

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: 'Search curated crypto news by keyword' specifies the verb (search), resource (crypto news), and scope (curated). It distinguishes from siblings like 'get_latest_news' by emphasizing keyword-based searching rather than recency-based retrieval. However, it doesn't explicitly contrast with 'get_enriched_news' or 'get_news_recap', leaving some sibling differentiation incomplete.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer 'search_news' over 'get_latest_news' (for recency) or 'get_enriched_news' (for enhanced data), nor does it specify prerequisites or exclusions. The only implied context is keyword-based searching, but this is insufficient for clear usage decisions among the six sibling tools.

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