AudioAlpha

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

No annotations are provided, so the description carries full burden. It mentions what data is returned (metadata fields) but lacks behavioral details such as error handling, authentication needs, rate limits, or whether it's a read-only operation. For a tool with no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the purpose and lists key metadata fields. It avoids unnecessary words, though it could be slightly more structured (e.g., separating fields with commas or bullets).

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 provides basic purpose and return fields, but lacks completeness for a tool in a complex domain with many siblings. It doesn't cover behavioral aspects or usage context, leaving gaps that could hinder an agent's effective use.

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

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

Schema description coverage is 100%, with the single parameter 'episode_id' documented in the schema. The description adds no additional meaning about the parameter beyond what the schema provides, such as format examples or constraints, so it meets 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 verb 'Get' and resource 'metadata for a specific episode', listing specific fields like title, publish date, and sentiment scores. It distinguishes from siblings like get_episode_summary or get_episode_transcript by focusing on metadata, but doesn't explicitly contrast with get_episode_full, which might overlap.

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

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

No guidance on when to use this tool versus alternatives like get_episode_full or get_episode_summary is provided. The description implies usage for episode metadata, but lacks explicit context or exclusions, leaving the agent to infer based on sibling names 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?

No annotations are provided, so the description carries the full burden. It states it's a read operation ('Get'), but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or response format. For a tool with no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes essential details without redundancy. Every word earns its place.

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

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

Given no annotations and no output schema, the description is incomplete for a tool that returns complex data ('details, summary, quotes and asset mentions'). It lacks information on response structure, pagination, or error cases. However, it adequately covers the purpose and usage relative to siblings, making it minimally viable but with 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 100%, so the schema already documents the single parameter 'episode_id'. The description doesn't add any meaning beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the verb ('Get') and resource ('full episode data'), specifying what data is included ('details, summary, quotes and asset mentions'). It distinguishes from siblings like get_episode_details, get_episode_quotes, etc., by emphasizing it provides comprehensive data 'in one call'.

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

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

The description explicitly indicates when to use this tool: to get 'full episode data in one call' instead of making multiple separate calls to sibling tools like get_episode_details or get_episode_quotes. This provides clear guidance on alternatives and context.

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

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

No annotations are provided, so the description carries full burden. It mentions what data is returned but doesn't disclose behavioral traits such as rate limits, authentication needs, pagination, error handling, or whether it's a read-only operation. The description is minimal and lacks operational context.

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

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

The description is a single, efficient sentence that front-loads the purpose and key data fields. It avoids redundancy and wastes no words, though it could be slightly more structured for clarity.

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

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

Given no annotations and no output schema, the description is incomplete. It doesn't explain the return format, data structure, or potential limitations. For a tool that returns complex data (quotes with sentiment and tickers), more context is needed to guide effective use.

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

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

Schema description coverage is 100%, so the input schema fully documents the episode_id parameter. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints, meeting the baseline for high coverage.

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

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

The description clearly states the verb 'Get' and resource 'all quotes extracted from a specific episode', specifying the data fields included (speaker, α-sentiment score, associated ticker). It distinguishes from siblings like get_episode_details or get_episode_transcript by focusing on quotes with sentiment and ticker data, though it doesn't explicitly name alternatives.

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

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

No guidance is provided on when to use this tool versus alternatives like get_ticker_featured_quotes or get_market_featured_quotes. The description implies usage for a specific episode, but lacks context on prerequisites, exclusions, or comparison with sibling tools.

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

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

No annotations are provided, so the description carries full burden. It mentions what is returned (transcript summary, assets, α-sentiment), but lacks details on permissions, rate limits, error handling, or response format. For a read operation with no annotations, this is a significant gap.

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

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

The description is a single, efficient sentence that front-loads the purpose and key details (assets, α-sentiment) without unnecessary words. Every part earns its place.

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

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

Given no annotations and no output schema, the description partially compensates by specifying return content (summary, assets, sentiment). However, it lacks details on behavioral traits and output structure, making it adequate but with clear gaps for a tool with one parameter.

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 one parameter (episode_id) documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints, so it meets 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 verb 'Get' and the resource 'transcript summary for a specific episode', specifying it includes assets mentioned and their α-sentiment. It distinguishes from siblings like get_episode_details or get_episode_transcript by focusing on summary and sentiment, but doesn't explicitly contrast them.

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

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

No guidance is provided on when to use this tool versus alternatives like get_episode_details or get_episode_full. The description implies usage for summary and sentiment, but lacks explicit when/when-not instructions 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?

No annotations are provided, so the description carries the full burden. It discloses the plan restrictions ('Pro and Enterprise plans only'), which is useful behavioral context. However, it lacks details on other traits such as rate limits, authentication needs, or error handling, leaving gaps in transparency for a tool with no annotation support.

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

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

The description is two concise sentences with zero waste: the first sentence specifies the purpose and features, and the second adds critical access information. It is appropriately sized and front-loaded, making it easy to understand 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 has no annotations and no output schema, the description does well by covering purpose and access restrictions. However, it lacks details on return values (e.g., transcript format) and behavioral aspects like error cases, which would improve completeness for a tool with such limited structured data.

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

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

The schema description coverage is 100%, with the single parameter 'episode_id' documented in the schema. The description does not add any meaning beyond what the schema provides, such as format examples 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 verb 'Get' and the resource 'full transcript for a specific episode', specifying it includes 'speaker diarization and speaker name mapping'. It distinguishes from siblings like get_episode_details or get_episode_summary by focusing on the transcript content rather than metadata or summaries.

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 with 'Pro and Enterprise plans only', indicating access restrictions. However, it does not explicitly state when to use this tool versus alternatives like get_episode_full or get_episode_quotes, which might overlap in functionality, leaving some ambiguity in sibling differentiation.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what data is returned (episodes with name, sentiment score, summary) but doesn't mention pagination, rate limits, authentication needs, error conditions, or whether the operation is read-only (though implied by 'Get'). This leaves significant gaps for a tool with potential complexity.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes essential details (date filtering, returned fields). Every element earns its place with 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 no annotations, no output schema, and a simple input schema, the description adequately covers the basic purpose and returned data. However, for a tool that might involve network calls, data processing, or sentiment analysis, it lacks details on behavioral aspects like performance, errors, or data freshness, making it minimally complete but with 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 100%, so the schema already documents the single parameter (date format and optional behavior). The description adds marginal value by reinforcing the date focus but doesn't provide additional syntax, format details, or constraints beyond what the schema specifies. Baseline 3 is appropriate when schema does the heavy lifting.

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 all crypto podcast episodes'), resource ('published on a given date'), and scope ('including podcast name, α-sentiment score (0-10 scale), and transcript summary'). It distinguishes from siblings by focusing on market-related episodes with sentiment scoring, unlike general episode tools like get_episode_details or get_podcast_episodes.

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 retrieving market-focused episodes with sentiment analysis on a specific date, but doesn't explicitly state when to use this versus alternatives like get_market_snapshot or get_market_signals. It provides context (date-based retrieval) but lacks explicit exclusions or comparisons to sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves and ranks quotes but doesn't cover critical aspects like pagination, rate limits, authentication needs, error handling, or what 'top' and 'relevance score' mean operationally. This leaves significant gaps in understanding how the tool behaves.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it highly concise and well-structured.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain the return format (e.g., structure of quotes, what 'relevance score' entails), error conditions, or how results are limited (e.g., number of quotes). For a tool with no structured behavioral data, this leaves too many unknowns for effective use.

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

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

The schema description coverage is 100%, with the parameter 'date' fully documented in the schema. The description adds minimal value by mentioning 'for a given date' and 'Omit for latest', which aligns with but doesn't significantly expand upon the schema. This meets 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 verb ('Get') and resource ('top featured quotes from crypto podcasts'), specifying the ranking criterion ('by relevance score') and date parameter. However, it doesn't explicitly differentiate from sibling tools like 'get_ticker_featured_quotes' or 'get_episode_quotes', which appear to serve similar but distinct purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_ticker_featured_quotes' or 'get_episode_quotes'. It mentions a date parameter but doesn't explain when to omit it or how it affects results, leaving usage context unclear.

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 lists output fields (signal type, horizon, etc.) but lacks critical behavioral details: whether it's read-only or mutative, any rate limits, authentication requirements, error handling, or pagination. The disclaimer '(Not financial advice.)' is useful but doesn't cover operational behavior.

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

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

The description is concise and front-loaded, with the core purpose in the first sentence and additional details efficiently listed. The disclaimer is brief and relevant. No wasted words, though it could be slightly more structured (e.g., bullet points for output fields).

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 is moderately complete for a simple read tool. It covers the purpose and output fields but lacks behavioral context (e.g., safety, performance) and usage guidelines. For a tool with 1 parameter and 100% schema coverage, it's adequate but leaves gaps in operational understanding.

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

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

Schema description coverage is 100%, so the schema fully documents the single optional parameter 'date'. The description adds no parameter-specific semantics beyond implying date filtering. It doesn't explain format nuances, default behavior when omitted, or edge cases, so it meets the baseline but adds minimal 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: 'Get scenario strategic signals across all assets for a given date.' It specifies the verb ('Get'), resource ('scenario strategic signals'), and scope ('across all assets'). However, it doesn't explicitly differentiate from sibling tools like 'get_ticker_signals' or 'get_market_snapshot', 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 sibling tools like 'get_ticker_signals' (for specific tickers) or 'get_market_snapshot' (for broader market data), nor does it specify prerequisites, exclusions, or optimal use cases beyond the basic date parameter.

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 describes the return content but lacks critical behavioral details such as rate limits, authentication requirements, error handling, or whether the data is cached or real-time, which are essential for safe and effective tool invocation.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose. However, the long list of return fields could be streamlined or structured more clearly (e.g., grouped or bulleted) to improve readability while maintaining informativeness.

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 the tool (returns multiple detailed metrics) and the absence of both annotations and an output schema, the description partially compensates by listing return fields. However, it lacks details on data formats, scales, or behavioral aspects, leaving gaps in completeness 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?

The input schema has 100% description coverage, with the 'date' parameter fully documented in the schema. The description adds no additional parameter semantics beyond implying a date-based query, so it meets the baseline score of 3 without compensating for any gaps.

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

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

The description clearly states the specific action ('Get the overall crypto market daily snapshot') and resource ('daily narrative insight report'), distinguishing it from sibling tools like get_market_episodes or get_ticker_snapshot by focusing on a comprehensive daily report rather than episodes, themes, or ticker-specific data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like get_market_signals or get_ticker_snapshot, leaving the agent to infer usage based on tool names alone without explicit context or exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what the tool returns (themes with specific attributes) but does not cover critical aspects such as rate limits, authentication requirements, data freshness, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, scope, and output details without any wasted words. It is front-loaded with the core action and resource, making it easy to parse and understand 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 complexity (retrieving analyzed themes with multiple scores) and lack of annotations and output schema, the description is moderately complete. It specifies the output attributes but does not explain return formats, pagination, or error cases. For a tool with no structured output information, more detail on behavioral aspects would enhance 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?

The input schema has 100% description coverage, clearly documenting the optional 'date' parameter with format and default behavior. The description adds no additional parameter semantics beyond what the schema provides, such as date range constraints or examples. With high schema coverage, the baseline score of 3 is appropriate as the description does not compensate but also does not 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 specific action ('Get narrative themes') and resource ('dominating the crypto podcast space'), with precise scope ('on a given date') and detailed output content ('Each theme includes title, summary, podcast coverage count, fragility score, novelty score and counterfactual narrative'). It effectively distinguishes from sibling tools like get_market_episodes or get_market_signals by focusing on themes rather than episodes or signals.

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 retrieving themes on a specific date, with the input schema indicating 'Omit for latest.' However, it lacks explicit guidance on when to use this tool versus alternatives like get_market_snapshot or get_market_signals, which might overlap in market analysis. No exclusions or prerequisites are mentioned, leaving usage context partially inferred.

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 implies a read-only operation ('Get') and mentions authentication ('authenticated user'), but lacks details on rate limits, error handling, response format, or pagination. This is inadequate for a tool with zero annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it highly concise and well-structured.

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

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

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate. It covers the basic purpose but lacks behavioral details (e.g., response format, authentication specifics) that would be helpful for an AI agent, especially with no annotations. It's complete enough for a simple read tool but could be more informative.

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 0 parameters, and schema description coverage is 100%, so no parameter information is needed. The description doesn't add parameter details, but that's acceptable here. A baseline of 4 is appropriate as it doesn't mislead or omit required parameter context.

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

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

The description clearly states the action ('Get the list') and resource ('crypto assets the authenticated user has favorited on AudioAlpha'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_my_favorite_podcasts' or 'get_my_feed', 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 prerequisites (e.g., authentication), compare it to similar tools (e.g., 'get_my_favorite_podcasts'), or specify contexts where it's appropriate, leaving usage unclear.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'authenticated user' which implies authentication is required, adding some context. However, it doesn't disclose other behavioral traits like rate limits, pagination, error handling, or what the return format looks like (list structure, fields included). For a read operation with zero annotation coverage, this leaves significant gaps.

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

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

The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes essential context (authenticated user, AudioAlpha platform). Every word earns its place 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 simplicity (0 parameters, no output schema), the description is adequate but minimal. It covers the basic purpose and authentication context, but lacks details on output format, error cases, or usage compared to siblings. For a tool with no annotations, it should ideally provide more behavioral transparency to be fully complete.

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

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

The tool has 0 parameters, and schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, so it meets the baseline expectation. No additional parameter information is required or provided.

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

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

The description clearly states the verb ('Get') and resource ('list of podcasts the authenticated user follows'), making the purpose specific and understandable. It distinguishes from siblings by focusing on user-followed podcasts rather than market data, episodes, or search results. However, it doesn't explicitly differentiate from 'get_my_favorite_assets' or 'get_my_feed', which could be related user-specific tools.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (authentication), compare to similar tools like 'get_my_favorite_assets' or 'search_podcasts', or indicate when this tool is appropriate versus others for accessing podcast data.

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

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

No annotations are provided, so the description carries full burden. It mentions the return structure but doesn't disclose behavioral traits like authentication requirements, rate limits, whether it's a read-only operation, or how personalization works. The description is functional but lacks operational context.

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

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

The description is a single, dense sentence that efficiently conveys the tool's purpose and detailed return structure without unnecessary words. Every element serves to inform the user about what the tool delivers.

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 to detail the return values, which compensates for the lack of output schema. However, for a tool with personalization and potentially complex behavior, more context on authentication, data freshness, or limitations would improve completeness.

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

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

Schema description coverage is 100%, so the schema fully documents the single optional parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage through structured data alone.

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

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

The description clearly states the verb 'Get' and the resource 'personalized feed', specifying it's based on 'user's favorite assets and followed podcasts'. It distinguishes from siblings like get_market_snapshot or get_my_favorite_assets by focusing on a combined, curated feed rather than individual components.

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

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

No guidance on when to use this tool versus alternatives. While the purpose is clear, there's no mention of when to choose this over get_market_snapshot, get_my_favorite_assets, or other sibling tools that might provide similar data in different formats.

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 states the basic function but lacks critical details such as whether this is a read-only operation, how results are ordered, if there's pagination, what format the episodes are returned in, or any rate limits. The description is too sparse for a tool that presumably returns multiple items.

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and doesn't include unnecessary details. This is an excellent example of conciseness.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'recent' means, how episodes are ordered, what fields are returned, or if there are any limitations beyond the parameter constraints. For a tool that fetches multiple items, this leaves significant gaps for an AI agent.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters (podcast_id and limit). The description mentions 'podcast ID' but doesn't add any semantic context beyond what's in the schema, such as where to obtain the ID or how 'recent' is defined. Baseline 3 is appropriate since the schema does the heavy lifting.

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 ('Get recent episodes') and target resource ('for a specific podcast by podcast ID'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_podcast_latest' or 'get_podcast_latest_full', which might have overlapping functionality.

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 minimal guidance on when to use this tool, mentioning only that it retrieves episodes for a specific podcast ID. It doesn't explain when to choose this over alternatives like 'get_podcast_latest' or 'get_episode_details', 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the data returned but does not cover important aspects like error handling (e.g., what happens if the podcast_id is invalid), rate limits, authentication needs, or whether it's a read-only operation. This leaves gaps in understanding how the tool behaves beyond basic functionality.

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

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

The description is a single, efficient sentence that front-loads the core purpose and lists returned data. It avoids unnecessary words, but could be slightly improved by structuring the data list more clearly (e.g., with bullet points) for better readability, though this is minor.

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 provides basic functionality but lacks completeness. It specifies what data is returned but not the format or structure, and misses behavioral context like error handling. For a tool with one parameter and no complex schema, it's minimally adequate but leaves room for improvement in covering operational aspects.

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 'podcast_id' documented as 'Podcast ID from search_podcasts'. The description adds no additional meaning beyond this, such as format examples or constraints. Since the schema already provides adequate coverage, a baseline score of 3 is appropriate, as the description does not compensate but also does not 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 action ('Get the latest episode') and resource ('for a specific podcast'), and lists specific data returned (title, episode_id, transcript summary, etc.). However, it does not explicitly differentiate from siblings like 'get_podcast_episodes' (which might list multiple episodes) or 'get_podcast_latest_full' (which might return more detailed data), leaving some ambiguity in scope.

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

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

No guidance is provided on when to use this tool versus alternatives. For example, it does not explain when to choose 'get_podcast_latest' over 'get_podcast_episodes' (for multiple episodes) or 'get_podcast_latest_full' (for more comprehensive data). The description implies usage for the latest episode only but lacks explicit context or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates this is a read operation ('Get') and specifies the output includes 'full details including quotes and asset mentions', which adds some context about return content. However, it lacks critical details like whether this requires authentication, has rate limits, or how it handles errors (e.g., invalid podcast IDs). For a tool with no annotation coverage, this is a significant gap.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Get the latest episode for a podcast') and adds clarifying details ('with full details including quotes and asset mentions'). There is no wasted verbiage, repetition, or unnecessary elaboration, making it highly concise and well-structured.

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

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

Given the tool's moderate complexity (fetching enriched episode data), no annotations, and no output schema, the description is partially complete. It specifies the output includes 'full details including quotes and asset mentions', which helps anticipate return values, but lacks information on behavioral traits (e.g., auth needs) and usage context. This is adequate as a minimum but has clear gaps 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?

The input schema has 100% description coverage, with the single parameter 'podcast_id' documented as 'Podcast ID from search_podcasts'. The description adds no additional parameter semantics beyond implying the tool fetches data for a specific podcast. Since the schema already fully describes the parameter, the baseline score of 3 is appropriate—the description doesn't compensate but doesn't need to 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 ('Get'), resource ('latest episode for a podcast'), and scope ('full details including quotes and asset mentions'), which distinguishes it from simpler sibling tools like 'get_podcast_latest' (likely minimal details) or specialized ones like 'get_episode_quotes' (only quotes). However, it doesn't explicitly differentiate from 'get_episode_full', which might serve a similar purpose for any episode rather than just the latest.

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 prerequisites (e.g., needing a podcast ID from 'search_podcasts'), exclusions, or comparisons to siblings like 'get_podcast_episodes' (for multiple episodes) or 'get_episode_details' (for specific episodes). This leaves the agent to infer usage from context 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?

No annotations are provided, so the description carries full burden. It mentions output features (α-sentiment scores, ranking) but lacks behavioral details such as rate limits, authentication needs, data freshness, or error handling. For a tool with no annotations, this leaves significant gaps in understanding how it behaves operationally.

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

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

The description is a single, dense sentence with zero waste—it efficiently conveys purpose, output features, and key details (sentiment scale, ranking). It is front-loaded with the core action and resource, 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?

Given no annotations and no output schema, the description provides basic purpose and output features but lacks completeness for a tool with behavioral and output uncertainties. It does not explain return values or error cases, leaving the agent with incomplete context for reliable invocation.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters (date and ticker). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate as the schema handles the heavy lifting.

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

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

The description clearly states the verb 'Get' and the resource 'featured podcast quotes for a specific crypto asset', specifying the output includes α-sentiment scores and ranking by selection score. It distinguishes from siblings like get_episode_quotes (general quotes) and get_market_featured_quotes (market-wide quotes) by focusing on a single ticker with sentiment analysis.

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

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

The description implies usage for retrieving sentiment-scored quotes for a crypto asset on a date, but does not explicitly state when to use this tool versus alternatives like get_ticker_signals or get_market_featured_quotes. No exclusions or prerequisites are mentioned, leaving some ambiguity in 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?

Since no annotations are provided, the description carries the full burden. It fully discloses that the date parameter is only honored for enterprise tier and silently ignored otherwise, that days are capped per tier, and that requests above the cap are silently capped. It also details the response structure.

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 with clear sections for purpose, required/optional parameters, tier details, and response. It is detailed but not overly verbose; every sentence adds value. Minor reduction because it could be slightly more concise.

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

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

Given no output schema, the description sufficiently explains the response format (array of daily snapshots with snapshot_date and standard fields, plus metadata). It also covers tier behavior and edge cases. Could mention pagination or rate limits if applicable, but otherwise complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds significant value by explaining the default for days, the tier caps, and the conditional behavior of the date parameter. This goes beyond what the schema provides.

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

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

The description clearly states that the tool fetches a historical time series of daily snapshots for one crypto ticker. It lists specific use cases (trend analysis, charting, computing averages) and distinguishes from sibling tools like get_ticker_snapshot which provides a current snapshot.

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

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

The description explicitly tells when to call the tool (e.g., when user asks about a ticker's recent trend or wants to chart). It explains parameter requirements, tier caps, and the behavior of the date parameter per tier. However, it does not explicitly state when not to use it or suggest alternatives.

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

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

No annotations are provided, so the description carries the full burden. It discloses the tool's behavior regarding output structure based on the category parameter, which is valuable. However, it doesn't mention rate limits, authentication needs, error conditions, or whether this is a read-only operation (though 'Get' implies read-only). For a tool with no annotations, this leaves some behavioral aspects unspecified.

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: the first sentence states the core purpose, and the second explains the key behavioral nuance. Every sentence earns its place with no wasted words, making it easy for an AI 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 moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains the output structure variation based on parameters, which is crucial. However, without annotations or output schema, it doesn't describe the format of 'highlights object' or 'LeaderboardEntry' arrays, leaving the agent uncertain about return values. For a read operation with parameter-driven output differences, more detail on return structures would be helpful.

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

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

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description adds marginal value by explaining the semantic effect of the category parameter on output structure ('Without category param... returns highlights object... With category param... returns array of LeaderboardEntry'). This clarifies the parameter's impact beyond the schema's technical description, but doesn't add significant new information about individual 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: 'Get ticker leaderboard for a given date.' It specifies the verb ('Get') and resource ('ticker leaderboard'), and distinguishes it from siblings by focusing on leaderboard data rather than episodes, podcasts, or market data. However, it doesn't explicitly contrast with specific sibling tools beyond the general domain difference.

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

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

The description provides clear context for when to use the tool: 'Without category param, returns highlights object... With category param..., returns array of LeaderboardEntry.' This gives explicit guidance on how parameter usage affects output. It doesn't mention when not to use it or name specific alternatives among siblings, but the parameter-based guidance is sufficient for most decision-making.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what data is returned but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or pagination behavior. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 in two sentences: one stating the purpose and returned data, and a disclaimer. It's front-loaded with the core functionality. However, the disclaimer adds minimal value for tool selection, slightly reducing conciseness.

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

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

Given no annotations and no output schema, the description provides basic purpose and return field information but lacks behavioral context (e.g., safety, performance) and detailed output semantics. It's adequate for a simple read operation but incomplete for full agent understanding, especially with sibling tools present.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters ('ticker' and 'date'). The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain format details or provide examples). This meets the baseline score when schema coverage is high.

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

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

The description clearly states the tool's purpose: 'Get scenario signals for a specific crypto asset.' It specifies the verb ('Get') and resource ('scenario signals'), and lists the returned data fields. However, it doesn't explicitly differentiate this from sibling tools like 'get_market_signals' or 'get_ticker_snapshot', which reduces it from 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. With sibling tools like 'get_market_signals' and 'get_ticker_snapshot' available, there's no indication of when this specific tool is appropriate or what distinguishes it from those options. The disclaimer '(Not financial advice.)' is not usage guidance.

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

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

With no annotations, the description carries full burden but only lists return fields without explaining behavioral traits like rate limits, authentication needs, error handling, or data freshness. It mentions 'daily insights' but doesn't clarify if historical dates are supported beyond the schema's date parameter.

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

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

The description is a single, dense sentence that efficiently lists all return fields without fluff. However, it could be more front-loaded by stating the core purpose first before detailing outputs, and some terms like 'α-index' are unexplained.

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

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

For a tool with no annotations and no output schema, the description adequately covers the purpose and outputs but lacks behavioral context and usage guidelines. It's complete enough for basic understanding but misses details like response format or error cases.

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

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

Schema description coverage is 100%, so the schema fully documents the two parameters. The description adds no parameter-specific information beyond implying 'crypto asset' for the ticker, which is already in the schema. Baseline 3 is appropriate as the schema handles parameter semantics.

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

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

The description clearly states the verb ('Get') and resource ('detailed daily insights snapshot for a specific crypto asset'), distinguishing it from siblings that focus on episodes, markets, podcasts, or other ticker functions. It specifies the exact type of data returned, making the purpose highly specific.

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

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

No guidance is provided on when to use this tool versus alternatives like get_ticker_signals or get_market_snapshot. The description lists outputs but does not indicate context, prerequisites, or exclusions for usage.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Returns podcast ID, name, artist, language, x-handle, and artwork,' which gives some output details, but it doesn't cover critical aspects like pagination, rate limits, error handling, or authentication needs. For a search tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is highly concise and front-loaded, consisting of two sentences that efficiently convey the core functionality and return values. Every sentence earns its place without redundancy or fluff, 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 complexity (a simple search with one parameter) and the absence of annotations and output schema, the description is moderately complete. It specifies the search scope and return fields, but it lacks details on behavioral traits like pagination or error handling. For a basic search tool, this is adequate but has clear gaps, aligning with a minimum viable score.

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 parameter 'q' documented as 'Podcast name search query e.g. unchained, bankless, chopping block.' The description adds no additional meaning beyond this, as it doesn't elaborate on query syntax, matching behavior, or result ordering. With high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

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 for crypto podcasts by name.' It specifies the verb ('search'), resource ('crypto podcasts'), and scope ('by name'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'get_my_favorite_podcasts' or 'get_podcast_episodes,' which might also retrieve podcast data, so it doesn't fully earn a 5.

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 any prerequisites, exclusions, or comparisons to sibling tools such as 'get_my_favorite_podcasts' for user-specific lists or 'get_podcast_episodes' for episode retrieval. This lack of context leaves the agent to infer usage, which is minimal guidance.

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