AudioAlpha

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

With no annotations, the description carries full burden but only states what data is returned, not behavioral traits like permissions, rate limits, or error handling. It mentions 'α-sentiment score (crypto)' and 'traditional markets sentiment' but doesn't explain their significance or how they're derived, leaving gaps 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 the inclusion of detailed sentiment scales adds slight complexity without full explanation.

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 listing returned fields, but it lacks details on format, structure, or behavioral context. For a simple read tool, it's adequate but has clear gaps in completeness for an agent's 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%, with the parameter 'episode_id' documented in the schema. The description adds no additional parameter semantics beyond implying it's for a specific episode, so it meets the baseline 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 verb 'Get' and resource 'metadata for a specific episode', listing specific fields like title, publish date, and sentiment scores. It distinguishes from siblings by focusing on metadata rather than full content, quotes, or transcripts, 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 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 mentions the tool 'Get full episode data' but does not disclose behavioral traits such as whether it's read-only, has rate limits, authentication needs, error handling, or response format. The description is minimal and lacks critical operational details.

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

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

The description is a single, efficient sentence that front-loads the key information: 'Get full episode data in one call'. Every word earns its place, with no wasted text, 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 complexity of a tool that aggregates multiple data types (details, summary, quotes, asset mentions), no annotations, and no output schema, the description is incomplete. It lacks information on response structure, error cases, or any behavioral context, leaving significant gaps for an AI agent to use it effectively.

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

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

Schema description coverage is 100%, with the single parameter 'episode_id' documented in the schema. The description does not add any meaning beyond the schema, such as format examples or constraints, but the high coverage justifies the baseline score of 3.

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

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

The description clearly states the verb 'Get' and the resource 'full episode data', specifying it includes '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 implies usage context by stating 'in one call', suggesting efficiency over using multiple sibling tools for separate data types. However, it does not explicitly state when to use this versus alternatives like get_episode_details or get_episode_summary, nor does it provide exclusions or prerequisites.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it describes what data is returned, it doesn't cover important aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or pagination behavior for large result sets. The description is purely functional without 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 packs substantial information: the action, resource scope, and key data fields. Every word earns its place with zero redundancy or fluff. It's appropriately sized for a simple retrieval tool with one parameter.

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

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

For a simple read operation with one parameter and no output schema, the description adequately covers the core functionality. However, without annotations or output schema, it should ideally provide more behavioral context (like safety, performance, or error handling). The description meets minimum requirements 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%, with the single parameter 'episode_id' clearly documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., format examples, validation rules, or where to find episode IDs). With complete schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the 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, get_episode_summary, or get_episode_transcript by focusing exclusively on quotes with sentiment and ticker 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 doesn't mention siblings like get_ticker_featured_quotes or get_market_featured_quotes, which might offer similar quote data in different contexts. There are no prerequisites, exclusions, or comparative usage hints.

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 behavioral details such as permissions needed, rate limits, error handling, or whether it's a read-only operation. For a tool with no annotations, this is insufficient disclosure of behavioral traits.

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 key details (assets, α-sentiment). There is no wasted verbiage, and it is appropriately sized for the tool's complexity.

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 is moderately complete. It specifies the return content but lacks details on output format, error cases, or behavioral context. For a tool with minimal structured data, it provides basic information but leaves gaps in understanding how to use it effectively.

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

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

Schema description coverage is 100%, with the single parameter 'episode_id' documented in the schema. The description adds no additional meaning beyond the schema, such as format examples or constraints. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.

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 'transcript summary for a specific episode', specifying what the tool does. It distinguishes from siblings like 'get_episode_details' or 'get_episode_transcript' by mentioning 'assets mentioned and their α-sentiment', but doesn't explicitly contrast with them. The purpose is specific but lacks explicit sibling differentiation.

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

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention when to choose it over siblings like 'get_episode_details' or 'get_episode_full', nor does it specify prerequisites or exclusions. The description implies usage for a specific episode summary but offers no contextual advice.

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 plan restrictions (Pro and Enterprise only), which is useful behavioral context. However, it lacks details on rate limits, authentication needs, error conditions, or what the output format looks like (e.g., structured text, JSON). For a tool 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 two concise sentences that efficiently convey the tool's purpose and access restrictions. It is front-loaded with the core functionality, and each sentence adds value 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 no annotations and no output schema, the description is incomplete. It covers the purpose and access restrictions but lacks crucial details like return format (e.g., whether it's plain text, structured data with timestamps), error handling, or prerequisites (e.g., how to obtain episode_id). For a tool with rich potential output (transcript with speaker mapping), more context is needed.

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 does not add any meaning beyond what the schema provides (e.g., it doesn't explain what an episode ID is or where to find it). Baseline is 3 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 verb 'Get' and the resource 'full transcript for a specific episode', with additional details about speaker diarization and name mapping. It distinguishes from siblings like get_episode_details or get_episode_summary by specifying transcript content, but does not explicitly contrast with get_episode_full (which might include transcript).

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

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

The description includes 'Pro and Enterprise plans only', which provides some usage context regarding access restrictions. However, it does not explicitly state when to use this tool versus alternatives like get_episode_full or get_episode_quotes, leaving the agent to infer based on the need for a transcript with speaker details.

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 mentions the data returned (podcast name, sentiment score, transcript summary) but does not address key behaviors like pagination, rate limits, authentication needs, error handling, or whether the date parameter is optional (implied by schema but not explicitly stated in description).

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 without redundancy. Every part earns its place by specifying the action, resource, date scope, and returned data 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 the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and data returned but lacks behavioral context (e.g., how results are structured, error cases), which is needed for full agent understanding without annotations or output schema.

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

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

Schema description coverage is 100%, so the schema fully documents the single parameter (date). The description adds no additional parameter semantics beyond what the schema provides, such as format details or usage context for the optional date, meeting the baseline score of 3.

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

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

The description clearly states 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'), distinguishing it from siblings like get_episode_details or get_podcast_episodes by focusing on market-related episodes with sentiment and summary 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 implies usage for retrieving episodes by date with specific data fields, but lacks explicit guidance on when to use this tool versus alternatives like get_market_snapshot or get_podcast_episodes, and does not 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 ranking by relevance score but fails to detail critical aspects like rate limits, authentication needs, pagination, error handling, or what 'featured' entails. This leaves significant gaps for an agent to understand 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, efficient sentence that front-loads key information (action, resource, date scope, ranking) with no wasted words. It is appropriately sized for the tool's complexity and structured for quick comprehension.

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

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

Given the lack of annotations and output schema, the description is incomplete. It does not explain the return format (e.g., structure of quotes, what 'top' means), error cases, or behavioral constraints like rate limits. For a tool with no structured support, more detail is needed to guide an agent effectively.

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

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

The input schema has 100% description coverage, documenting the optional 'date' parameter with format and default behavior. The description adds minimal value beyond this, as it only reiterates the date context without providing additional semantics or clarifying the 'relevance score' ranking. Baseline 3 is appropriate given the 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 ('top featured quotes from crypto podcasts'), and scope ('for a given date, ranked by relevance score'), making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'get_ticker_featured_quotes' or 'get_episode_quotes', which might offer similar functionality, preventing 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, such as 'get_ticker_featured_quotes' or 'get_episode_quotes', nor does it mention prerequisites or exclusions. It implies usage for a specific date but lacks context about optimal scenarios or limitations.

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 output includes signal details (type, horizon, etc.) but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or how data is returned (e.g., pagination). The disclaimer '(Not financial advice.)' adds minor context 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 front-loaded with the core purpose in the first sentence, followed by details on signal components and a disclaimer. It's efficient with two sentences, though the disclaimer could be integrated more seamlessly. No wasted words, but minor structural improvements are possible.

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 outlining output fields (signal type, horizon, etc.), but lacks details on return format, error handling, or behavioral constraints. For a tool with 1 parameter and high schema coverage, it's adequate but has clear gaps in operational context.

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

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

Schema description coverage is 100% for the single parameter 'date', which is fully documented in the schema. The description adds no additional parameter semantics beyond implying date filtering, so it meets the baseline of 3 where 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 verb 'Get' and the resource 'scenario strategic signals across all assets for a given date', specifying what the tool does. It distinguishes from siblings like get_ticker_signals by focusing on 'all assets' rather than a specific ticker, but doesn't explicitly contrast with other market-related tools like get_market_snapshot or get_market_themes.

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. It doesn't mention when to prefer it over get_ticker_signals (for specific assets) or other market tools, nor does it specify prerequisites or exclusions. The description only states what it does, not when it's appropriate.

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

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

With no annotations, the description carries the full burden. It describes the return content but lacks behavioral details such as data freshness, rate limits, authentication needs, or error handling for invalid dates. It mentions the tool returns a 'snapshot' but doesn't clarify if it's cached or real-time.

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

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

The description is front-loaded with the core purpose but becomes overly detailed by listing all return fields in a single run-on sentence. This reduces readability, though all information is relevant to the tool's function.

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

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

Given no annotations and no output schema, the description compensates by detailing return values. However, it lacks context on behavioral aspects like performance or constraints. For a tool with one parameter and high schema coverage, it's adequate but not comprehensive.

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

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

The schema description coverage is 100%, so the baseline is 3. The description adds value by specifying that omitting the date returns the 'latest' snapshot, which clarifies the optional parameter's default behavior beyond the schema's 'Omit for latest' note.

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') and resource ('overall crypto market daily snapshot'), and distinguishes it from siblings by specifying it returns a 'daily narrative insight report' with detailed metrics, unlike other tools focused on episodes, podcasts, or tickers.

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_market_signals' or 'get_ticker_snapshot'. The description lists outputs but does not specify use cases, 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 full burden. It mentions what data is returned (themes with metrics like fragility and novelty scores) but lacks behavioral details such as data freshness, source limitations, rate limits, authentication needs, or error handling. This is a significant gap for a tool with no 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, dense sentence that efficiently conveys purpose, scope, and output structure without any wasted words. It is front-loaded with the core action and resource, making it highly scannable and effective.

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

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

Given the tool's moderate complexity (thematic analysis with metrics), lack of annotations, and no output schema, the description is partially complete. It covers the output components well but misses behavioral context and deeper usage guidance, leaving gaps for an AI agent to infer operational details.

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% for the single optional parameter 'date', documenting its format and default behavior. The description adds value by contextualizing the parameter's purpose ('on a given date') and implying the tool's temporal focus, compensating beyond the schema's technical details.

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 detailed output components listed. It distinguishes from sibling tools like get_episode_details or get_market_signals by focusing on thematic analysis rather than episode-level or signal 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 implies usage for analyzing crypto podcast themes on a specific date, but provides no explicit guidance on when to use this tool versus alternatives like get_market_signals or get_market_snapshot. No exclusions or prerequisites are mentioned.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read operation ('Get') but does not specify whether it requires authentication (though 'authenticated user' hints at this), rate limits, pagination, error handling, or the format of the returned list. For a 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to understand quickly, though it could be slightly more structured to include usage hints.

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 does not explain the return format (e.g., list structure, data fields), authentication needs, or error conditions. For a tool that likely returns user-specific data, more context is needed to ensure proper usage by 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 tool has 0 parameters, and schema description coverage is 100%, so there is no need for parameter details in the description. The description does not add parameter semantics, but this is acceptable given the lack of parameters, warranting a baseline score of 4 as it adequately addresses the absence of inputs.

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 crypto assets the authenticated user has favorited'), specifying the scope (favorited by the authenticated user) and domain (AudioAlpha). However, it does not explicitly differentiate from its sibling 'get_my_favorite_podcasts', which is similar but for podcasts instead of crypto assets, leaving room for potential confusion.

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, such as 'get_my_favorite_podcasts' for podcasts or other market-related tools. It lacks context on prerequisites (e.g., authentication requirements) or exclusions, offering only a basic statement of purpose without usage instructions.

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', implying an auth requirement, but doesn't disclose other behavioral traits such as rate limits, pagination, error handling, or what the return format looks like (e.g., list structure). This is a significant gap 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 any fluff or unnecessary details. It's front-loaded and wastes no words, 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 adequate but has clear gaps. It lacks details on behavioral aspects like authentication specifics, return format, or error cases, which are important for a tool with no annotations. It meets the minimum viable standard but doesn't fully compensate for the missing 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 input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately doesn't discuss parameters, and the baseline for this scenario is 4, as it avoids redundancy while being complete for a parameterless tool.

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. However, it doesn't explicitly differentiate from sibling tools like 'get_my_favorite_assets' or 'get_my_feed', which might also involve user-specific data, leaving room for ambiguity.

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), exclusions, or comparisons to siblings like 'get_my_favorite_assets' or 'search_podcasts', leaving the agent to infer usage context.

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

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

With no annotations, the description carries full burden but provides minimal behavioral context. It mentions the return structure but not operational aspects like authentication needs, rate limits, or whether it's a read-only operation (implied by 'Get' but not explicit). It lacks details on error conditions or performance characteristics.

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

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

The description is front-loaded with the core purpose, followed by detailed return structure. It's appropriately sized for a tool with rich output, though the list of return fields is lengthy but necessary for clarity. Every sentence contributes to understanding the tool's function.

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

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

Given no annotations and no output schema, the description compensates by detailing the return structure extensively. However, it lacks context on authentication, error handling, or data freshness. For a personalized tool with complex output, more behavioral context 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 adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. It doesn't explain how 'episodes_lookback' affects the feed composition.

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 'personalized feed', specifying it's based on 'user's favorite assets and followed podcasts'. It distinguishes from siblings like get_my_favorite_assets or get_my_favorite_podcasts by combining these into a feed. However, it doesn't explicitly contrast with get_market_snapshot or other market 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?

No guidance on when to use this tool versus alternatives is provided. The description implies usage for personalized content but doesn't specify prerequisites (e.g., requires user authentication or having favorites/follows), nor does it compare to siblings like get_market_snapshot for non-personalized 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets' episodes, implying a read-only operation, but doesn't mention any constraints like rate limits, authentication requirements, or what 'recent' means (e.g., time frame, ordering). This leaves significant gaps for a tool with potential behavioral nuances.

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 includes a useful contextual note about the podcast ID source. Every part of the sentence 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?

For a simple read operation with 2 parameters and no output schema, the description is minimally adequate. It covers the basic purpose and parameter context, but lacks behavioral details (e.g., rate limits, ordering) and doesn't explain return values. With no annotations, this leaves the agent with incomplete information about how the tool behaves.

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 adds no additional parameter semantics beyond what's in the schema, such as explaining podcast ID formats or 'recent' criteria. The baseline of 3 is appropriate when the schema does all the work.

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 differentiate this tool from sibling tools like 'get_podcast_latest' or 'get_podcast_latest_full', which likely 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 no guidance on when to use this tool versus alternatives like 'get_podcast_latest' or 'search_podcasts'. It mentions the podcast ID comes from 'search_podcasts', which is helpful context but doesn't constitute explicit usage guidelines 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. It mentions the data returned but does not disclose behavioral traits such as error handling, rate limits, authentication needs, or whether it's a read-only operation. This is a significant gap for a tool with no 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, dense sentence that efficiently lists all key information—action, resource, and returned data—with zero wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

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

Given the tool's low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate but lacks output details (no output schema) and behavioral context. It covers the purpose and data returned but does not fully compensate for the absence of annotations, making it minimally viable.

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 parameter semantics beyond what the schema provides, so it meets the baseline score of 3 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 action ('Get the latest episode') and the specific resource ('for a specific podcast'), distinguishing it from sibling tools like get_podcast_episodes (which retrieves multiple episodes) and get_episode_details (which requires an episode_id). It also specifies the data returned, including unique elements like α-sentiment for crypto markets.

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

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

The description implies usage when needing the most recent episode of a podcast, with the parameter podcast_id sourced from search_podcasts. However, it does not explicitly state when not to use it or name alternatives like get_podcast_latest_full, leaving some ambiguity for the agent.

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 'full details including quotes and asset mentions', which adds some behavioral context about return content. However, it lacks critical details like whether this is a read-only operation, error handling, rate limits, or authentication needs, leaving significant gaps for a tool with no annotations.

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

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

The description is a single, efficient sentence that front-loads the core purpose ('Get the latest episode for a podcast') and adds specific detail ('with full details including quotes and asset mentions'). There is no wasted wording, 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 no annotations, no output schema, and a simple input schema, the description provides basic purpose and return content hints. However, it lacks details on behavioral traits, error cases, or output structure, making it minimally adequate but incomplete for informed tool selection and 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%, with the parameter 'podcast_id' documented in the schema as 'Podcast ID from search_podcasts'. The description doesn't add any extra meaning beyond this, such as format examples or constraints, so it meets the baseline of 3 for high schema coverage without compensating 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 action ('Get') and resource ('latest episode for a podcast'), specifying it returns 'full details including quotes and asset mentions'. It distinguishes from siblings like 'get_podcast_latest' (implied less detail) and 'get_episode_full' (requires episode ID vs. podcast ID), but 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 explicit guidance on when to use this tool versus alternatives like 'get_podcast_latest' or 'get_episode_full'. The description implies it's for the latest episode with full details, but doesn't specify prerequisites, exclusions, or comparative contexts with 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 of behavioral disclosure. It mentions the output includes α-sentiment scores and ranking, but fails to detail critical aspects like rate limits, authentication needs, data freshness, or error handling. This leaves significant gaps for an agent to understand operational constraints.

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, output features (α-sentiment scores, ranking), and key parameters without redundancy. It is front-loaded with essential information and wastes no words, 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 no annotations and no output schema, the description adequately covers the basic purpose and output features but lacks details on behavioral traits, error cases, or data formats. For a tool with 2 parameters and 100% schema coverage, it is minimally viable but incomplete in providing full operational context for an agent.

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

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

Schema description coverage is 100%, so the schema fully documents the parameters (date and ticker). The description adds no additional semantic context beyond what the schema provides, such as examples or constraints, but does imply the tool's focus on crypto assets and sentiment scoring, which aligns with the schema. Baseline 3 is appropriate as the schema handles parameter documentation.

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

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

The description clearly states the verb 'Get' and resource 'featured podcast quotes for a specific crypto asset', specifying the inclusion of α-sentiment scores and ranking by selection score. However, it does not explicitly differentiate from sibling tools like 'get_episode_quotes' or 'get_market_featured_quotes', which might offer similar functionality, 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?

The description provides no guidance on when to use this tool versus alternatives, such as 'get_episode_quotes' or 'get_market_featured_quotes', nor does it mention prerequisites or exclusions. Usage is implied through the tool name and parameters but lacks explicit context for selection among siblings.

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

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

No annotations are provided, so the description carries the full burden. It describes the tool's behavior regarding output format based on the category parameter, which is valuable. However, it doesn't disclose other behavioral traits such as rate limits, authentication needs, error conditions, or whether it's a read-only operation (though 'Get' implies read-only). The description adds some context but leaves gaps in behavioral 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 two sentences with zero waste. It front-loads the core purpose and efficiently explains the conditional behavior based on the category parameter. Every word contributes to understanding the tool's functionality, 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 (3 parameters, no output schema, no annotations), the description is mostly complete. It covers the purpose, usage guidelines, and parameter semantics effectively. However, it lacks details on output structure (e.g., what fields are in the highlights object or LeaderboardEntry) and behavioral traits like error handling. Since there's no output schema, the description could be more complete by hinting at the return format, but it's still strong for the context.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds meaningful semantics by explaining the effect of the 'category' parameter on the return type (highlights object vs. array of LeaderboardEntry) and mentions the default behavior when 'category' is omitted. This goes beyond the schema's parameter descriptions, providing valuable context for parameter usage.

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

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

The description clearly states the verb ('Get') and resource ('ticker leaderboard') with specific scope ('for a given date'). It distinguishes this tool from all sibling tools (which focus on episodes, markets, podcasts, etc.) by specifying it's about ticker leaderboards. The description provides concrete details about what the tool returns, making its purpose unambiguous.

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

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

The description explicitly states when to use this tool vs. alternatives: 'Without category param, returns highlights object... With category param..., returns array of LeaderboardEntry.' It provides clear guidance on parameter usage and the resulting behavior, which helps the agent decide how to invoke the tool based on the desired output format.

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 lists return fields (e.g., signal type, confidence score) but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or how data is sourced. The disclaimer adds caution but doesn't clarify operational behavior, leaving significant gaps for a tool that might involve financial data.

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, stating the core purpose in the first sentence. The second sentence lists return fields efficiently, and the disclaimer is brief. However, the list of return fields could be more structured (e.g., grouped), and some redundancy exists (e.g., 'scenario signals' implies details like description).

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 listing return fields, but it's incomplete. It doesn't explain the format or meaning of values (e.g., what 'confidence score' ranges are), potential errors, or how 'date' affects results. For a tool with financial implications and two parameters, more context is needed to ensure safe and effective use by an agent.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('ticker' as asset symbol, 'date' as optional YYYY-MM-DD). The description adds no parameter-specific information beyond implying 'ticker' is for a crypto asset, which is redundant with the schema. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with additional context like date range implications.

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 distinguishes it from siblings like 'get_market_signals' by focusing on a single asset. However, it doesn't explicitly differentiate from 'get_ticker_snapshot' or 'get_ticker_featured_quotes' in the same ticker-focused group.

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. The description mentions it returns scenario signals for a crypto asset, but doesn't specify scenarios like 'bullish' or 'bearish,' nor does it compare to siblings like 'get_market_signals' (for broader market) or 'get_ticker_snapshot' (for general data). The disclaimer '(Not financial advice.)' is a legal note, 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 provided, the description carries the full burden of behavioral disclosure. It lists return metrics but fails to describe operational traits such as rate limits, authentication needs, error handling, or data freshness. For a tool with complex outputs and no annotation coverage, this leaves significant gaps in understanding its 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 front-loaded with the core purpose and efficiently lists return metrics in a single sentence. However, the long list of metrics could be slightly overwhelming, and it lacks structural elements like bullet points for better readability, though it remains concise overall.

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

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

Given the tool's complexity (multiple output metrics) and lack of annotations or output schema, the description is incomplete. It specifies what is returned but not the format, units, or interpretation of values like 'α-index' or 'consensus score'. For a tool with rich outputs and no structured documentation, more contextual detail is needed.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('ticker' and 'date'). The description adds no parameter-specific details beyond what the schema provides, such as examples for 'date' or clarifications on 'ticker' format. Baseline 3 is appropriate when the schema handles parameter documentation.

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

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

The description clearly states the tool's purpose with specific verbs ('Get detailed daily insights snapshot') and resource ('for a specific crypto asset'). It distinguishes from sibling tools by focusing on daily asset-level insights rather than episodes, markets, podcasts, or user preferences, making its scope immediately apparent.

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 daily crypto asset insights but provides no explicit guidance on when to use this tool versus alternatives like 'get_market_snapshot' or 'get_ticker_signals'. It lacks any mention of prerequisites, exclusions, or comparative contexts 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 the full burden of behavioral disclosure. It states the tool returns specific fields (podcast ID, name, etc.), which adds some context about output. However, it doesn't cover critical behaviors like whether this is a read-only operation, potential rate limits, error conditions, or pagination for large result sets. For a search tool with zero 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 highly concise and front-loaded, consisting of two sentences that directly state the action and return values without any fluff. Every sentence earns its place by providing essential information efficiently, 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 moderate complexity (a search function with one parameter) and no annotations or output schema, the description is minimally adequate. It covers the purpose and return fields but lacks details on behavioral traits, usage guidelines, and error handling. Without an output schema, it partially compensates by listing return values, but gaps remain 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 parameter 'q' documented as 'Podcast name search query e.g. unchained, bankless, chopping block.' The description adds no additional parameter semantics beyond what the schema provides, such as search syntax or matching rules. 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 could also retrieve podcast information, so it misses full sibling distinction.

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, exclusions, or compare it to sibling tools such as 'get_my_favorite_podcasts' for user-specific data or 'get_podcast_episodes' for episode listings. This lack of context leaves the agent to infer usage based on the name alone.

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