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. It discloses the basic purpose but does not mention behavioral traits such as rate limits, authentication needs, or side effects. The description adequately explains what it does but lacks depth.

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 conveys the core purpose efficiently. It is concise and front-loaded, although it could include slightly more detail without becoming verbose.

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

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

Without an output schema, the description should hint at the response structure. It mentions ranking by relevance score but does not describe other fields or return format. For a tool with four parameters and no output schema, the description is incomplete.

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 covers all four parameters (100% coverage). The description does not add significant meaning beyond the schema; it simply reiterates the tool's purpose. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool retrieves top featured quotes from crypto podcasts for a given date, ranked by relevance. It uses specific verbs and resources, and differs from sibling tools like get_episode_quotes or get_ticker_featured_quotes.

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 (fetching featured quotes for a date) but does not provide explicit guidance on when to use this versus alternatives, 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?

The description discloses critical behavioral traits: tier caps on days with specific values per tier, the `date` parameter behavior (only honored for enterprise, silently ignored otherwise), and that requests above the cap are silently capped. It also explains the `version_info` parameter behavior and the `snapshot_type` limitation (only 'crypto' available currently). No annotations were provided, so the description fully handles 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 well-structured and front-loaded with purpose, but contains four paragraphs. While each sentence adds value, it is slightly verbose. Still, it is efficient and avoids fluff.

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

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

Given no output schema, the description adequately describes the return format (array of daily market snapshots with snapshot_date and standard MarketSnapshot fields, plus metadata fields). It covers parameter behavior and limitations. No gaps are evident for the tool's purpose.

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

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

Schema coverage is 100%, but the description adds significant extra context: for `days`, it details tier caps and silent capping; for `date`, it clarifies tier-dependent honoring; for `version_info`, it explains which fields are included; for `snapshot_type`, it notes future reservation. This adds meaning beyond the schema descriptions.

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

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

The description clearly states the tool fetches a historical time series of daily market-level snapshots, overall market sentiment, not a single ticker. It distinguishes itself from get_ticker_history by explicitly stating that for a single ticker's history, that sibling tool should be used.

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 explicit when-to-use scenarios (e.g., overall market mood/regime trend, charting α-sentiment, computing averages) and when not (for a single ticker, use get_ticker_history). It offers a clear alternative.

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 must carry the full burden of behavioral disclosure. It describes the output content but does not mention whether the operation is read-only, authentication requirements, rate limits, or any side effects. The disclaimer '(Not financial advice)' is not a behavioral trait.

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 only two sentences, front-loaded with the core purpose. Every sentence adds value, and there is no extraneous information. It is appropriately sized for a tool with no output schema and moderate parameter count.

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 4 parameters (all optional, with enums and defaults) and no output schema, the description covers the output fields but does not explain parameter interactions or behavior like version_number fallback. It is minimally complete but lacks depth for a moderately complex tool.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds minimal extra meaning beyond the schema, only stating the date parameter's purpose. It does not provide usage examples or additional context for the other 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 verb 'Get', the resource 'scenario strategic signals across all assets', and the scope 'for a given date'. It lists the fields included in each signal, distinguishing it from sibling tools like get_market_snapshot (which returns different data) or get_ticker_signals (which is per-asset).

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

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

The description implies usage for obtaining strategic signals across all assets on a specific date, but it does not explicitly state when to use this tool vs. alternatives such as get_market_snapshot or get_ticker_signals. No exclusions or conditions are provided, so the guidance is only implicit.

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 must carry the full burden of behavioral disclosure. It does not mention any side effects, rate limits, data freshness guarantees, authentication requirements, or whether the operation is read-only. The description only lists return fields, failing to disclose 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 sentence followed by a long, comma-separated list of return fields. While the purpose is front-loaded, the list is dense and could be more structured (e.g., bullet points) for readability. It is moderately concise but could be improved.

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 4 well-documented parameters and no output schema, the description compensates by listing many return fields (headline, scores, narratives, etc.). This helps an agent understand what to expect. However, it lacks explanations for interpreting some scores (e.g., α-sentiment z-scores), but overall it is fairly 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 no additional meaning beyond what the schema provides for parameters like date, version_info, snapshot_type, and version_number. It does not clarify how these parameters affect the output in more practical terms.

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 'Get the overall crypto market daily snapshot (daily narrative insight report) for a given date.' It specifies the resource (daily snapshot) and action (get), and lists the specific data fields returned, distinguishing it from siblings like get_market_signals or get_market_episodes which focus on different aspects.

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 the many sibling tools (e.g., get_market_signals, get_market_episodes). It does not mention prerequisites, context, or alternatives, leaving the agent to infer usage solely from the output description.

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 does not mention safety (read-only vs destructive), authentication needs, rate limits, or side effects. It only describes the output fields, missing critical behavioral context for agent 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?

Two concise sentences, no wasted words. Front-loads the core purpose and immediately follows with a clear list of output fields. Every 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 retrieval tool with 4 parameters and no output schema, the description covers what the tool returns (fields of each theme) but lacks behavioral context, error cases, or usage constraints. 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?

Schema description coverage is 100%, so the input schema already documents all parameters adequately. The description does not add meaning beyond the schema; it only references the date implicitly. 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 uses a specific verb ('Get') and clearly identifies the resource ('narrative themes dominating the crypto podcast space on a given date'). It lists the fields each theme includes, distinguishing it from sibling tools like get_market_signals or get_market_snapshot that likely return different 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 prerequisites, when not to use it, or how it relates to sibling tools such as get_market_signals or get_market_snapshot.

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

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

No annotations provided, so description carries full burden. It discloses that the tool always returns the latest authoritative snapshot per asset ('is_latest') and implies authentication is required ('authenticated user'). This adds useful behavioral context beyond the schema.

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

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

The description is two sentences, both essential. The first states the primary purpose, the second adds a key behavioral promise. No extraneous 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?

For a simple list-retrieval tool with only optional parameters, the description is mostly complete. It lacks mention of pagination or rate limits, but given no output schema, the core functionality is adequately covered.

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 no additional meaning beyond what the schema already provides for the two parameters. It does not explain usage patterns or constraints not evident from the schema.

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

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

The description clearly states the verb 'Get', the resource 'list of crypto assets the authenticated user has favorited', and the platform 'AudioAlpha'. It differentiates from sibling tools like get_my_favorite_podcasts by specifying the asset type.

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

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

There is no guidance on when to use this tool versus alternatives, nor any exclusion criteria. The description only states what it does, not when to choose it over other tools like search or market snapshot functions.

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?

Without annotations, the description provides some behavioral insight (always uses latest snapshot, returns specific structure) but omits important traits such as auth requirements, whether the tool is read-only, or what happens if no favorites are set. It does not discuss side effects or rate limits.

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

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

The description is concise with two sentences. The first sentence states purpose, the second details return structure. Could be improved by separating return types into a list for clarity, but overall no wasted words.

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

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

The description explains the return format (snapshot_date, assets array with nested fields) but fails to mention the 'status' field which is always returned per schema, and does not clarify optional version_info behavior. Given no output schema, more detail is needed for full completeness.

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

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

Schema description coverage is 100%, and the description does not add meaning beyond the schema. The description's mention of 'latest authoritative snapshot' is not parameter-specific. According to rules, baseline 3 applies, and no extra value is 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 'Get a personalized feed' with specific verb and resource, and differentiates from sibling tools (e.g., get_market_snapshot) by emphasizing personalization based on user's favorites and followed podcasts.

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

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

The description mentions 'Always uses the latest authoritative snapshot (is_latest)' which implies usage for latest data, but lacks explicit when-not-to-use or alternatives for historical data (e.g., get_market_history). No guidance on prerequisites like having a user profile with favorites.

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?

No annotations are provided, so the description carries full burden. It discloses that results are ranked by selection score and include sentiment scores, but does not mention if the tool is read-only, any side effects, rate limits, or what happens on missing data (e.g., empty response). The parameter description for ticker warns about bare symbols, but that is not in the main 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?

A single sentence that efficiently conveys the core purpose and outputs. No wasted words; front-loaded with verb and resource.

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 5-parameter tool with no output schema, the description covers the main purpose and key outputs (sentiment scores, ranking). It does not explain 'selection score' or version parameters, but those are in the schema. Adequate for a query tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds no parameter details beyond what the schema already provides. It mentions 'specific crypto asset' and 'given date' which correspond to ticker and date, but does not explain version_info or snapshot_type.

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

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

Description clearly states the verb 'Get', the resource 'featured podcast quotes', the scope 'for a specific crypto asset on a given date', and additional features 'α-sentiment scores (0-10 scale), ranked by selection score'. This distinguishes it from siblings like get_market_featured_quotes which is market-level.

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 quotes for a specific ticker and date, but does not explicitly state when to use versus alternatives like get_ticker_snapshot or get_market_featured_quotes. No when-not or alternative guidance is provided.

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

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

No annotations provided, so description carries full burden. It discloses tier-dependent caps, that the date parameter is ignored for non-enterprise tiers, and the exact return fields including version_info 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?

Well-structured with purpose first, then usage guidelines, parameter details, and return format. Slightly verbose but each sentence adds value; could be slightly tightened.

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?

All 5 parameters are thoroughly explained, return format described (array of snapshots with fields and tier info), and no omissions given the tool's complexity.

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

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

Schema coverage is 100% but description adds critical context: required '-USD' suffix, tier caps and capping behavior, date parameter honored only for enterprise, version_info toggle details, and snapshot_type default.

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 fetches a historical time series of daily snapshots for one crypto ticker. It distinguishes from siblings by focusing on time-series history, specifying the required '-USD' suffix, and outlining return structure.

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?

Explicit use cases are given: when user asks about trends, plotting, or computing statistics. Tier caps and parameter behavior are explained, though no explicit when-not-to-use or alternatives to sibling tools 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 transparently describes the different return structures based on the category parameter, the behavior of snapshot_type (with a note on future availability), and version_info. It does not mention side effects or destructive actions, which is appropriate for a read-only tool.

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

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

The description is a single paragraph that efficiently conveys the main purpose and key details. However, it could be more structured (e.g., using bullet points or separate sentences for each mode) to improve readability for an agent. It is not overly verbose, but lacks optimal formatting.

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

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

Given that there is no output schema, the description adequately explains the return values for both usage modes. It covers the highlights object and the LeaderboardEntry array. With 5 parameters and no output schema, the description is fairly complete in providing necessary context for correct 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?

The input schema has 100% description coverage for parameters, but the description adds value beyond the schema. It explains the overall behavior when category is omitted or provided, and provides more detail on the version_info parameter's effect than the schema does. This helps the agent understand the return format better.

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 gets a ticker leaderboard for a given date and distinguishes two modes: with and without the category parameter. It lists the specific fields in the highlights object and the array returned for categorized queries, differentiating it from sibling tools like get_ticker_snapshot or get_ticker_history.

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 implicitly guides usage by explaining the effect of omitting or providing the category parameter. It does not explicitly state when not to use this tool or compare it to alternatives, but the context from sibling names and the detailed description make the usage clear.

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

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

No annotations are provided, so the description bears full responsibility. It does not disclose behavioral traits such as destructive potential, authorization requirements, rate limits, or side effects. The only behavioral note is the 'Not financial advice' disclaimer.

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

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

The description is concise, with two sentences that directly state the tool's purpose and output. No unnecessary words; information is front-loaded.

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

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

Given no output schema, the description provides a useful list of return fields. However, the tool has 5 parameters and many siblings; more context about when to use this tool relative to other ticker tools 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?

The input schema has 100% description coverage, so the schema already documents all parameters sufficiently. The tool description adds no additional meaning to the parameters beyond the schema, thus meeting the baseline but not exceeding it.

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

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

The description clearly states the tool retrieves scenario signals for a specific crypto asset and lists the return fields, distinguishing it from sibling tools like get_ticker_history or get_ticker_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 does not explicitly guide when to use this tool over alternatives. While the purpose is clear, there is no 'use this when' or 'consider X instead' advice, leaving the agent to infer from 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 provided, the description carries full responsibility. It lists many return fields, mentions behavior for invalid tickers (empty/404), but does not disclose side effects (none expected), permission requirements, rate limits, or whether the snapshot is computed synchronously. It provides moderate transparency but could be more thorough.

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

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

The description is a single dense paragraph listing many metrics. It is not overly long but could be better structured (e.g., bullet points for clarity). Every sentence provides value, but the density may reduce readability for an AI agent. It is adequate but not optimal.

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 (5 parameters, no output schema), the description covers the key information: purpose, available metrics, and basic behavior (invalid ticker). However, it lacks details on the response format, pagination (if any), and error handling beyond 404. For a snapshot tool with many metrics, additional context on return structure 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 coverage is 100%, so baseline is 3. The description adds some semantic context by listing all returned metrics, but it does not elaborate on parameter behavior beyond what's in the schema. For example, it doesn't explain the version_info or version_number in detail. The parameter descriptions in the schema are already quite comprehensive, so description adds limited extra value.

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

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

The description uses specific verbs ('Get') and clearly identifies the resource ('daily insights snapshot for a specific crypto asset'). It lists many metrics (α-index, α-pulse, etc.), distinguishing it from sibling tools like get_market_snapshot (overall market) and get_ticker_history (historical data). The purpose is unambiguous and precise.

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

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

The description implies usage for obtaining a detailed daily snapshot of a crypto asset. However, it provides no guidance on when not to use this tool or how it compares to alternatives (e.g., get_market_snapshot for market-level data, get_ticker_signals for signal-specific data). Lack of explicit when-to-use vs. when-not-to-use limits agent 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 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.