AudioAlpha

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

No annotations are provided, so the description carries full responsibility for disclosing behavior. It does not mention that this is a read-only operation, whether authentication is required, or how errors (e.g., invalid episode_id) are handled. It only lists returned fields, leaving behavioral traits undisclosed.

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, concise and front-loaded, listing the key output fields without any filler. Every word contributes to the tool's purpose, making it highly efficient.

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

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

The tool is simple with one parameter, but without an output schema, the description should clarify the response structure. While it lists fields, it does not explain what 'podcast info' includes or how this differs from get_episode_full. The description is adequate but leaves some ambiguity about the full response and relationship to siblings.

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% for the single parameter episode_id, described simply as 'Episode ID' in the schema. The description adds minimal meaning by referring to 'a specific episode,' but does not elaborate on format, source, or validation rules. Baseline 3 applies due to full schema coverage.

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

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

The description clearly states the tool retrieves metadata for a specific episode and enumerates the specific fields returned (title, publish date, duration, sentiment scores, podcast info). This distinguishes it from siblings like get_episode_summary or get_episode_transcript, which likely handle other content types.

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

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

The description implies usage for metadata retrieval but does not explicitly state when to use this over alternatives like get_episode_full or get_episode_summary. There is no mention of exclusions or prerequisites, so the guidance is inferred from the name and field list rather than stated.

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

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

There are no annotations, so the description carries the burden. It discloses the composite nature of the data ('details, summary, quotes and asset mentions') and the efficiency trait ('in one call'), but does not mention what happens on errors, authentication needs, rate limits, or any other behavioral aspects. The description adds value beyond the name but remains limited.

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 one concise sentence, front-loaded with the core action and outcome ('Get full episode data in one call'), followed by a relevant list of contents. Every word earns its place.

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

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

For a single-parameter, no-output-schema tool, the description provides sufficient context: it names the data categories returned without over-explaining. It could be more explicit about the return structure, but given the simplicity and sibling context, it is adequately 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 coverage is 100% with a single parameter, so the schema fully documents 'episode_id'. The description does not add any extra meaning about the parameter, keeping it at the baseline level for high coverage.

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

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

The description uses a specific verb ('Get') and resource ('full episode data'), explicitly listing what's included (details, summary, quotes, asset mentions). This distinguishes it from sibling tools like get_episode_details, get_episode_quotes, and get_episode_summary, which fetch individual components.

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

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

The phrase 'in one call' implies an aggregating use case, but there is no explicit guidance on when to choose this over the granular siblings or whether to use it instead of multiple calls. Context is clear but exclusions/alternatives are not stated.

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

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

No annotations are present, so the description is the sole source of behavioral information. It correctly implies a read-only operation by saying 'Get all quotes', but it does not disclose potential edge cases (e.g., empty results, quote text inclusion, or any 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 a single, focused sentence that front-loads the action and resource. No redundant or irrelevant information is included.

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

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

For a tool with one parameter and no output schema, the description adequately covers the primary return fields (speaker, sentiment score, ticker). It does not mention the response structure or whether the quote text itself is included, but the tool's simplicity likely makes this sufficient.

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% for the single parameter episode_id, which is described as 'Episode ID'. The description doesn't add further parameter details, but with full schema coverage, the baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Get') and clearly identifies the resource ('all quotes extracted from a specific episode') while enumerating key fields (speaker, sentiment score, ticker). This distinguishes it from sibling tools like get_episode_transcript or get_episode_summary.

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 choose this tool over alternatives is provided. However, the description implies its use for retrieving episode quotes, and the sibling tool names (e.g., get_episode_transcript) suggest distinct purposes, but the description itself doesn't state exclusions or alternatives.

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

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

With no annotations provided, the description carries the burden of disclosing behavior. It specifies that the output includes a transcript summary, assets, and sentiment scale, which helps set expectations. However, it does not mention error handling, authentication, or any side effects, though as a GET-like read operation the need for such disclosure is lower.

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, front-loaded sentence that conveys the action, target resource, and key output details without any wasted words. It earns its place by including the sentiment scale, which is essential for understanding the tool's unique value.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is complete enough. It tells the agent exactly what the tool returns and the scale for sentiment, covering all essential aspects. No additional behavioral or return information is needed for the tool to be used correctly.

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 already covers the sole parameter episode_id with 100% coverage, so the description adds minimal new meaning. It merely restates the concept of 'a specific episode' in the description, which does not enhance the parameter's semantics. The baseline of 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 uses a specific verb 'Get' and resource 'transcript summary for a specific episode', clearly distinguishing it from sibling tools like get_episode_transcript or get_episode_details. It also specifies the unique output (assets and α-sentiment on a 0-10 scale), leaving no ambiguity about its function.

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 concise summary with sentiment, but it does not explicitly state when to use this over alternatives such as get_episode_full or get_episode_quotes. There is no direct comparison or exclusion guidance, though the summary-oriented wording serves as a subtle hint.

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 burden of disclosure. It adds valuable context about the output (speaker diarization and speaker name mapping) and access restrictions (plans). It doesn't mention error handling or data limits, but for a read-only transcript tool, it gives sufficient 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, front-loaded with the action, and every word adds value. No fluff or repetition.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers the essential aspects: what is returned (full transcript with diarization) and access restrictions. It might benefit from specifying return format, but it's not necessary for basic comprehension.

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% coverage: 'episode_id' is described as 'Episode ID'. The description adds no further parameter semantics, so the baseline of 3 applies.

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 the full transcript for a specific episode' with specific details about speaker diarization and name mapping. This distinguishes it from sibling tools that handle summaries, details, or 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 provides a clear context by mentioning the plan restriction ('Pro and Enterprise plans only'), which is a usage constraint. However, it doesn't explicitly compare to alternatives or state when to prefer this over sibling tools like get_episode_summary.

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 does reveal the sentiment score scale (0-10) and the included fields, but it omits details about response ordering, pagination, or any limitations, which are important for a list-returning endpoint.

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, front-loaded sentence that immediately communicates the core purpose and output. Every word contributes value, with no filler or redundant phrasing.

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

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

For a tool with one optional parameter and no output schema, the description sufficiently covers inputs and outputs (episode name, sentiment score, summary). However, given the large sibling set and absence of annotations, a brief contrast with similar tools would make it fully complete.

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

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

The input schema already provides 100% description coverage for the single 'date' parameter, including format and 'omit for latest' guidance. The tool description adds no additional parameter semantics beyond what the schema already states.

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: retrieving all crypto podcast episodes published on a given date, and explicitly lists the returned fields (podcast name, sentiment score on a 0-10 scale, transcript summary). This distinguishes it from siblings like get_podcast_episodes and get_episode_details by focusing on market-wide episodes.

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

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

The description provides clear usage context by specifying the date parameter and noting that omitting it returns the latest. However, it does not explicitly mention when to use this tool over alternatives or state any exclusions, so it stops short of full alternative 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 correctly states the core behavior (a GET returning ranked quotes) and implies a read-only operation via 'Get', but it omits details like response structure, potential errors, or any caveats around the versioning parameters—though those are partly covered by the input 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 a single, well-structured sentence. It leads with the verb, states the object, the qualifier, and the ranking criterion—no wasted words. This is an ideal length for a simple GET tool.

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 this is a simple read operation with a comprehensive schema (4 optional params, fully documented), the description sufficiently conveys what the tool returns. The absence of an output schema is partially mitigated by the description's mention of 'featured quotes' and 'ranked by relevance score'. It could add a little more about the response envelope, but the tool is straightforward.

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 no extra meaning for the parameters (date, version_info, snapshot_type, version_number) beyond what the schema already provides. The schema is detailed and self-sufficient.

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 is very specific: it names the resource ('featured quotes from crypto podcasts'), the parameter ('for a given date'), and the ranking ('ranked by relevance score'). This clearly distinguishes it from sibling tools like get_episode_quotes (per-episode) and get_ticker_featured_quotes (per-ticker).

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?

Usage is implied: you would use this tool when you need market-level featured quotes for a specific date. However, it does not explicitly state when to choose this over alternatives, nor does it provide the 'when-not-to-use' guidance. The agent must infer from the name and sibling list.

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 takes on full responsibility for disclosing behavior. It reveals important quirks: the `date` parameter is silently ignored for non-enterprise tiers, `days` is tier-capped, and the return order is oldest-first with extra metadata fields. It does not explicitly state 'read-only', but the verb 'Fetch' implies it, and the disclosed tier caps act as rate-limit context. Minor omissions (e.g., no explicit side-effect statement) keep it from a 5.

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 organized into four clear paragraphs, front-loaded with purpose and usage. It is information-dense but not bloated; each sentence carries useful context. Minor redundancy with schema details (e.g., tier caps) is acceptable given their importance. Could be slightly tighter, but overall 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?

Since there is no output schema, the description compensates by specifying the return format: 'array of daily market snapshots (oldest first), each with snapshot_date plus all standard MarketSnapshot fields' and additional response metadata. It also covers edge-case behavior (tier caps, date ignored) thoroughly, making the tool's behavior fully understandable 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 coverage is 100%, so the baseline is 3. The description adds meaningful nuance beyond the schema: it explains that `date` is only honored at the enterprise tier and otherwise silently ignored, and it reiterates the `days` default and tier caps in a user-facing way. This is valuable, though the description stays silent on `version_info` and `snapshot_type` (both already well-documented in 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 opens with a specific verb and resource: 'Fetch a historical time series of daily market-level snapshots.' It explicitly distinguishes this from single-ticker data by stating 'overall market sentiment, not a single ticker' and later name-drops the sibling tool get_ticker_history, making the purpose unmistakable.

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?

Provides concrete 'Call this when...' scenarios (trending market mood, charting α-sentiment, computing averages/momentum) and an explicit alternative: 'For a single ticker's history use get_ticker_history instead.' This gives clear guidance on when to use this tool versus its 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, the description carries the full burden of behavioral disclosure. It only includes a 'not financial advice' disclaimer and output field list, without stating side-effect-free read behavior, default date handling, or versioning nuances, which are merely in 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 a single concise sentence with a clear front-loaded verb and resource, followed by a compact list of output fields. Every word earns its place, with no repetition or fluff.

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

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

Despite lacking an output schema, the description explicitly lists all major signal components (type, horizon, scenario, triggers, confidence, invalidators), providing strong clarity on return values. The schema covers parameter details, so the description is sufficiently complete for a read-only data retrieval 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 the baseline is 3. The description adds no meaning beyond the schema's detailed parameter descriptions, merely echoing 'given date' already in the date parameter.

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 scenario strategic signals across all assets for a date, using a specific verb and resource. It distinguishes itself from per-ticker tools like get_ticker_signals by specifying 'across all assets,' and lists the exact signal fields returned.

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 a given date, across all assets) but provides no explicit guidance on when to use this tool versus alternatives like get_ticker_signals or get_market_snapshot. There are no exclusions or alternative tool mentions.

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 the many return fields but does not mention behaviors like date defaulting to latest when omitted, the fallback for version_number, or the fact that snapshot_type currently only supports 'crypto'. These are covered in the schema, but the description adds no behavioral caveats beyond the output list.

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, front-loaded sentence that efficiently states the tool's purpose and enumerates valuable return fields. It is slightly long and contains a typo ('intesity'), but every part contributes useful information given there is no output schema.

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, the rich schema documentation for parameters, and the absence of an output schema, the description provides a thorough list of return fields that helps the agent anticipate the response. However, it slightly implies date is required when it is optional, which is a minor gap in 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 baseline is 3. The description only indirectly references the 'date' parameter by saying 'for a given date' and adds no additional meaning to the parameters beyond what the schema already explains.

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 an 'overall crypto market daily snapshot' for a given date, with a specific verb and resource. It distinguishes itself from sibling tools like get_market_history, get_market_signals, and get_ticker_snapshot by focusing on the daily narrative report.

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 a daily market narrative report for a specific date, which provides clear context. However, it does not explicitly mention alternatives or when not to use this tool, such as pointing to get_market_themes or get_market_history for other data needs.

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 disclose behavioral traits. It does not state whether the operation is read-only, idempotent, or mention any side effects, permissions, or rate limits. The listed output fields offer some insight into the return structure but not into the underlying behavior, which is a significant gap for a tool with no annotation support.

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

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

The description is two concise sentences: the first states the primary purpose, and the second lists the theme attributes. It is front-loaded, contains no unnecessary words, and every sentence earns its place by clarifying what the tool returns.

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?

With no output schema, the description compensates by listing the theme fields, offering a clear picture of the response. However, it omits any mention of the versioning behavior or snapshot_type parameter, but those are fully covered in the schema. Given the tool's moderate complexity, the description is largely complete.

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

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

The schema provides 100% coverage of all four parameters with clear descriptions, so the parameter semantics are already well-documented. The description adds no additional parameter context beyond the schema, but the high schema coverage makes this a 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 uses a specific verb 'Get' and resource 'narrative themes dominating the crypto podcast space' with a temporal scope 'on a given date'. It also lists the output fields (title, summary, podcast coverage count, fragility score, novelty score, counterfactual narrative), which clearly distinguishes it from sibling tools like get_market_snapshot and get_market_episodes that do not mention 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?

The description implies usage by stating the purpose, but it does not explicitly mention when to use this tool versus alternatives like get_market_snapshot. There are no exclusions or alternative tool references, leaving the agent to infer from the name and description alone. The context is clear but the guidance is minimal.

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 adds valuable behavioral context—'Always returns the latest authoritative snapshot (is_latest) per asset'—and implies authentication. However, it does not disclose edge cases like empty results or potential rate limits, making it adequate but not rich.

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, consisting of two sentences that directly state the purpose and a key behavioral guarantee. It is well-structured, front-loaded, and contains no redundant information.

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

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

For a low-complexity getter tool with rich schema and no output schema, the description is sufficient. It covers the core function and mentions the `is_latest` field, giving a hint about response structure. However, it could mention the authentication requirement explicitly or note that no favorites may return an empty list, but this is not a major gap.

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

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

The schema provides 100% description coverage for both parameters, so the baseline is 3. The description itself does not elaborate on parameters, but the schema descriptions are already comprehensive, so it adds no extra semantic value.

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

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

The description clearly states the tool's function: retrieve the authenticated user's favorited crypto assets on AudioAlpha. It uses a specific verb ('Get'), a specific resource ('crypto assets'), and distinguishes from sibling tools like get_my_favorite_podcasts by 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 gives clear context by specifying 'authenticated user' and 'crypto assets', making it obvious when to use this tool. However, it does not explicitly mention alternatives or when-not-to-use conditions, such as comparing with get_my_favorite_podcasts or excluding tradfi, so it lacks explicit exclusionary guidance.

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

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

With no annotations, the description carries the burden of behavioral disclosure and does state that the result is scoped to the authenticated user's follows, implying authentication is required. It does not elaborate on return format or edge cases, but for a simple read-only list tool this is reasonable.

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, clear sentence with no filler or redundant information. It front-loads the verb and resource and does not repeat schema details.

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

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

The tool is simple (no params, no output schema), and the description sufficiently explains what the tool returns: a list of the user's followed podcasts. It could specify the shape of each podcast item, but the description is likely adequate for an agent to select and invoke the tool correctly.

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 is empty (0 params), and the description correctly implies that no arguments are needed because the output is determined by the authenticated user's follows. This meets the baseline for parameterless tools; the description adds context about how the implicit user scope drives the result.

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: the list of podcasts the authenticated user follows on AudioAlpha. This distinguishes it from siblings like get_my_favorite_assets or search_podcasts by specifying both the object type (podcasts) and the scope (user's followed list).

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

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

The description provides clear context that this tool is for retrieving the authenticated user's followed podcasts, making its use case evident. It does not explicitly name alternatives or exclusions, but the user-specific framing implies that tools like search_podcasts are for discovery rather than personal follows.

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

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

With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: 'Always uses the latest authoritative snapshot (is_latest)', and it details the response structure. However, it does not mention potential edge cases, error conditions, or authentication/permissions. The description adds some value but is not rich behavioral 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 two sentences, front-loaded with the core purpose, then a structured list of returned fields. Every clause adds value and there is no redundant or filler content.

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 provides a comprehensive overview of the return payload, which is especially important since there is no output schema. It explains the feed's composition and the meaning of key fields. However, it lacks information about potential empty results or error handling, so it is slightly below maximum 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%, with each parameter having a clear description. The tool description adds no additional parameter-level meaning beyond what the schema already provides, so it meets the baseline.

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

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

The description clearly states the tool's purpose: 'Get a personalized feed based on the user's favorite assets and followed podcasts.' It uses a specific verb and resource, and the scope (personalized, based on favorites/followed podcasts) distinguishes it from siblings like get_market_snapshot or get_my_favorite_assets.

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 the use case (personalized feed for the user), but it does not explicitly state when to use this tool vs alternatives, nor does it mention when not to use it. No alternative tools are referenced.

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. It indicates a read-only operation via 'Get' and adds the behavior of returning only 'recent' episodes, which is useful. However, it does not disclose response format, ordering, pagination, or error behavior, 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, direct sentence with no filler. It front-loads the core action and scope, making it immediately understandable and appropriately sized.

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 tool has no output schema and no annotations, so the description should explain the return structure and any prerequisite steps. It covers the core purpose but does not mention that the podcast ID comes from search_podcasts (though the schema notes it) or what fields the episodes contain. Given the simplicity, it is minimally viable but not complete.

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

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

The input schema has 100% parameter description coverage, so the baseline is 3. The description briefly reiterates the podcast ID but adds no extra syntax, constraints, or relationships beyond what the schema already provides. No compensation is needed.

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 resource 'recent episodes', and the scope 'for a specific podcast by podcast ID'. It distinguishes from sibling tools like get_episode_details which focus on a single episode, while this tool returns multiple episodes for a podcast.

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_podcast_latest or get_episode_details. The description states what it does but does not mention exclusions, prerequisites, or when a different tool would be more 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 discloses the return fields but omits error behavior, read-only status, or any side effects. The description is minimal and lacks depth for the agent to understand edge cases.

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, concise sentence that immediately states the action and lists key output fields. It is well-structured and contains no superfluous 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 one-parameter tool, the description provides adequate coverage of the output fields. However, it lacks explanation of how this tool differs from get_podcast_latest_full and does not address error cases, leaving some gaps 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?

The parameter podcast_id is fully described in the schema with the note 'Podcast ID from search_podcasts'. The description adds no additional parameter details, so the high schema coverage sets the baseline at 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 tool fetches the latest episode for a given podcast and lists the included data fields. It is specific about the resource and action but does not explicitly contrast with the sibling tool get_podcast_latest_full.

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 usage guidance is provided. The description does not mention when to use this tool versus alternatives like get_podcast_latest_full or get_podcast_episodes, leaving the decision to the agent's interpretation.

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 must carry the burden of behavioral disclosure. It only describes what is returned ('latest episode' with 'quotes and asset mentions') but does not state whether the operation is read-only, requires authentication, or how the response is structured, leaving key behavioral aspects implicit.

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 14-word sentence that front-loads the core purpose and includes relevant content details without any filler, earning the highest score for conciseness.

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

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

The tool is simple (one parameter, no output schema), but the description leaves 'full details' vague and does not specify the response format or edge cases (e.g., no episodes). It covers the main intent but not enough for an agent to know exactly what to expect.

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 already fully documents the only parameter (podcast_id) with a helpful source reference ('from search_podcasts'), so the description adds little beyond reinforcing the podcast context. Baseline 3 is appropriate due to high schema coverage.

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

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

The description clearly states the verb 'Get' and the exact resource ('latest episode for a podcast') while adding detail granularity ('full details including quotes and asset mentions'), which distinguishes it from siblings like get_podcast_latest and get_episode_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 when to use this tool (when the full detail of the latest episode with quotes and asset mentions is needed) but does not explicitly contrast it with alternatives such as get_podcast_latest, so exclusions are absent.

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 output scoring scale (0-10) and ranking by selection score, which is useful. However, it does not mention error behavior (e.g., empty response for invalid ticker), authorization, or explicit read-only guarantees, leaving part of the behavioral context unspecified.

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

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

One sentence, approximately 20 words, that efficiently conveys the action, object, scope, and key output characteristics. No wasted words or redundancy. The most critical 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?

No output schema exists, so the description should explain return structure. It mentions featured quotes, α-sentiment scores scale, and ranking, but does not describe specific fields, whether the response is an array or object, or pagination/versioning behavior. Parameter coverage is strong, but response semantics are only partially described.

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%: all five parameters include detailed descriptions (e.g., ticker suffix requirement, date format, version_number semantics). The tool description adds no additional parameter-level meaning beyond what the schema already provides, so 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 uses a specific verb ('Get'), identifies the exact resource ('featured podcast quotes'), scopes it to 'a specific crypto asset' and 'a given date', and details output traits (α-sentiment scores, ranking). This clearly distinguishes it from market-level or episode-level siblings like get_market_featured_quotes and get_episode_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 clearly implies the use case: retrieve featured quotes for a specific asset on a selected date. It provides clear context but does not explicitly mention alternatives or exclusions (e.g., 'for market-wide quotes use get_market_featured_quotes'). However, the scoping is unambiguous enough to guide selection.

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 of behavioral disclosure, and it delivers richly. It reveals that `date` is silently ignored for non-enterprise tiers, `days` is silently capped, bare ticker symbols return empty/404, and the response includes tier_cap, effective_days, start_date, end_date, and date_param_honored. It even explains the version_info behavior and that the status field is always returned. This is exemplary 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. The first sentence defines the core action, followed by explicit use-case guidance, then clear parameter requirements, tier caps, date behavior, and return format. Every sentence adds necessary information without redundancy or fluff, making it 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?

Despite lacking an output schema, the description adequately describes the return format: 'array of daily snapshots (oldest first), each with snapshot_date plus all standard AssetSnapshot fields' and lists the response metadata fields. It also covers parameter edge cases (bare symbols, tier caps, date honoring). The description is complete enough for an agent to invoke the tool correctly and set expectations for the response without any additional documentation.

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?

Although schema coverage is 100%, the description adds significant semantic value beyond the schema. It explains the '-USD' suffix requirement in detail, clarifies that the `date` parameter is only honored for enterprise tier (silently ignored otherwise), specifies that `days` is tier-capped and silently capped, and elaborates on `version_info` fields and `snapshot_type` reserved 'tradfi' status. This goes well beyond the schema's basic 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 opens with a specific verb+resource+scope: 'Fetch a historical time series of daily snapshots for one crypto ticker.' It clearly distinguishes the tool from siblings like get_ticker_snapshot (current snapshot) and get_market_history (market-wide) by focusing on per-ticker historical data. The use-cases ('recent trend', 'chart', 'averages, momentum, or volatility') further reinforce the specific purpose.

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 call the tool: 'Call this when the user asks about a ticker's recent trend... or needs a window of data to compute averages, momentum, or volatility.' This is clear context for invocation. However, it does not explicitly name alternatives or when-not-to-use, though the sibling tool names imply the distinction. Thus, it earns a 4 for clear context lacking explicit 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?

There are no annotations provided, so the description carries the full burden of behavioral disclosure. It does explain the two response shapes depending on the category parameter, which is useful. However, it omits details about potential side effects, rate limits, error behavior, or the impact of other parameters like date omission and limit. For a read-only getter, this is adequate but not comprehensive.

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, front-loaded with the core purpose, and directly conveys both modes of operation without any redundant phrasing. Every sentence earns its place, providing high information density in a compact structure.

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 usefully specifies the return shape for both modes. It covers the main complexity of the tool (category-dependent behavior) but does not mention defaults for date or limit, though those are covered in the schema. Overall, it is sufficiently complete for the tool's moderate 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 description coverage is 100%, so the baseline is 3. The description adds value specifically for the category parameter by explaining the two possible return formats. It does not add additional semantics for date, limit, version_info, or snapshot_type beyond what the schema already documents. Thus, it meets but does not exceed the baseline.

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

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

The description starts with a specific verb+resource+time: 'Get ticker leaderboard for a given date.' It clearly distinguishes itself from sibling ticker tools by focusing on leaderboard rankings. It further specifies the two operational modes based on the category parameter, leaving no ambiguity about what the tool returns.

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

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

The description provides clear context: use this tool to retrieve ticker leaderboards for a date, with or without a category filter. It explains the difference between calling without category (returns highlights) and with category (returns an array). However, it does not explicitly state when to choose this over sibling tools like get_ticker_snapshot or get_ticker_history, nor does it mention exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal the acceptable return fields and implicitly indicates a read-only operation via 'Get'. However, it does not disclose edge-case behaviors like fallback to the latest version for invalid versions, empty/404 responses for invalid tickers, or that the 'status' field is always returned—though some of these are covered in parameter descriptions. The 'Not financial advice' disclaimer is not behavioral 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 two sentences with no redundancy. The first sentence states the purpose and scope, the second lists output components. The disclaimer is slightly extraneous but does not detract from conciseness or structure.

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 absence of an output schema, the description helpfully enumerates the returned signal attributes, which is important for the agent. The parameter schema handles the parameter-side complexity well. The only notable gaps are the lack of explicit usage alternatives and edge-case behavior, but for a read-only signal retrieval tool with a rich schema, the description is largely complete.

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

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

The input schema provides 100% parameter coverage with detailed descriptions, including the mandatory '-USD' suffix and version fallback semantics. The tool description adds no parameter-level information beyond the schema, so the baseline 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 resource ('scenario signals for a specific crypto asset'), clearly distinguishing it from market-level siblings like get_market_signals. It also enumerates what the tool returns (signal type, time horizon, scenario description, trigger conditions, confidence score, invalidating conditions), leaving no ambiguity about its function.

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 phrase 'for a specific crypto asset' implies the tool is for ticker-level signals, but the description offers no explicit guidance on when to use it versus alternatives such as get_market_signals or get_ticker_snapshot. It also does not mention exclusions or prerequisites, so the usage context remains only implied.

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 must disclose behavioral traits. It lists return metrics and some scales (0-1, 0-10), but does not mention behavior such as lazy version resolution, empty responses for invalid tickers, or the always-returned status field. Those details live only in the parameter schema, not 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?

The description is a single dense sentence that front-loads the primary purpose ('Get detailed daily insights snapshot for a specific crypto asset') and then lists key metrics. It is verbose but efficient, avoiding fluff. The list format could be more scannable, but every element adds 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?

There is no output schema, so the description must convey return contents. It does this well by itemizing the snapshot's fields with scales for some metrics. However, it lacks high-level context about response structure or edge cases (e.g., fallback to latest version), which are deferred to the schema. For a tool with moderate complexity, this is mostly adequate but not exhaustive.

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

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

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter meaning beyond the output metrics, and does not discuss the 'ticker' suffix requirement or version parameters. Baseline 3 is appropriate since the schema carries the parameter documentation burden.

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 returns 'detailed daily insights snapshot for a specific crypto asset' and enumerates specific metrics (α-index, α-pulse, episode count, etc.), distinguishing it from sibling tools like get_ticker_history or get_ticker_signals. The verb 'Get' plus resource 'crypto asset snapshot' is specific and unambiguous.

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

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

The description implies use for fetching a comprehensive daily snapshot for a single asset, but provides no explicit guidance on when to choose this over alternatives like get_ticker_history or get_ticker_signals. No exclusions or alternative tool references are mentioned, leaving some ambiguity in a crowded sibling set.

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 discloses the output fields (podcast ID, name, artist, language, x-handle, artwork), which is useful. However, it does not describe key behavioral traits such as whether the search is case-insensitive, supports partial matches, returns multiple results, or has pagination or rate limits. This leaves gaps but does not mislead.

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 that front-loads the core action and then lists return fields. Every word earns its place with no redundancy or unnecessary detail.

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 is reasonably complete for a simple search tool: it states the domain (crypto podcasts), the matrix (name), and the return fields. It lacks mention of result count limits or how to use the returned podcast IDs with sibling tools, but given the simplicity and good schema coverage, it suffices.

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%: the q parameter is described with examples (e.g., 'unchained, bankless, chopping block'). The tool description adds little beyond the schema, only noting the search is by name, which is already implied. Baseline 3 applies since the schema fully documents the parameter.

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 'Search for crypto podcasts by name' with a specific verb and resource, and lists the return fields (podcast ID, name, artist, language, x-handle, artwork). This distinguishes it from sibling tools, which are all 'get_' tools for episodes, market data, or favorites, making the search functionality unmistakable.

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: use this when you need to find a podcast by name, likely to obtain a podcast ID for subsequent calls. However, it does not explicitly state when to use this tool vs alternatives, nor does it mention any exclusions or prerequisites. There is no guidance on preferring get_my_favorite_podcasts for saved podcasts or get_podcast_episodes if the ID is already known.

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