AudioAlpha

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 returned fields and implies a read-only metadata operation, but does not mention error conditions, authentication, rate limits, or whether the episode must exist. For a simple getter, this is acceptable but not deeply transparent.

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, front-loaded with the action, and includes all necessary detail without fluff. Every word contributes to understanding the tool's output.

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 metadata getter with no output schema, the description covers the main return fields and the podcast info. It does not describe the full return structure or error behavior, but given the tool's simplicity, 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 coverage is 100% (episode_id) and the schema description is sufficient. The tool description does not add extra meaning to the parameter beyond indicating it identifies a specific episode, which the schema already conveys. 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 clearly identifies the tool as retrieving metadata for a specific episode, listing the exact fields (title, publish date, duration, sentiment scores, podcast info). This distinguishes it from sibling tools like get_episode_summary or get_episode_full, 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 implies the tool is for fetching comprehensive metadata for a single episode, but it does not explicitly state when to choose this over alternatives like get_episode_summary or get_episode_full. Sibling names suggest clear distinctions, but the description itself lacks explicit when/when-not 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 full burden. It discloses the primary behavior (retrieval of specific episode components) and adds value by explaining what 'full' includes. However, it omits any mention of authentication, response format, error behavior, or potential rate limits, leaving gaps for an agent.

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 front-loads the core action ('Get full episode data in one call') and directly lists contents. Every word is purposeful, with 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?

The description is sufficient for a simple get tool with a single parameter: it states what the tool does and what data it returns. No output schema exists, but the listed components give a clear expectation. Minor gaps remain (e.g., whether transcript is included), but overall it is well-complete for 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?

The input schema already provides 100% coverage with a clear description for episode_id ('Episode ID'). The tool description adds no additional parameter-level detail, so it meets the baseline 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 tool's function: 'Get full episode data in one call' with a specific list of included data (details, summary, quotes, asset mentions). It distinguishes itself from sibling tools like get_episode_details and get_episode_quotes by aggregating multiple data 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 phrase 'in one call' suggests using this tool when you need all the listed data at once, implicitly contrasting with the need for multiple calls to other tools. It provides clear context but does not explicitly mention alternatives or when not to use it.

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 reveals output details (speaker, α-sentiment score scale 0-10, associated ticker) and implies read-only behavior via 'Get'. While it doesn't mention edge cases like missing episodes or pagination, the provided specifics go beyond a bare statement.

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, front-loaded with the action and resource. No unnecessary words 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?

For a simple one-parameter tool with no output schema, the description adequately explains the return content (speaker, sentiment, ticker). It lacks potential failure modes or pagination info, but for a straightforward getter, this is sufficient. The presence of many sibling tools distinguishes its role.

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

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

The schema description covers 100% of the parameter (episode_id described as 'Episode ID'). The description confirms the need for a specific episode but adds no additional format, constraints, or examples. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool's function: retrieving all quotes from a specific episode, with speaker, sentiment score, and ticker. It distinguishes itself from sibling tools like get_episode_transcript, get_episode_details, and get_episode_summary by specifying it returns 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?

Usage is implied: you use this tool when you need quotes from a specific episode. However, there is no explicit comparison to alternatives like transcript or details, nor guidance on when not to use this tool. The description lacks explicit when-to-use vs alternatives.

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

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

With no annotations provided, the description carries the full burden. It adds useful behavioral context like the 0-10 α-sentiment scale and that assets are included, but it does not disclose error behavior, required permissions, or whether the summary is concise vs. detailed. It is a straightforward read operation, so some leniency applies, but more detail would improve 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, front-loaded sentence that efficiently conveys the core function and key additional data points. Every word earns its place with no redundancy 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?

For a simple one-parameter tool without an output schema, the description adequately explains the return content (summary, assets, sentiment). It could be more complete by noting that the output is not a full transcript, but that is implicitly covered by the word 'summary' and the sibling name get_episode_transcript.

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 covers the only parameter (episode_id) with a minimal description 'Episode ID', and schema description coverage is 100%. The tool description does not add extra meaning to this parameter beyond what is already in the schema, so a 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 gets a 'transcript summary for a specific episode' and adds specifics about included assets and α-sentiment, distinguishing it from siblings like get_episode_transcript or get_episode_details. The verb 'Get' and resource are explicit.

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 context is implied by the term 'transcript summary' and the mention of sentiment, suggesting it is for users needing a condensed summary rather than the full transcript. However, no explicit when-to-use or alternatives are provided, leaving room for ambiguity among the many get_episode_* siblings.

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

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

With no annotations provided, the description is the primary source of behavioral disclosure. It adds meaningful traits: the transcript includes speaker diarization and speaker name mapping, and access is plan-restricted. However, it does not disclose the output format, response structure, or potential size limits, which could be relevant for a full transcript.

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 front-loads the action and resource, includes key features, and states the plan restriction. There is no unnecessary detail 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?

For a simple tool with one parameter and no output schema, the description conveys the essential purpose and a critical access restriction. However, it leaves out the return format (e.g., plain text, JSON) and any handling of large transcripts, which would improve completeness. Overall, it is fairly complete for 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?

The input schema has 100% coverage for its single parameter, episode_id, which is described as 'Episode ID'. The description does not add any additional parameter-level details, but the baseline of 3 applies because the schema already 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 the tool's purpose: to get the full transcript for a specific episode. It specifies the resource (episode transcript) and the action (get), and differentiates from siblings like get_episode_summary and get_episode_quotes by emphasizing 'full transcript' and including speaker diarization/mapping.

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 access restriction ('Pro and Enterprise plans only'), which helps the agent determine when the tool can be used. It implies usage for obtaining full transcripts versus summaries or quotes, but does not explicitly name alternative tools or provide when/when-not scenarios beyond the plan restriction.

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 carries the full burden for behavioral disclosure. It states the output fields and scope, but does not mention the optional date behavior (omitting for latest), edge cases like empty results, or explicit read-only guarantees. This is adequate but not 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, succinct sentence that front-loads the core purpose and includes essential return field details. There is no fluff or 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?

For a simple read-only tool with one parameter and no output schema, the description covers the purpose, filter, and return fields adequately. It omits the optional-date nuance, but that is documented in the schema, so the overall context is 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?

The schema already fully describes the single 'date' parameter with format and optionality (100% coverage). The description adds little beyond restating that episodes are date-filtered, so it does not meaningfully enhance parameter semantics.

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

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

The description uses a specific verb ('Get'), a clear resource ('crypto podcast episodes'), and a scoping condition ('published on a given date'). It also lists the return fields, distinguishing it from per-episode tools like get_episode_details.

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 usage for retrieving all crypto podcast episodes for a specific date, which differentiates it from per-episode or per-podcast tools. However, it does not explicitly state when not to use it or name alternatives.

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

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

No annotations are provided, so the description must carry the behavioral burden. It mentions ranking by relevance, but does not disclose response shape, pagination, default date behavior, or what happens when no data exists. The schema provides parameter details, but the description itself adds little 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?

Single, front-loaded sentence with no filler. Every word contributes to the purpose, 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?

The tool has 4 parameters and no output schema, yet the description does not hint at response structure or pagination. It is minimally viable given the rich parameter schema, but leaves gaps about return values and edge-case behavior.

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 does not add meaning beyond the schema—saying 'for a given date' merely echoes the 'date' parameter description. The rich schema already documents defaults and enum values.

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 uses specific verb 'Get' and clearly identifies the resource: 'top featured quotes from crypto podcasts'. It adds scoping details ('for a given date', 'ranked by relevance score') that distinguish it from sibling tools like 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?

Usage context is implied—this tool gets market-level featured quotes—but no explicit guidance is given about when to choose it over alternatives like get_ticker_featured_quotes. There is no mention of exclusions or fallbacks.

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 behavioral burden. It discloses significant traits: the `date` parameter is silently ignored for non-enterprise tiers, `days` is tier-capped and silently capped above the cap, snapshots are returned oldest first, and response metadata includes `tier_cap`, `effective_days`, `start_date`, `end_date`, and `date_param_honored`. This is thorough and candid.

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 dense but well-organized: purpose and usage in the first paragraph, parameter caveats in the second/third, and a clear return-value statement. Every sentence provides necessary information, and the structure makes the important caveats (tier caps, date ignoring) easy to spot.

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—tier caps, date behavior, return format—and the absence of an output schema, the description is complete. It explains what the response contains (array of daily snapshots with `snapshot_date` and standard `MarketSnapshot` fields, plus metadata fields) and covers all critical edge cases. It doesn't need to re-explain parameters already well-documented in the 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 coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining the `date` parameter's enterprise-only behavior ('silently ignored' on other tiers) and by restating the `days` range and default in operational context. It relies on the schema for `version_info` and `snapshot_type`, which is acceptable given the schema's own detailed 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 and resource: 'Fetch a historical time series of daily market-level snapshots.' It immediately clarifies scope ('overall market sentiment, not a single ticker') and explicitly distinguishes itself from a sibling by saying 'For a single ticker's history use get_ticker_history instead.' This makes the tool's 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?

The description gives explicit when-to-use guidance: 'Call this when the user asks how the overall market mood/regime has trended over time, wants to chart market α-sentiment / z-score over a window, or needs a range of daily market snapshots to compute averages or momentum.' It also names the alternative tool for single-ticker histories, and explains tier-specific behaviors affecting usage.

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

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

No annotations are provided, so the description carries the burden of disclosure. It explains the structure of the returned signals and adds a 'not financial advice' disclaimer, which is useful. However, it does not mention authentication, rate limits, or behavior for missing data or invalid dates, leaving some ambiguity for a read operation.

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 followed by a short disclaimer. It is front-loaded with the action and resource, and every phrase serves a purpose. This is appropriately sized 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?

With no output schema, the description sufficiently outlines the content of each signal. It does not explain the optional date or snapshot_type limitations, but the schema covers these. Given the read-only nature and adequate schema documentation, the description is complete enough 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?

The schema covers all four parameters with descriptions, including defaults and version behavior. The description does not add any additional parameter semantics beyond what the schema already provides, so it relies entirely on 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 resource ('scenario strategic signals'), the scope ('across all assets'), and the temporal constraint ('for a given date'). It also enumerates the signal components, which differentiates it from per-ticker tools like get_ticker_signals.

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

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

The description implies the tool is for retrieving market-wide scenario signals, but it does not explicitly state when to use it over sibling tools such as get_ticker_signals or get_market_snapshot. No exclusions or alternative recommendations are provided, making the usage context 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly enumerates a rich set of output fields (OHLC scores, z-scores, wordcloud, etc.), providing transparency about what the tool returns. It does not mention side effects (none expected for a GET) or rate limits, but the detailed output list gives the agent a strong sense of the tool's behavior. The only minor omission is explicit clarification that this is a read-only operation, but the verb 'Get' implies it.

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, information-dense paragraph (two sentences) that front-loads the purpose and then lists all return fields. While it is somewhat run-on, every item in the list adds value, and it avoids redundant phrasing. The structure is acceptable for a tool with a complex return payload, and there is no wasted text.

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 explain what the tool returns. It lists all major components of the snapshot (headline, delta narrative, regime, α-sentiment scores, z-scores, narrative summary, market psychology, consensus, tensions, surprise mentions, episode volume, wordcloud). This gives a solid overview, though it does not define each field's meaning or the versioning behavior (which is covered in the input schema). Given the tool's complexity, the description is reasonably complete for an agent to understand the output scope.

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 provides 100% coverage for the four parameters, including descriptions for date, version_info, snapshot_type, and version_number. The description adds marginal value beyond the schema—it mentions 'for a given date' and 'Omit for latest,' but these are already captured in the schema. The description does not explain the semantics of version_info or snapshot_type beyond what the schema states, so 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 opens with 'Get the overall crypto market daily snapshot (daily narrative insight report) for a given date,' which clearly identifies the verb (Get), resource (market snapshot), and scope (daily narrative insight). The detailed list of return fields distinguishes it from sibling tools like get_market_signals or get_market_history by focusing on the narrative report nature, making its purpose unambiguous.

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

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

The description provides clear context: it is a daily snapshot intended for a specific date, with the ability to omit the date for the latest report. However, it does not explicitly mention when not to use it or suggest alternatives among the sibling tools. Still, the 'daily narrative insight' framing implies a distinct use case, so it earns a 4 rather than a 5.

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 behavioral disclosure. It does state what each theme includes (title, summary, coverage count, fragility score, novelty score, counterfactual narrative), but it does not disclose behaviors like version fallback, the always-returned status field, or potential empty results for missing dates—though some of these are covered in parameter descriptions.

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 exactly two sentences: the first front-loads the verb, resource, and time scope; the second lists the output fields. Every word earns its place, with no fluff or repetition of 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?

Given no output schema, the description does a good job of conveying what each theme contains, giving a clear mental model of the return shape. It stops short of stating whether the response is a list or object, or how missing dates are handled, but the input schema complicates the missing-date behavior with the version fallback, so the description covers the essentials for a read-only 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 only adds 'given date', which maps to the date parameter, but adds no extra semantic insight for version_info, snapshot_type, or version_number. The schema itself does the heavy lifting, so the description provides minimal additional 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 the specific verb 'Get' with the resource 'narrative themes dominating the crypto podcast space', which clearly distinguishes it from sibling tools like get_market_snapshot or get_episode_details. It also lists the key output components, leaving no ambiguity about what the tool provides.

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 'on a given date' clearly indicates the primary use case: retrieving themes for a specific date, with the input schema noting that omitting the date gets the latest. However, it does not explicitly mention when not to use this tool or name alternative tools, so it stops short of full 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 full burden of behavioral disclosure. It adds a meaningful guarantee—'Always returns the latest authoritative snapshot (is_latest) per asset'—which is not evident from the tool name or schema. This is valuable, though it omits details like pagination or error handling, so not 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 two sentences, each serving a distinct purpose: the first identifies the tool's core function, the second provides a key behavioral guarantee. There is no redundant information, making it highly concise and easy to parse.

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

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

Given the tool's moderate complexity (2 optional params, no output schema), the description adequately covers its purpose and a notable behavior (is_latest). It could be more explicit about the response structure or sorting, but it is sufficiently complete for an agent to understand 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?

Parameter descriptions in the schema are comprehensive, covering all semantics including defaults and enum options. The tool description adds no extra parameter-related context beyond what the schema already explains, 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 function: retrieving a list of crypto assets the authenticated user has favorited on AudioAlpha. The verb 'Get' is specific, the resource is well-defined, and the mention of 'is_latest' adds precision. It is easily distinguishable from sibling tool get_my_favorite_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 provides clear context that this is for the authenticated user's favorites, which helps in selecting this tool over generic market snapshot tools. However, it does not explicitly state when not to use it or name alternative tools for comparison, so it falls short of a 5.

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

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

With no annotations provided, the description carries the full burden. It discloses that it requires an authenticated user and returns a list of followed podcasts, implying a read-only operation. However, it does not mention any additional behavioral details such as pagination, ordering, rate limits, or authentication failure handling. The description is minimally adequate 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, concise sentence that front-loads the core action and resource. There is no extraneous information, and every word contributes to understanding the tool's purpose.

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 (no parameters, no output schema), the description is adequately complete. It specifies the resource and user scope, which is sufficient for a straightforward getter. It could be enhanced by describing the return format, but for a list-returning tool, the current description covers the essential context.

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

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

The tool has zero parameters, so there are no parameters to explain. The description adds no parameter semantics beyond the schema because the schema is empty. Per the rubric, 0 params baseline is 4; the description is consistent with that.

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: 'Get the list of podcasts the authenticated user follows on AudioAlpha.' It uses a specific verb ('Get'), identifies the resource ('list of podcasts'), and specifies the scope (authenticated user's follows on AudioAlpha). This distinguishes it from siblings like get_my_favorite_assets and get_podcast_episodes.

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

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

The description implies usage when you need the authenticated user's followed/favorite podcasts, but provides no explicit guidance on when to use this tool over alternatives or any exclusions. No alternative tools are mentioned, and the context is inferred from the name and 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 behavioral burden. It discloses a key behavior: 'Always uses the latest authoritative snapshot (is_latest).' It also details the return structure comprehensively (snapshot_date, array of assets with daily snapshot, trading signals, curated quotes). This goes well beyond a vague description, though it omits potential edge cases like empty feeds or authentication requirements.

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: the first states purpose and a key behavior, the second details the output structure. Every sentence adds value, with no redundancy or fluff. It is front-loaded with the action and efficiently communicates the full return shape.

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 no output schema and no annotations, the description is self-sufficient. It explains what the feed is based on, the is_latest behavior, and the exact structure of the response (snapshot_date, assets with subfields). Combined with complete schema descriptions for parameters, the tool is fully specified for an agent to select and invoke 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 has 100% description coverage for its three parameters (version_info, snapshot_type, episodes_lookback), so the schema already provides full parameter semantics. The tool description itself does not discuss parameters, which is acceptable given the high schema coverage. 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 clearly states the tool's function: 'Get a personalized feed based on the user's favorite assets and followed podcasts.' This uses a specific verb ('Get') and resource ('feed'), and the inclusion of 'personalized' differentiates it from sibling tools like get_market_snapshot or get_my_favorite_assets. The description also adds a distinctive behavior ('Always uses the latest authoritative snapshot') further distinguishing it.

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 the tool (when a personalized feed is needed) but does not explicitly compare to alternatives or state when not to use it. For example, it doesn't mention that get_my_favorite_assets is for the raw list or that get_ticker_snapshot is for individual assets. This is sufficient context but lacks explicit exclusions or 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, the description must disclose behavioral traits itself. It says 'Get', suggesting a read-only operation, but does not explicitly confirm the absence of side effects, nor does it mention any limitations or additional behavior such as ordering, pagination, or the meaning of 'recent'. This is a minimal disclosure.

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, front-loaded with the action and resource. Every word is purposeful, with no redundant information or filler.

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 relatively simple with two params and no output schema. The description explains the core purpose but does not describe the return format or what fields are included in each episode. Given the absence of an output schema, this is a notable gap, making the description only minimally 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% coverage for both parameters (podcast_id and limit) with clear descriptions including default and max values. The tool description adds no new semantic meaning beyond restating 'by podcast ID', so a 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's function: retrieving recent episodes for a specific podcast using a podcast ID. The verb 'Get' and resource 'episodes' are specific, and the scope ('for a specific podcast by podcast ID') distinguishes it from episode-level tools like get_episode_details and other podcast tools like get_podcast_latest.

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

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

The description implies usage: if you need recent episodes for a podcast ID, use this tool. However, it provides no explicit guidance on when not to use it or how it compares to sibling tools such as get_podcast_latest or get_episode_details, so the usage context 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?

With no annotations provided, the description carries the full transparency burden. It discloses the return payload (title, episode_id, summary, artwork, sentiment), which is useful. However, it does not mention behavior on invalid podcast_id, define what 'latest' means (e.g., publish date vs. addition date), or note that it is a read-only operation beyond the verb 'Get'.

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

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

The description is a single sentence that leads with the action and resource, then enumerates the return fields. It is compact and all information contributes to understanding what the tool does. No redundant filler.

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 only one parameter and no output schema, the description does a good job of listing the expected return fields. However, it leaves gaps by not defining 'latest' or explaining the sentiment format. This is slightly beyond the minimum viable for a simple getter.

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

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

The schema description for podcast_id is already complete ('Podcast ID from search_podcasts'), and schema coverage is 100%. The tool description adds no further parameter semantics 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 clearly states the tool returns the latest episode for a specific podcast and lists the key returned fields including title, episode_id, sentiment, and artwork. This specific verb+resource combination distinguishes it from siblings like get_podcast_episodes, which lists all episodes. The tool's function is 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 usage when you need the most recent episode and its sentiment, but it does not explicitly contrast with alternatives such as get_podcast_latest_full or get_episode_details. There is no clear statement of when not to use this tool versus others.

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 behavioral disclosure. It adds value by revealing that the response includes quotes and asset mentions, which goes beyond the tool name. However, it does not clarify read-only status, error behavior, or other potential side effects, leaving some ambiguity.

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 is front-loaded with the action and scope. There is no redundant information or filler, 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?

For a simple one-parameter read tool, the description adequately states what it does and highlights key output components (quotes, asset mentions). However, without an output schema, it could be more explicit about the full return structure or edge cases, though it remains mostly 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 fully describes the only parameter (podcast_id) with a reference to search_podcasts, achieving 100% schema coverage. The description adds no additional parameter 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 clearly states a specific verb ('Get'), resource ('latest episode for a podcast'), and scope ('full details including quotes and asset mentions'). This distinguishes it from siblings like get_podcast_latest (which likely lacks full details) and get_episode_full (which targets a specific episode rather than the latest).

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

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

The phrase 'latest episode' and 'full details' provide clear context for when to use this tool, but it does not explicitly name alternatives or when-not-to-use conditions. The guidance is clear but not exhaustive.

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 that results are ranked by selection score and include α-sentiment scores on a 0-10 scale. However, it does not explain response format, error behavior, or data freshness, which could be important.

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 effectively communicates the core purpose without filler. It is front-loaded with the action and resource, making it easy to scan.

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 and annotations, the description does not fully explain response structure or edge cases. However, the schema covers parameter specifics, and the description gives a reasonable overview. It could mention pagination or limit behavior, but it is adequate for basic 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?

All five parameters have thorough schema descriptions with examples and defaults. The tool description adds no extra parameter meaning; schema coverage is 100%, so the baseline score 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 function: retrieving featured podcast quotes for a specific crypto asset on a given date, with α-sentiment scores and ranking. It distinguishes itself from sibling tools like get_market_featured_quotes by specifying 'specific crypto 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 a single ticker via 'specific crypto asset' but does not explicitly contrast with alternatives such as get_market_featured_quotes or get_episode_quotes. There is no when-not-to-use 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 fully discloses behavioral quirks: tier caps on days, silent capping, the date parameter being ignored for non-enterprise tiers, and the return format (oldest first, includes tier_cap, effective_days, etc.). This goes beyond the schema and gives the agent critical runtime expectations.

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 dense but every sentence earns its place: purpose, usage triggers, required format, optional parameters, tier caps, a behavior caveat, and return structure. It's front-loaded with the core action and organized logically, with no 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 properly explains the return shape (array of daily snapshots, oldest first, plus metadata fields). It also covers the complex interplay of parameters (days vs date) and tier restrictions, making it complete for a 5-parameter tool with notable edge cases.

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

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

Although the schema has 100% coverage, the description adds significant meaning beyond it: the mandatory '-USD' suffix for ticker, tier-capping behavior for days, and the enterprise-only honoring of the date anchor. These are not fully inferable from the schema alone and prevent costly mistakes.

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 and resource: 'Fetch a historical time series of daily snapshots for one crypto ticker.' It clearly distinguishes this from sibling tools like get_ticker_snapshot (current snapshot) and get_market_history (market-level history) by focusing on a single ticker's time series.

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 explicit when-to-use scenarios: 'Call this when the user asks about a ticker's recent trend, wants to chart or plot... asks how has X changed over the last N days, or needs a window of data.' It doesn't mention alternatives or when-not-to-use, but the context is clear. Without exclusions, it falls short of a 5.

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 clearly explains the two distinct output structures depending on the presence or absence of the category parameter, which is valuable and not captured in the schema. It does not mention optional date behavior or limit caps, but those are already documented in the schema, so the additional context is meaningful.

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 a front-loaded, action-first opening. It efficiently presents the default behavior and the conditional alternative, with no redundant or irrelevant 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 tool has 5 parameters and no output schema, but the schema documents every parameter fully, and the description adds the key behavioral branching. It names the high-level return shapes but does not detail the LeaderboardEntry structure. This is adequate for correct invocation but not fully complete due to the absence of an 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 coverage is 100%, so a baseline of 3 applies. The description adds semantic value by explicitly linking category values to the return type (highlights object vs LeaderboardEntry array), going beyond the schema's enum listing. Other parameters are left to the schema, but this added context for the most behavior-impacting parameter justifies a 4.

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 identifies the tool as retrieving a ticker leaderboard for a given date, and distinguishes it from sibling tools like get_ticker_snapshot or get_ticker_history by focusing on leaderboard rankings. It also explicitly names the two possible response shapes, leaving no ambiguity about the tool's 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 when a ticker leaderboard is needed, but it does not explicitly state when to prefer this tool over alternatives or mention exclusions (e.g., only 'crypto' snapshot_type). The conditional category explanation helps the agent choose parameters, but no guidance is given on tool selection relative to siblings.

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

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

No annotations are provided, so the description carries the burden. It lists return fields and includes a disclaimer, but does not disclose error behavior, data source, idempotency, or any side effects. For a read operation this is 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 two sentences: the first states purpose and return details, the second is a brief disclaimer. It is front-loaded, concise, and free of 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?

With 5 parameters, no output schema, and no annotations, the description lists return fields but leaves operational nuances (e.g., -USD suffix requirement, date defaults, snapshot_type) to the schema. This is adequate for basic use but not fully complete for a 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 all parameters are documented there. The tool description adds no parameter-level semantics beyond the schema, warranting 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 uses a specific verb 'Get' and resource 'scenario signals for a specific crypto asset', and enumerates the returned signal properties. This clearly distinguishes it from sibling tools like get_ticker_snapshot or get_market_signals.

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

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

The description provides no guidance on when to use this tool versus alternatives, nor any exclusions or conditions. Sibling tool names imply differentiation, but the description itself does not state 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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It describes the output fields in detail, including scales (0-1, 0-10) and components, which informs the agent of the data shape. However, it doesn't explain error behavior, authorization requirements, or side effects, though the 'Get' prefix implies a read-only operation. This is sufficient for a simple retrieval tool 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 a single, information-dense sentence that conveys a large amount of return-value detail efficiently. While it could be improved with bullet points, it is not wasteful and all terms are relevant. It front-loads the purpose before listing metrics.

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 adequately lists the key fields the agent can expect. It covers the core purpose and the primary outputs, and the schema handles parameter details. It doesn't discuss date/version behavior in the description but those are documented in parameter descriptions, so overall it is complete enough for a snapshot 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%, meaning all five parameters have detailed descriptions in the schema. The description does not add parameter-specific information beyond the obvious 'specific crypto asset', so it neither enhances nor detracts from the schema's clarity. 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 clearly states the action ('Get') and the resource ('detailed daily insights snapshot for a specific crypto asset'). It distinguishes itself from siblings like get_ticker_history or get_ticker_signals by enumerating unique metrics such as α-index, α-pulse, and narrative intensity, making its 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?

The description provides a clear context: this is for obtaining a comprehensive daily snapshot of a single asset, with a specific set of alpha and sentiment metrics. It doesn't explicitly mention when not to use it or name alternatives, but the scope is unambiguous enough for an agent to select it over sibling tools.

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

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

With no annotations, the description carries the burden of behavioral transparency. It discloses the search-by-name behavior and the set of returned fields, but omits details such as whether search is exact/partial, case sensitivity, pagination, or rate limits. For a search tool this is minimal but not misleading.

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 communicates the tool's purpose and return fields. There is no redundant information or filler, making it appropriately concise.

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

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

Given the tool's simplicity (1 parameter, no output schema), the description adequately covers the return values, which is essential since there is no output schema. It could mention that multiple results may be returned or how the results are ordered, but the essentials are present.

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

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

The input schema already provides 100% coverage of the 'q' parameter, including a description and examples. The tool description only echoes 'by name' without adding new parameter semantics, so it adds little value beyond 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 tool's purpose: 'Search for crypto podcasts by name.' It specifies the resource (crypto podcasts) and the action (search), and lists the exact return fields (podcast ID, name, artist, language, x-handle, artwork), distinguishing it from sibling tools that focus on episodes or market 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 compared to alternatives like get_podcast_latest or get_my_favorite_podcasts. It simply states what the tool does, leaving the agent to infer usage context from the search verb alone. No exclusions or alternative recommendations are provided.

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