AudioAlpha

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

With no annotations, the description carries full burden but only states what data is returned, not behavioral traits like permissions, rate limits, or error handling. It mentions 'α-sentiment score (crypto)' and 'traditional markets sentiment' but doesn't explain their significance or how they're derived, leaving gaps in transparency.

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

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

The description is a single, efficient sentence that front-loads the purpose and lists key metadata fields. It avoids unnecessary words, though the inclusion of detailed sentiment scales adds slight complexity without full explanation.

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

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

Given no annotations and no output schema, the description partially compensates by listing returned fields, but it lacks details on format, structure, or behavioral context. For a simple read tool, it's adequate but has clear gaps in completeness for an agent's understanding.

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

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

Schema description coverage is 100%, with the parameter 'episode_id' documented in the schema. The description adds no additional parameter semantics beyond implying it's for a specific episode, so it meets the baseline of 3 without compensating for any gaps.

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

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

The description clearly states the verb 'Get' and resource 'metadata for a specific episode', listing specific fields like title, publish date, and sentiment scores. It distinguishes from siblings by focusing on metadata rather than full content, quotes, or transcripts, though it doesn't explicitly name alternatives.

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

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

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

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

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

No annotations are provided, so the description carries the full burden. It mentions the tool 'Get full episode data' but does not disclose behavioral traits such as whether it's read-only, has rate limits, authentication needs, error handling, or response format. The description is minimal and lacks critical operational details.

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

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

The description is a single, efficient sentence that front-loads the key information: 'Get full episode data in one call'. Every word earns its place, with no wasted text, making it highly concise and well-structured.

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

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

Given the complexity of a tool that aggregates multiple data types (details, summary, quotes, asset mentions), no annotations, and no output schema, the description is incomplete. It lacks information on response structure, error cases, or any behavioral context, leaving significant gaps for an AI agent to use it effectively.

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

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

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

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

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

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

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

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

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

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it describes what data is returned, it doesn't cover important aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or pagination behavior for large result sets. The description is purely functional without operational context.

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

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

The description is a single, efficient sentence that packs substantial information: the action, resource scope, and key data fields. Every word earns its place with zero redundancy or fluff. It's appropriately sized for a simple retrieval tool with one parameter.

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

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

For a simple read operation with one parameter and no output schema, the description adequately covers the core functionality. However, without annotations or output schema, it should ideally provide more behavioral context (like safety, performance, or error handling). The description meets minimum requirements but leaves gaps in operational understanding.

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

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

Schema description coverage is 100%, with the single parameter 'episode_id' clearly documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., format examples, validation rules, or where to find episode IDs). With complete schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'all quotes extracted from a specific episode', specifying the data fields included (speaker, α-sentiment score, associated ticker). It distinguishes from siblings like get_episode_details, get_episode_summary, or get_episode_transcript by focusing exclusively on quotes with sentiment and ticker data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like get_ticker_featured_quotes or get_market_featured_quotes, which might offer similar quote data in different contexts. There are no prerequisites, exclusions, or comparative usage hints.

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

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

No annotations are provided, so the description carries full burden. It mentions what is returned (transcript summary, assets, α-sentiment) but lacks behavioral details such as permissions needed, rate limits, error handling, or whether it's a read-only operation. For a tool with no annotations, this is insufficient disclosure of behavioral traits.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes key details (assets, α-sentiment). There is no wasted verbiage, and it is appropriately sized for the tool's complexity.

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

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

Given no annotations, no output schema, and a simple input schema, the description is moderately complete. It specifies the return content but lacks details on output format, error cases, or behavioral context. For a tool with minimal structured data, it provides basic information but leaves gaps in understanding how to use it effectively.

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

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

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

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

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

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

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

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

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

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

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

No annotations are provided, so the description carries full burden. It mentions plan restrictions (Pro and Enterprise only), which is useful behavioral context. However, it lacks details on rate limits, authentication needs, error conditions, or what the output format looks like (e.g., structured text, JSON). For a tool with no annotations, this is a significant gap.

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

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

The description is two concise sentences that efficiently convey the tool's purpose and access restrictions. It is front-loaded with the core functionality, and each sentence adds value without redundancy.

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

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

Given no annotations and no output schema, the description is incomplete. It covers the purpose and access restrictions but lacks crucial details like return format (e.g., whether it's plain text, structured data with timestamps), error handling, or prerequisites (e.g., how to obtain episode_id). For a tool with rich potential output (transcript with speaker mapping), more context is needed.

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

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

Schema description coverage is 100%, with the single parameter 'episode_id' documented in the schema. The description does not add any meaning beyond what the schema provides (e.g., it doesn't explain what an episode ID is or where to find it). Baseline is 3 when schema coverage is high.

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

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

The description clearly states the verb 'Get' and the resource 'full transcript for a specific episode', with additional details about speaker diarization and name mapping. It distinguishes from siblings like get_episode_details or get_episode_summary by specifying transcript content, but does not explicitly contrast with get_episode_full (which might include transcript).

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

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

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

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data returned (podcast name, sentiment score, transcript summary) but does not address key behaviors like pagination, rate limits, authentication needs, error handling, or whether the date parameter is optional (implied by schema but not explicitly stated in description).

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes essential details without redundancy. Every part earns its place by specifying the action, resource, date scope, and returned data fields.

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

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

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and data returned but lacks behavioral context (e.g., how results are structured, error cases), which is needed for full agent understanding without annotations or output schema.

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

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

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

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

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

The description clearly states the specific action ('Get all crypto podcast episodes'), resource ('published on a given date'), and scope ('including podcast name, α-sentiment score (0-10 scale), and transcript summary'), distinguishing it from siblings like get_episode_details or get_podcast_episodes by focusing on market-related episodes with sentiment and summary data.

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

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

The description implies usage for retrieving episodes by date with specific data fields, but lacks explicit guidance on when to use this tool versus alternatives like get_market_snapshot or get_podcast_episodes, and does not mention prerequisites or exclusions.

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

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

With no annotations provided, the description carries full burden. It discloses quotes are ranked by relevance and date-specific, but does not mention response format, authentication needs, rate limits, or edge cases (e.g., missing data). Minimal disclosure for a GET 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 sentence with 15 words, front-loading the core action and key attributes. Every word is purposeful, with no redundancy 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?

There is no output schema, so the description should explain the return structure, but it only vaguely mentions 'top featured quotes'. It also ignores three of four parameters (version_info, snapshot_type, version_number) in the description. This is insufficient for a tool with multiple optional parameters.

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 schema already documents all four parameters. The description does not add meaning beyond what parameters do; it only mentions 'date' implicitly. A baseline of 3 is appropriate as the description does not detract but adds no new parameter context.

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

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

The description states a specific verb 'Get' and resource 'top featured quotes from crypto podcasts', with constraints 'for a given date' and ordering 'ranked by relevance score'. It clearly distinguishes from siblings like get_ticker_featured_quotes which target tickers instead of 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 implies usage for fetching featured podcast quotes by date but lacks explicit guidance on when to use this versus alternatives like get_market_snapshot or get_ticker_featured_quotes. No when-not-to-use or recommended scenarios are provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses the components of a signal (type, horizon, etc.) and includes a disclaimer. However, it does not mention behavior when optional parameters are omitted (e.g., 'date' returns latest), rate limits, or authentication needs.

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

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

Two sentences, front-loaded with the primary function in the first sentence. Every word serves a purpose, and the disclaimer is appropriately appended. No unnecessary information.

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

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

Given complete schema coverage and no output schema, the description adequately explains the tool's purpose and return content. It could be improved by explicitly stating the scope ('across all assets') more prominently, but it is already implied.

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 schema already documents all parameters. The description does not add additional parameter-level meaning; it only describes the return fields. Therefore, the description adds no value beyond the schema for 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 the specific verb 'Get' and clearly identifies the resource as 'scenario strategic signals across all assets' for a given date. It lists the signal components (type, horizon, etc.), distinguishing it from sibling tools like 'get_ticker_signals' which operate on a single 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 does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'get_ticker_signals' for single-asset signals). There are no conditions or exclusions mentioned, leaving the agent to infer usage from context.

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

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

With no annotations, the description clearly explains version fallback behavior and the current limitation of snapshot_type. It lists all return fields, providing full transparency with no contradictions.

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 long sentence that efficiently lists all return fields. It is clear but could benefit from structured formatting like bullet points for easier reading.

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 fully covers all return fields, compensating for the lack of an output schema. However, it does not address edge cases like missing data for a given date.

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

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

Schema coverage is 100%, so baseline is 3. The main description does not add parameter-specific meaning beyond what the schema already provides, but it provides overall context.

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

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

The description clearly states the tool retrieves a daily crypto market snapshot with a detailed list of returned fields. It specifically identifies the resource (daily narrative insight report) and differentiates from sibling tools like get_market_episodes 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 implies usage for daily market snapshots and notes that 'tradfi' is reserved for future use. However, it does not explicitly state when to use this tool versus 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?

No annotations are provided, so the description carries full burden. It describes the output fields (title, summary, etc.) but omits any behavioral traits like authentication needs, rate limits, or error handling. The 'get' prefix implies read-only, but this is not explicit.

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 that efficiently conveys purpose and output. It is front-loaded but could benefit from slight structuring, though not necessary 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?

Given no output schema and no annotations, the description adequately covers purpose and output fields. The 100% schema coverage compensates for missing details like defaults and limitations. However, it could mention that date defaults to latest or that snapshot_type defaults to 'crypto'.

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 all parameters described. The description adds no parameter-specific information beyond the schema, so 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 retrieves narrative themes from the crypto podcast space, specifying the exact resource and context. It distinguishes from sibling tools like get_market_snapshot or get_episode_details as no other tool mentions '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 for obtaining narrative themes on a given date but does not explicitly exclude alternatives or provide when-to-use guidance. The unique resource name 'themes' indirectly distinguishes it from 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 present, so description bears full burden. Discloses that it returns the latest authoritative snapshot per asset with 'is_latest' field, but does not mention authentication requirements, rate limits, or other behavioral traits like pagination.

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

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

Two concise sentences with front-loaded action. First sentence states what it does, second adds critical behavioral detail. No unnecessary words.

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

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

Given the tool's simplicity (no required params, no output schema), the description and schema cover main purpose and snapshot freshness. However, it omits response structure details (e.g., list format, possible pagination) that could aid agent understanding.

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

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

Schema coverage is 100%, so baseline is 3. Description adds no new parameter meaning beyond the schema's detailed descriptions. The mention of 'latest authoritative snapshot' does not directly elaborate on parameters.

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

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

Clearly specifies verb 'Get list', resource 'crypto assets the authenticated user has favorited', and distinguishes from sibling 'get_my_favorite_podcasts' by focusing on crypto. Also mentions unique 'is_latest' snapshot property.

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?

Implies usage for retrieving the user's favorite crypto assets, but lacks explicit guidance on when to choose this over similar tools like 'get_my_favorite_podcasts' or market tools. No when-not-to-use or alternative mention.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'authenticated user', implying an auth requirement, but doesn't disclose other behavioral traits such as rate limits, pagination, error handling, or what the return format looks like (e.g., list structure). This is a significant gap for a tool with zero annotation coverage.

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

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

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

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

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

Given the tool's simplicity (0 parameters, no output schema), the description is adequate but has clear gaps. It lacks details on behavioral aspects like authentication specifics, return format, or error cases, which are important for a tool with no annotations. It meets the minimum viable standard but doesn't fully compensate for the missing structured data.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately doesn't discuss parameters, and the baseline for this scenario is 4, as it avoids redundancy while being complete for a parameterless tool.

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

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

The description clearly states the verb ('Get') and resource ('list of podcasts the authenticated user follows'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_my_favorite_assets' or 'get_my_feed', which might also involve user-specific data, leaving room for ambiguity.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., authentication), exclusions, or comparisons to siblings like 'get_my_favorite_assets' or 'search_podcasts', leaving the agent to infer usage context.

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

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

With no annotations, the description carries the full burden. It explains the return structure in detail and states it always uses the latest authoritative snapshot, indicating no historical data. However, it does not disclose authorization requirements or potential side effects, which are minimal 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 concise with two front-loaded sentences covering purpose and return structure. No superfluous words.

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

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

Given no output schema, the description thoroughly documents the return fields (snapshot_date, assets with sub-fields). It does not mention pagination or error cases, but for a read operation with no parameters beyond those given, it 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?

Schema coverage is 100% with each parameter already well-described. The description adds little beyond what the schema 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 gets a personalized feed based on the user's favorite assets and followed podcasts, distinguishing it from sibling snapshot tools like get_market_snapshot and get_ticker_snapshot which are not personalized.

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 personalized feeds but does not explicitly mention when not to use it or alternatives. For example, it lacks guidance that for individual ticker snapshots get_ticker_snapshot should be used.

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

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

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

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

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

The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes a useful contextual note about the podcast ID source. Every part of the sentence earns its place.

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

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

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

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

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

Schema description coverage is 100%, so the schema fully documents both parameters (podcast_id and limit). The description adds no additional parameter semantics beyond what's in the schema, such as explaining podcast ID formats or 'recent' criteria. The baseline of 3 is appropriate when the schema does all the work.

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

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

The description clearly states the action ('Get recent episodes') and target resource ('for a specific podcast by podcast ID'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_podcast_latest' or 'get_podcast_latest_full', which likely have overlapping functionality.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_podcast_latest' or 'search_podcasts'. It mentions the podcast ID comes from 'search_podcasts', which is helpful context but doesn't constitute explicit usage guidelines or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It mentions the data returned but does not disclose behavioral traits such as error handling, rate limits, authentication needs, or whether it's a read-only operation. This is a significant gap for a tool with no annotation coverage.

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

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

The description is a single, dense sentence that efficiently lists all key information—action, resource, and returned data—with zero wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

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

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

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

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

Schema description coverage is 100%, with the parameter podcast_id documented as 'Podcast ID from search_podcasts'. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.

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

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

The description clearly states the action ('Get the latest episode') and the specific resource ('for a specific podcast'), distinguishing it from sibling tools like get_podcast_episodes (which retrieves multiple episodes) and get_episode_details (which requires an episode_id). It also specifies the data returned, including unique elements like α-sentiment for crypto markets.

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

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

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

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

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

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

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

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

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

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

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

Given no annotations, no output schema, and a simple input schema, the description provides basic purpose and return content hints. However, it lacks details on behavioral traits, error cases, or output structure, making it minimally adequate but incomplete for informed tool selection and invocation.

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

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

Schema description coverage is 100%, with the parameter 'podcast_id' documented in the schema as 'Podcast ID from search_podcasts'. The description doesn't add any extra meaning beyond this, such as format examples or constraints, so it meets the baseline of 3 for high schema coverage without compensating value.

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

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

The description clearly states the action ('Get') and resource ('latest episode for a podcast'), specifying it returns 'full details including quotes and asset mentions'. It distinguishes from siblings like 'get_podcast_latest' (implied less detail) and 'get_episode_full' (requires episode ID vs. podcast ID), but doesn't explicitly name alternatives.

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

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

No explicit guidance on when to use this tool versus alternatives like 'get_podcast_latest' or 'get_episode_full'. The description implies it's for the latest episode with full details, but doesn't specify prerequisites, exclusions, or comparative contexts with siblings.

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

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

With no annotations, the description partially describes behavioral traits: it mentions the output includes sentiment scores on a 0-10 scale and ranking by selection score. However, it omits details like data freshness, rate limits, or what happens when the date is omitted (schema says 'omit for latest'). It does not contradict any annotations.

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

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

The description is a single, front-loaded sentence that conveys the core purpose and key features (sentiment scores, ranking) without any fluff. Every phrase earns its place.

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

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

Given no output schema, the description hints at response content (sentiment scores, rank), but does not list all expected fields (e.g., quote text, source). The 5 parameters are well-documented in the schema, so the description provides sufficient context for an agent to understand the tool's purpose and basic output structure.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds minimal additional meaning beyond the schema—it only implies date and ticker usage. The schema already provides detailed descriptions for all parameters (e.g., ticker format, version_info, snapshot_type).

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

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

The description uses a specific verb ('Get') and resource ('featured podcast quotes') with clear qualifiers ('for a specific crypto asset on a given date, with α-sentiment scores, ranked by selection score'). This distinguishes it from sibling tools like get_market_featured_quotes (market-level) and get_episode_quotes (episode-level).

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

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

The description implies usage for a specific crypto asset and date, but does not explicitly state when to choose this tool over alternatives (e.g., get_market_featured_quotes) or when not to use it. 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.

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

No annotations are provided, so the description carries full responsibility. It discloses tier caps on `days`, silent capping, date parameter ignored for lower tiers, ticker format requirements, and response order (oldest first). It also mentions version_info behavior, providing complete behavioral insight for a read-only data tool.

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

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

The description is well-structured with paragraphs for purpose, requirements, tier caps, and response. It front-loads the core use case. While slightly verbose (e.g., listing all tier caps again), every sentence adds value. Minor trimming could improve conciseness.

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

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

Given 5 parameters, no output schema, and no annotations, the description covers behavior thoroughly: returns array of snapshots with fields, includes metadata in response, and explains tier limitations. It assumes knowledge of AssetSnapshot fields, which is reasonable. Missing details on error handling (empty response) but complete enough for typical usage.

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

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

Schema coverage is 100%, so baseline is 3. The description adds marginal value beyond schema by explaining the tier-specific behavior of the `date` parameter and reinforcing the '-USD' suffix rule. However, it largely repeats schema descriptions without deeper semantics.

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

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

The description clearly states 'Fetch a historical time series of daily snapshots for one crypto ticker' and provides specific use cases (trend, charting sentiment/index/pulse, computing averages/momentum/volatility). It differentiates from 21 sibling tools focused on episodes, snapshots, and signals, making it easy for an agent to select this tool.

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 says 'Call this when...' and lists several scenarios. It also provides key usage constraints (ticker suffix '-USD', tier caps, date parameter honor). It lacks explicit when-not-to-use or alternative sibling names, but the context is clear for an informed agent.

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

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

No annotations exist, so the description carries full transparency burden. It discloses the two response structures and categories, which covers primary behavior. However, it omits potential behaviors like error states, response limits beyond the implicit limit parameter, or data freshness guarantees. Adequate but not exhaustive.

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, logically organized: first states purpose, second explains dual behavior. It is efficient with no wasted words. Could be slightly improved by using bullet points for the two modes, but current structure is clear and 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?

No output schema exists, so the description must cover return structure. It does so for both modes, listing fields in highlights and noting array return for categories. However, it doesn't describe the LeaderboardEntry fields, pagination, or error handling. Adequate for basic use but incomplete for a comprehensive understanding.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining how the 'category' parameter transforms the response structure and that omitting date gives latest. For other parameters (limit, version_info, snapshot_type), the description adds nothing beyond schema. Overall, marginal additional meaning.

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

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

The description clearly states 'Get ticker leaderboard for a given date' and immediately distinguishes between the two modes (without category returns highlights, with category returns array). It specifies exact fields in highlights, leaving no ambiguity. No sibling tool duplicates this functionality.

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

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

The description explains when to use each mode (omit category for top-1 highlights, include category for full list). It does not explicitly mention when not to use this tool or provide alternatives, but the context of sibling tools implies this is the single source for leaderboard data. Clear usage context is given.

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 only includes a financial disclaimer and lists return fields, but does not mention side effects, rate limits, authentication needs, or other critical behaviors. For a read-only tool, this is insufficient.

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 three well-structured sentences. The first sentence states the core purpose, the second enumerates return fields, and the third adds a disclaimer. No wasted words; effectively front-loaded.

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

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

Given the tool has 5 parameters, no output schema, and a sibling set including similar tools, the description is adequate but not thorough. It explains what the tool returns but fails to clarify typical use cases, constraints, or how to interpret the signals. Missing details like whether it can be used for historical or latest signals.

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 description adds minimal value beyond the schema. It mentions return fields but does not elaborate on parameter usage beyond what the schema already provides. 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 retrieves scenario signals for a specific crypto asset and lists the returned fields (signal type, time horizon, scenario description, etc.). It uses a specific verb ('Get') and resource ('scenario signals'), distinguishing it from siblings like get_market_signals which operate at market level.

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

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

The description provides no guidance on when to use this tool versus alternatives such as get_market_signals or get_ticker_snapshot. It lacks explicit conditions, prerequisites, or exclusions.

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

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

Without annotations, the description carries the burden. It details what the tool returns but does not mention error handling, auth requirements, or side effects. The schema's IMPORTANT note about ticker suffix is not reflected in the description, but the description is otherwise 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?

Description is a single paragraph listing many metrics. It is somewhat dense but not overly long. Could be more concise by grouping related metrics, but it is adequately front-loaded with 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 complexity (5 parameters, no output schema), the description covers the return fields but lacks explanation of structure or relationships. It provides a reasonable overview but leaves details to 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 description adds limited value beyond parameter documentation. The description lists return fields but does not enhance parameter meaning beyond what the schema already provides.

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

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

Clearly states the tool returns a detailed daily insights snapshot for a crypto asset, listing many specific metrics. Differentiates from sibling tools like get_ticker_history by focusing on aggregated daily snapshot 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?

No explicit guidance on when to use this tool versus alternatives like get_ticker_signals or get_ticker_history. The description implies its use for daily aggregated insights but does not provide context or exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns specific fields (podcast ID, name, etc.), which adds some context about output. However, it doesn't cover critical behaviors like whether this is a read-only operation, potential rate limits, error conditions, or pagination for large result sets. For a search tool with zero annotation coverage, this is a significant gap.

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

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

The description is highly concise and front-loaded, consisting of two sentences that directly state the action and return values without any fluff. Every sentence earns its place by providing essential information efficiently, making it easy for an agent to parse quickly.

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

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

Given the tool's moderate complexity (a search function with one parameter) and no annotations or output schema, the description is minimally adequate. It covers the purpose and return fields but lacks details on behavioral traits, usage guidelines, and error handling. Without an output schema, it partially compensates by listing return values, but gaps remain for effective agent use.

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

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

The input schema has 100% description coverage, with the parameter 'q' documented as 'Podcast name search query e.g. unchained, bankless, chopping block.' The description adds no additional parameter semantics beyond what the schema provides, such as search syntax or matching rules. With high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Search for crypto podcasts by name.' It specifies the verb ('Search'), resource ('crypto podcasts'), and scope ('by name'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'get_my_favorite_podcasts' or 'get_podcast_episodes,' which could also retrieve podcast information, so it misses full sibling distinction.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare it to sibling tools such as 'get_my_favorite_podcasts' for user-specific data or 'get_podcast_episodes' for episode listings. This lack of context leaves the agent to infer usage based on the name alone.

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