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 effectively describes key traits: the tool returns structured data in table format, supports various report types (implied by examples), and references pagination via the 'limit' parameter. However, it doesn't explicitly mention rate limits, authentication needs, or error handling, leaving some gaps for a tool with 8 parameters.

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

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

The description is well-structured and front-loaded, starting with the core purpose, followed by usage guidelines, return format, examples, and sibling tool references. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 (8 parameters, no output schema, no annotations), the description does a good job of covering purpose, usage, and behavioral context. It includes examples and references to sibling tools, but lacks details on output structure (beyond 'structured data with headers/rows') and error cases, which could be more complete for a reporting 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 schema already documents all 8 parameters thoroughly. The description adds minimal parameter-specific semantics beyond what's in the schema (e.g., implying 'report_type' options through examples), but it doesn't provide additional syntax or format details. Baseline 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 with specific verbs ('Get detailed analytics') and resource ('in TABLE format for reporting'), distinguishing it from siblings like 'get_analytics_timeseries' for charts/graphs. It explicitly mentions the format and use case, making it highly specific and differentiated.

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

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

The description provides explicit usage guidelines with a 'USE WHEN:' section listing scenarios (e.g., creating reports, comparing metrics) and clear alternatives: 'Use list_analytics_capabilities to see all 60+ report types' and 'For charts/graphs, use get_analytics_timeseries instead.' This gives comprehensive guidance on when 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 full burden of behavioral disclosure. It indicates this is a read operation ('Get') and specifies the data format ('optimized for charts and visualizations'), but doesn't mention rate limits, authentication requirements, pagination, or error conditions. It provides some context about the tool's specialized purpose but lacks comprehensive behavioral 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 perfectly concise with just two sentences that each earn their place. The first sentence states the purpose and specialization, the second provides clear usage guidelines. No wasted words, and the most important information is front-loaded.

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

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

For a tool with 6 parameters, no annotations, and no output schema, the description provides good purpose and usage context but lacks details about the return format, data structure, or what 'optimized for charts' actually means in practice. Given the complexity and absence of output schema, more information about the response format would be helpful for complete 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%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

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

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

The description clearly states the tool's purpose with specific verbs ('Get time-series analytics data') and distinguishes it from siblings by specifying it's 'optimized for charts and visualizations'. It explicitly differentiates from general analytics tools like 'get_analytics' by focusing on time-series data for visualization purposes.

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

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

The description provides explicit usage guidance with 'Use this when creating graphs, dashboards, or tracking trends over time.' This gives clear context for when to select this tool versus alternatives like 'get_analytics' (general analytics) or 'get_realtime_metrics' (real-time data).

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (downloads or reads attached files), mentions the return behavior ('RETURNS: File content (if text) or download URL'), and provides context about prerequisites (needs attachment ID from list_attachment_assets). However, it doesn't mention potential limitations like file size restrictions, authentication requirements, or rate limits.

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

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

The description is efficiently structured with clear sections (purpose, usage guidelines, returns, examples, prerequisites), uses bullet-like formatting without actual bullets, and every sentence adds value. It's appropriately sized for the tool's complexity and front-loads the most important 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 the tool's moderate complexity (single parameter, no annotations, no output schema), the description is quite complete. It covers purpose, usage scenarios, return values, examples, and prerequisites. The main gap is the lack of output schema, so the description must describe returns, which it does adequately but could be more specific about format distinctions between text content and download URLs.

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

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

Schema description coverage is 100%, so the schema already fully documents the single parameter (attachment_asset_id). The description adds minimal value beyond the schema by mentioning that the ID comes from 'list_attachment_assets' and giving a format example, but this is essentially redundant with the schema's description. Baseline 3 is appropriate when 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 specific action ('Download or read') and resource ('ATTACHED FILES from videos'), distinguishing it from siblings like get_caption_content (captions) or get_download_url (general downloads). It explicitly mentions attachment files, which differentiates it from other media-related tools.

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

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

The description provides explicit guidance with 'USE WHEN:' listing specific scenarios (accessing supplementary materials, downloading PDFs, etc.), includes an alternative tool ('Use after list_attachment_assets to get specific attachment ID'), and gives concrete examples ('Download the PDF slides', 'Read the attached notes'). This clearly tells the agent when and how to use this tool.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by specifying what the tool returns ('Full caption text and download URL') and providing example use cases. However, it doesn't mention potential limitations like file format, size constraints, or authentication requirements that would be helpful for a download 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 efficiently structured with clear sections (purpose, usage guidelines, returns, examples, prerequisites). Every sentence adds value with no redundant information. The front-loaded purpose statement immediately communicates the tool's function.

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

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

For a single-parameter tool with no output schema, the description provides good context about what the tool does, when to use it, and what it returns. It could be more complete by specifying output format details or potential error conditions, but given the tool's relative simplicity and the absence of annotations, it covers most essential information.

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

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

The schema description coverage is 100%, so the schema already documents the single parameter. The description adds some context by mentioning the parameter comes from 'list_caption_assets' and provides format examples, but doesn't add significant semantic meaning beyond what the schema provides. This meets the baseline 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 purpose with specific verbs ('Get actual CAPTION TEXT or download captions file') and identifies the resource (caption content). It distinguishes from sibling tools by specifying this is for caption content retrieval rather than analytics, attachments, or other media assets.

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

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

The description provides explicit usage guidance with a 'USE WHEN:' section listing specific scenarios (reading transcripts, downloading subtitles, analyzing content, creating accessible content). It also mentions when to use it in relation to a sibling tool ('Use after list_caption_assets to get specific caption ID'), giving clear context and 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively reveals key traits: the tool returns a 'Time-limited secure URL' (implying expiration and authentication needs) and is for 'saving files' (clarifying the download intent). However, it doesn't mention rate limits, error conditions, or file size considerations.

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 efficiently structured with clear sections (purpose, usage guidelines, returns, examples, differentiation) in just four sentences. Every sentence adds value: the first states the core function, the second provides usage context, the third describes the return value, and the fourth gives examples and differentiation.

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

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

For a tool with no annotations and no output schema, the description does well by explaining the return value ('Time-limited secure URL') and providing usage guidance. However, it could be more complete by detailing the URL's expiration timeframe or error cases, given the mutation-like nature of generating download links.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters (entry_id format, flavor_id optionality and purpose). The description adds no additional parameter semantics beyond what's in the schema, maintaining 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 specific action ('Get direct DOWNLOAD link') and resource ('for video files'), distinguishing it from sibling tools like get_thumbnail_url (thumbnails) or get_media_entry (metadata). It explicitly contrasts with 'streaming' to clarify this is for saving files, not playback.

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 an explicit 'USE WHEN:' section listing four concrete scenarios (download/save locally, export for editing, backup content, share downloadable link) and provides two examples. It also differentiates from streaming, giving clear context for when to choose this tool over 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 of behavioral disclosure. It effectively describes what the tool returns (views/viewers by geographic units with percentages), mentions 'map-ready data' as a behavioral trait, and provides example queries. However, it doesn't mention potential limitations like data latency, access permissions, or rate limits.

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

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

The description is well-structured and front-loaded with purpose, usage guidelines, returns, and examples in distinct sections. Every sentence earns its place by providing essential information without redundancy. The formatting with clear sections enhances readability.

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

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

For a tool with 5 parameters, 100% schema coverage, but no annotations or output schema, the description provides good contextual completeness. It covers purpose, usage scenarios, return format, and examples. The main gap is the lack of output schema, but the description compensates by describing what's returned.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add significant parameter semantics beyond what's in the schema, though it reinforces the geographic focus. The baseline of 3 is appropriate when 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 with specific verbs ('analyze viewer locations and regional performance') and distinguishes it from siblings by focusing on geographic breakdown rather than general analytics, time-series, or other media-specific operations. It explicitly mentions the resource being analyzed (viewer locations).

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

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

The description provides explicit usage guidance with a 'USE WHEN:' section listing five specific scenarios (understanding global reach, planning regional strategies, checking market penetration, optimizing CDN, compliance checks). This gives clear context for when to select this tool over alternatives like general analytics 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 provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns ('complete metadata including URLs, dimensions, creation date') and implies it's a read-only operation by using 'Get' and providing an example of usage after search. However, it doesn't mention potential errors (e.g., invalid entry_id), rate limits, or authentication needs, leaving some behavioral aspects uncovered.

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 efficiently structured with three distinct parts: purpose statement, usage guidelines, and an example. Each sentence adds value without redundancy, and it's front-loaded with the core purpose. The total length is appropriate for the tool's complexity, 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 output schema, no annotations), the description is nearly complete. It covers purpose, usage, returns, and provides an example. The main gap is the lack of output schema, so the description doesn't specify the exact structure of returned metadata, but it lists key fields (title, description, etc.), which mitigates this. For a simple read tool, this is sufficient but not exhaustive.

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

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

Schema description coverage is 100%, with the schema already documenting the entry_id parameter's type, format, and requirement. The description adds minimal value beyond this by mentioning entry_id in the 'USE WHEN' section and example, but doesn't provide additional semantic context (e.g., where to obtain entry_id from, or validation rules). This meets the baseline 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 specific action ('Get complete metadata') and resource ('a single video/media file'), distinguishing it from siblings like search_entries (which finds IDs) or get_thumbnail_url (which retrieves only thumbnails). It explicitly mentions what details are included (title, description, duration, tags, thumbnail, status), making the 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 includes an explicit 'USE WHEN' section that specifies when to use this tool ('You have a specific entry_id and need full details') and provides an example contrasting with search_entries. This clearly guides the agent on when to choose this tool over alternatives like search_entries (for finding IDs) or other get_* tools that return partial data.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It does well by specifying what the tool returns (buffer rates, bitrate averages, error rates, startup times, quality scores) and its purpose (analyzing quality, troubleshooting, monitoring). However, it doesn't mention potential limitations like data freshness, rate limits, or authentication requirements, leaving some behavioral aspects unspecified.

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

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

The description is well-structured and appropriately sized, using clear sections (purpose, usage guidelines, returns, examples) with zero wasted sentences. Each section adds distinct value, and the information is front-loaded with the core purpose stated first.

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 read-only analysis tool with 5 parameters and no output schema, the description provides good contextual completeness by explaining what the tool does, when to use it, what it returns, and giving examples. However, without annotations or output schema, it could benefit from more detail about the return format structure or data interpretation.

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

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

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 since the schema does the heavy lifting for parameter documentation.

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

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

The description clearly states the tool's purpose with specific verbs ('analyze streaming QUALITY and viewer experience') and resources ('streaming quality metrics'), distinguishing it from sibling tools like get_analytics or get_realtime_metrics by focusing specifically on quality metrics rather than general analytics or real-time 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 explicitly provides usage guidelines with 'USE WHEN:' section listing specific scenarios (troubleshooting playback issues, monitoring streaming performance, optimizing delivery, investigating viewer complaints), and includes examples that illustrate appropriate contexts, giving clear guidance on when to use this tool.

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 effectively describes key traits: real-time nature ('updating every 30 seconds'), scope ('LIVE analytics'), and return values. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, which would be helpful for a real-time monitoring 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 clear sections (purpose, usage guidelines, returns, examples, differentiation) and every sentence adds value. It's appropriately sized for a tool with real-time complexity, front-loading key information without unnecessary elaboration.

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 real-time monitoring complexity and lack of output schema, the description does well by explaining return values and providing usage examples. However, without annotations and with behavioral aspects like rate limits or error handling unaddressed, there's room for improvement in fully preparing an agent for 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%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3. The 'RETURNS' section describes output values but doesn't clarify how they map to parameters.

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

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

The description clearly states the tool's purpose with specific verbs ('Get LIVE analytics') and resources ('analytics updating every 30 seconds'), distinguishing it from siblings like 'get_analytics' or 'get_analytics_timeseries' by emphasizing real-time nature. The final sentence explicitly differentiates it from historical analytics.

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

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

The description provides explicit usage guidelines with a dedicated 'USE WHEN:' section listing specific scenarios (monitoring live events, building real-time dashboards, tracking campaign impact, detecting issues). It also states when not to use it ('Different from historical analytics - this is NOW'), offering clear 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 of behavioral disclosure. It effectively describes key behaviors: it returns an image URL, allows size specification, and can capture frames from any timestamp. However, it doesn't mention potential limitations like rate limits, authentication needs, or error conditions, which would be helpful 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 well-structured with clear sections (purpose, usage, returns, examples) and is appropriately sized. However, the examples could be more concise, and some phrasing ('Can capture any frame from video') is slightly redundant with earlier content, preventing a perfect score.

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 (4 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, returns, and examples, but lacks details on output format beyond 'Image URL' and doesn't address error handling or constraints, which would enhance completeness for a tool with no structured output or annotations.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'specified size' and 'any frame from video', which align with width/height and second parameters but don't provide additional semantic context. This meets the baseline 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 purpose with specific verbs ('Get video THUMBNAIL/POSTER image') and distinguishes it from siblings by focusing on thumbnail generation rather than analytics, content retrieval, or listing operations. It explicitly identifies the resource (video thumbnail) and the action (get).

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

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

The description provides explicit usage guidance with a 'USE WHEN:' section listing scenarios like displaying previews, creating galleries, and generating custom thumbnails. It distinguishes from siblings by focusing on visual preview needs rather than data analysis or content access, though it doesn't name specific 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 of behavioral disclosure. It effectively describes key behaviors: it returns '101 data points (0-100%) showing viewer count at each percent of video,' specifies that it 'Shows exact percentages where audience is lost,' and implies analysis rather than mutation. However, it lacks details on rate limits, authentication needs, or data freshness, which are minor gaps.

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

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

The description is well-structured and front-loaded with the core purpose, followed by usage guidelines, return values, and examples. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 5-parameter analytics tool with no annotations and no output schema, the description is largely complete. It covers purpose, usage, return format, and examples. However, it could benefit from more details on output structure or error handling, which slightly reduces completeness.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema, such as implying user_filter allows comparison of 'anonymous vs logged-in users' in the examples. This meets the baseline of 3, 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 explicitly states the tool's purpose: 'Analyze WHERE viewers stop watching in a video.' It specifies the verb ('analyze') and resource ('video retention'), and clearly distinguishes it from siblings like get_analytics or get_quality_metrics by focusing on viewer retention patterns rather than general analytics or technical metrics.

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

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

The description provides explicit usage guidelines with 'USE WHEN: Optimizing video content, finding boring sections, identifying engaging moments, improving completion rates.' It lists specific scenarios for when to use this tool, and the examples further clarify its application, such as comparing retention for different user segments.

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

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

No annotations are provided, so the description carries full burden. It discloses behavioral traits: returns a complete list with details (7 functions, 60+ reports, dimensions, intervals), and states no parameters needed. However, it lacks info on rate limits, auth needs, or error handling, leaving some gaps.

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?

Front-loaded with purpose, followed by usage guidelines, returns, and example. Sentences are efficient, but could be slightly more streamlined (e.g., combining some usage examples). Overall, minimal waste 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 0 parameters, no annotations, and no output schema, the description provides good context: purpose, usage, return details, and example. However, it doesn't specify output format or error cases, leaving some completeness gaps for a tool with no structured output info.

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?

With 0 parameters and 100% schema coverage, the baseline is 4. The description adds value by explicitly stating 'No parameters needed - just call it!', clarifying that no inputs are required beyond what the empty schema indicates.

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 'Discover' and resource 'ALL analytics capabilities', specifying the scope comprehensively. It distinguishes from siblings like get_analytics (which likely retrieves specific data) by focusing on listing available capabilities rather than fetching analytics 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?

Explicitly provides when-to-use scenarios: 'User asks what analytics can you do?', exploring reports, understanding metrics, learning features. It includes an example: 'Always run this when user first asks about analytics', offering clear guidance on context and 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 and does well by stating it 'RETURNS: List of attached files with names, types, sizes, IDs', disclosing output format. It adds context that 'Attachments are additional files uploaded with videos', clarifying what attachments are. However, it doesn't mention potential limitations like empty results or error conditions.

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 efficiently structured with clear sections (purpose, usage, returns, examples, clarification), uses bullet-like formatting without waste, and is front-loaded with the core action. Every sentence adds value, such as the examples that reinforce usage context.

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 1 parameter (100% schema coverage) and no output schema, the description is nearly complete: it covers purpose, usage, returns, and examples. It lacks details on output structure (e.g., pagination) or error handling, but given the low complexity, this is a minor gap.

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

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

Schema description coverage is 100% (the entry_id parameter is fully described in the schema as 'Video to check for attachments (format: '1_abc123')'), so the baseline is 3. The description doesn't add any parameter-specific information beyond what the schema provides, but it doesn't need to compensate for 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 specific action ('Find FILES ATTACHED to videos') and resource ('videos'), distinguishing it from sibling tools like get_attachment_content (which retrieves content) or get_media_entry (which gets video metadata). It explicitly mentions 'supplementary materials, PDFs, slides, documents' to clarify scope.

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

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

The description explicitly states 'USE WHEN: Looking for supplementary materials, PDFs, slides, documents linked to video' and provides examples like 'What documents are attached to training video?', giving clear context for when to use this tool versus alternatives like get_attachment_content (for file content) or search_entries (for broader video searches).

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 effectively describes what the tool does (lists caption files with languages, formats, IDs), its purpose (checking availability, finding languages), and its role in a workflow (first step before getting content). However, it doesn't mention potential limitations like pagination, error conditions, 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 efficiently structured with clear sections (purpose, usage guidelines, returns, examples, workflow positioning). Every sentence adds value: the first states the core function, the second provides usage contexts, the third specifies returns, and the fourth gives examples and workflow role. No wasted words 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 single-parameter tool with no output schema, the description provides excellent context: clear purpose, usage guidelines, return information, examples, and workflow positioning. The only minor gap is the lack of explicit mention of what happens when no captions exist or error scenarios, but overall it's highly complete for this 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 schema description coverage is 100%, with the single parameter 'entry_id' fully documented in the schema as 'Video to check for captions (format: '1_abc123')'. The description doesn't add any additional parameter semantics beyond what's already in the schema, so it meets the baseline 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 specific action ('Find all CAPTIONS/SUBTITLES for a video') and resource ('video'), distinguishing it from sibling tools like get_caption_content (which retrieves actual content) and list_attachment_assets (which lists different asset types). It explicitly identifies what it returns, making the 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 explicit usage scenarios with 'USE WHEN: Checking if video has captions, finding available languages, preparing for accessibility, getting transcript' and positions it as 'First step before getting caption content', clearly indicating when to use this tool versus alternatives like get_caption_content. This gives comprehensive guidance on appropriate contexts.

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 and does well by disclosing key behaviors: returns a tree structure with names, IDs, and parent-child relationships, and provides a practical example of how to use the output. It doesn't mention rate limits or authentication needs, but covers the core operational behavior adequately.

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

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

Well-structured with clear sections (purpose, usage guidelines, returns, example), front-loaded with core purpose, and every sentence adds value without redundancy. The example is practical and illustrative without being verbose.

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

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

For a read-only tool with no annotations and no output schema, the description provides good context: clear purpose, usage guidelines, return format description, and an example. It could mention pagination or default behavior more explicitly, but covers most essential aspects given the tool's complexity.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

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

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

The description clearly states the tool's purpose with specific verbs ('browse content organization hierarchy') and resource ('categories'), distinguishing it from siblings like search_entries or list_attachment_assets by focusing on hierarchical taxonomy rather than media content or analytics.

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

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

Explicit 'USE WHEN' section provides clear scenarios: exploring content structure, finding category IDs for filtering, and understanding taxonomy. It distinguishes this tool from search_entries by emphasizing hierarchy browsing rather than direct content search.

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 'POWERFUL SEARCH across titles, descriptions, tags, captions' and 'primary discovery tool,' which gives some behavioral context (e.g., broad search capabilities). However, it lacks details on permissions, rate limits, pagination, or error handling, leaving gaps for a tool with 11 parameters and no output schema.

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

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

The description is front-loaded with key information (purpose and usage) and uses bullet-like examples efficiently. However, it includes some redundancy (e.g., repeating search capabilities) and could be slightly more streamlined, but overall it's well-structured and earns its place with helpful guidance.

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 (11 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage well but lacks behavioral details (e.g., response format, error cases) and doesn't fully address the tool's richness. It's a minimum viable description that leaves the agent to rely heavily on the schema for execution.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema, such as implying the tool handles 'videos' and 'content' and giving example queries, but it doesn't provide additional meaning or clarify complex interactions between parameters. Baseline 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 with specific verbs ('SEARCH for videos or LIST all content') and resource ('videos'/'content'). It distinguishes this as the 'primary discovery tool' from siblings that appear to be more specific retrieval tools (like get_media_entry, get_caption_content, etc.), making the scope and role 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?

The description explicitly states 'USE WHEN:' with multiple scenarios (finding by keyword, listing newest content, discovering what's available, filtering by date/category). It provides concrete examples and distinguishes this as the go-to tool for discovery, implying alternatives are not needed for these tasks, though it doesn't name specific sibling tools to avoid.

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