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

Annotations indicate a mutation (readOnlyHint=false). Description expands on behavior with return format, error cases (404, 403), and position details. No contradictions with 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?

Well-organized with headings (Args, Returns, Examples, Errors). Every sentence is informative, no redundancy. Efficient and easy to parse.

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

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

Despite no output schema, the description includes the return JSON structure and common errors. It is nearly complete, though it could mention prerequisites like video visibility.

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

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

Schema coverage is 100%, but description adds significant value: clarifies position as zero-based, optional with append behavior, and provides concrete examples. Exceeds baseline.

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

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

Clearly states 'Add a YouTube video to one of your playlists, with optional position.' Verb and resource are specific, and the tool is distinct from siblings like `youtube_remove_playlist_item`.

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. Examples and errors are provided, but there is no direct statement about typical use cases 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?

Annotations already declare readOnly, destructive, idempotent, openWorld hints. Description adds useful behavioral context: returns percentage data, indicates that empty rows may occur if viewership data is insufficient, and explains error 403 requires re-authentication. This is extra value beyond 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?

Well-structured with clear sections: purpose, Args, Returns, Examples, Errors. Front-loaded with the main functionality. Each sentence adds necessary information; no fluff. Efficiently covers all needed aspects in a readable format.

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

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

Despite lacking an output schema, the description includes a full JSON shape of the return value, covering columns and example rows. Handles date ranges, optional parameters, default behavior, and common errors. Sufficient for an agent to understand and invoke the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. Description adds value by providing return shape (JSON with columns/rows), default values, and examples showing parameter usage. It clarifies the meaning of dates and the response_format enum. Slightly redundant with schema but still helpful.

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 retrieves age group and gender distribution of viewers as percentages. Specific verb 'retrieve' and resource 'age and gender distribution' with no ambiguity. Distinguishes itself from sibling analytics tools like youtube_revenue and youtube_traffic_sources.

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

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

Provides example calls with and without arguments, and describes error scenarios. However, no explicit guidance on when to prefer this tool over siblings. The examples implicitly guide usage for date ranges, but lacks 'when not to use' or alternatives.

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

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

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral context: the JSON shape of the response, error handling (403 scope), and the effect of the dimension parameter on output structure. It does not contradict annotations. The added detail on default date range and format enhances transparency.

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

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

The description is well-structured with sections for description, arguments, returns, examples, and errors. It is not overly verbose for the amount of detail provided. Every sentence adds value. However, it could be slightly more concise; some details about default behavior are repeated in both description and schema.

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

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

Given no output schema, the description compensates by providing the exact JSON shape and explaining the markdown output. It covers error handling (403), includes examples, and explains optional parameters with defaults. The tool is well-documented for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by providing usage examples (e.g., 'Daily breakdown of my channel this week'), explaining the effect of dimension on output, and clarifying default behavior. The error note also adds context. This elevates the score above baseline.

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

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

The description clearly states the tool retrieves a 'broad performance summary' for the authenticated channel, listing specific metrics. The verb 'Retrieve' and resource 'channel performance summary' are precise. Among siblings, this is distinct from query-based analytics (youtube_run_analytics_query) and video-specific tools (youtube_top_videos, youtube_video_performance).

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 guidance on when to use the tool (for channel summary), includes optional parameters with defaults, and gives examples. It does not explicitly state when not to use it or suggest alternatives, but the examples and error note indirectly guide usage. A slightly stronger score would require explicit exclusions.

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

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

The description supplements annotations by detailing the return format, error scenarios (empty text, disabled comments, missing video), and the fact that it creates a new resource. Annotations already indicate mutation and non-destructiveness, and the description adds context about what the tool does and its failure modes.

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 and well-structured with clear sections (Args, Returns, Examples, Errors). Every sentence adds value, and the first sentence immediately states the purpose. No redundancy or fluff.

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

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

For a simple write tool with only 2 parameters and no output schema, the description covers the operation, example usage, return data, and common errors. It does not mention authentication or rate limits explicitly, but the error note on quota covers some of that. It is largely complete for the given complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal extra value: it mentions that text supports basic HTML entities, which schema does not. Otherwise, parameter descriptions are nearly identical to schema. The return format and example provide indirect 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 clearly states the tool posts a new top-level comment on a YouTube video, creating a new comment thread. It uses a specific verb and resource, and distinguishes from siblings like youtube_reply_to_comment (which replies to an existing thread) and youtube_list_comment_threads.

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 an example and lists error conditions, which imply use cases, but it does not explicitly state when to use this tool vs alternatives like youtube_reply_to_comment. There is no direct contrast or prerequisites mentioned beyond the schema.

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?

Annotations indicate a non-read-only, non-destructive operation with open-world hints. The description adds context by mentioning errors (e.g., 403 quota exceeded) and return structure, which supplements the annotations well.

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-organized with clear sections (Args, Returns, Examples, Errors), front-loaded with the purpose, and contains no unnecessary information. Every part serves a 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?

The description covers the operation, parameters, return format, error scenarios, and an example. For a creation tool with 4 parameters and no output schema, it is comprehensive and leaves no gaps for the agent.

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

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

Since schema description coverage is 100%, the baseline is 3. The description adds marginal value by repeating defaults and providing an example, but does not substantially enhance parameter meaning beyond the schema.

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

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

The description clearly states 'Create a new playlist on the authenticated channel' using a specific verb and resource. It distinguishes from sibling tools like youtube_delete_playlist and youtube_update_playlist through the creation intent.

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?

While the description provides examples and error codes, it does not explicitly specify when to use this tool versus alternatives (e.g., youtube_update_playlist). Usage context is implied but not explicitly stated.

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

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

Annotations already indicate destructiveHint=true, but the description adds critical context: the action is irreversible, requires a confirm flag, and lists possible error codes (403, 404). This goes beyond the annotations without contradiction.

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 sections for warning, args, returns, examples, and errors. It is front-loaded with the purpose and safety warning, and every section adds value without unnecessary verbosity.

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 destructive nature and lack of output schema, the description covers purpose, safety, parameters, errors, and usage examples. It effectively compensates for missing output schema by showing the return string. Minor omission: no explicit mention of rate limits.

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 examples and clarifies the confirm flag's role, but the schema already provides adequate descriptions for both 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 explicitly states 'Permanently delete a caption track from a YouTube video,' which is a specific verb+resource. This clearly distinguishes it from sibling tools like youtube_upload_caption or youtube_delete_video.

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

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

The description provides clear context for when to use the tool (to delete a caption track) and highlights prerequisites (own channel, need for confirm flag). However, it does not explicitly mention when not to use it or suggest 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?

Adds value beyond annotations: details irreversibility, confirmation requirement, and error scenarios, fully disclosing 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?

Well-structured with clear sections (Args, Returns, Examples, Errors), concise and front-loaded with essential info.

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?

Complete for a simple delete operation: covers purpose, parameters, return value, errors, and safety mechanisms.

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%; description adds example and error context for confirm parameter, enhancing clarity.

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

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

Description clearly states the action (permanently deletes), resource (comment), and ownership condition (owned by authenticated channel), distinguishing it from siblings like update or mark as spam.

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 notes irreversibility, confirm flag as safety guard, and error conditions (403 if not owned, 404 if not exist), guiding appropriate use. Lacks direct comparison to alternatives like spam marking.

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?

Annotations already indicate destructiveHint=true. Description adds detail: it's irreversible, requires explicit confirmation, and lists potential errors (404, 403). This goes beyond annotations without contradicting them.

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 well-structured with separate sections for Args, Returns, Examples, and Errors. It is concise yet comprehensive, with key information 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's simplicity and lack of output schema, the description covers all necessary aspects: what it does, how to use it (with confirm), error handling, and return value. No gaps.

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 covers both parameters fully. Description adds practical context: the confirm parameter must be true to execute, else the tool will refuse. This adds value beyond the schema definitions.

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

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

Description clearly states 'Permanently delete a playlist. This is irreversible.' The verb (delete) and resource (playlist) are explicit, and the scope is well-defined, distinguishing it from sibling tools like youtube_delete_video or youtube_update_playlist.

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?

Description provides clear context: it's for permanent deletion and requires confirm=true. It also lists error conditions. However, it does not explicitly compare with alternatives or state when not to use it, leaving room for improvement.

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?

Annotations provide destructiveHint=true, but description adds crucial detail: irreversible action, required confirm flag, and specific error conditions. This provides rich behavioral context beyond 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?

Description is well-structured with sections for summary, warning, args, returns, examples, and errors. Every sentence is informative and 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 delete operation, the description covers irreversibility, required parameters, example usage, and error conditions. No output schema needed; 'Short confirmation' is sufficient.

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

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

Schema coverage is 100%, but description adds value by describing 'confirm' as a safety guard and specifying that it must be true. However, videoId parameter receives no additional detail beyond schema.

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

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

Description explicitly states 'Permanently delete a video from the authenticated channel,' clearly specifying verb and resource. It distinguishes from sibling tools like youtube_delete_playlist or youtube_delete_caption.

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

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

The description clearly implies usage when deleting a video, with warnings about irreversibility. It mentions error conditions for not-owned videos, but does not explicitly state alternatives or when not to use.

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?

Discloses truncation behavior, write-to-file vs inline return, error codes (403, 404), and aligns perfectly with annotations (readOnly, idempotent).

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, front-loaded purpose, and efficient use of space.

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?

Covers all aspects: input, output, error handling, examples. No gaps given the complexity and schema richness.

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?

Adds significant value beyond schema: default format behavior, return types, examples. Schema coverage is 100% but the description enriches each parameter.

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

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

The description clearly states the verb 'download' and resource 'caption track', distinguishing it from sibling tools like 'list', 'delete', and 'update'.

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

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

Provides clear context on when to use (with caption ID) and constraints (own channel, scopes), though does not explicitly mention when not to use.

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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context beyond annotations: mentions a possible 403 error (insufficient scope) and provides default date ranges, enhancing transparency without contradiction.

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 a concise intro, bullet-pointed arguments, a formal return shape, and example usage. Every sentence adds value, no fluff.

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

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

For a read-only analytics tool with no output schema, the description provides complete context: purpose, parameters with defaults, return format, examples, and error handling. It is self-contained and sufficient for an agent to use correctly.

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

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

Although schema coverage is 100%, the description adds significant meaning beyond schema by including the default value for startDate (28 days ago), the return shape with columns and rows, and a clear example. This fully compensates for the lack of an output schema.

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

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

The description clearly states it breaks down views, watch time, and average view duration by country. The verb "break down" and resource "by country" are specific and distinguish it from sibling tools like youtube_top_videos (top videos) or youtube_traffic_sources (traffic sources).

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

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

The description provides clear context for when to use the tool (geographic analytics for the channel) and includes examples like "Where are my viewers located?" but does not explicitly mention when not to use it or directly compare to 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?

Annotations already indicate idempotent, read-only, non-destructive behavior. The description adds that it retrieves only public details and accepts three identifier types, with automatic @-stripping, providing behavioral context beyond 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 structured into clear sections (Args, Returns, Examples, Errors) and is comprehensive without being overly verbose. Some redundancy exists but overall it's efficient.

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

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

Combined with the schema and annotations, the description provides thorough context: return shape, error handling, examples, and identifier usage. It is fully complete for a read-only lookup tool with no 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?

All parameters are well-documented in the schema, and the description adds examples, error scenarios, and clarifies the relationship between the three optional identifiers, significantly enhancing understanding.

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 it retrieves public details for a YouTube channel by three specific identifiers, distinguishing it from sibling tools like youtube_get_my_channel.

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 specifies exactly when to use the tool (retrieve channel by ID/handle/username) and provides error conditions. It could explicitly contrast with similar tools like youtube_channel_summary, but overall it provides clear 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?

Annotations already imply safety (readOnlyHint, idempotent). The description adds explicit error codes (401/403, 404) and detailed return schema, providing rich behavioral context well beyond 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?

Compact, well-organized sections (Args, Returns, Examples, Errors). No wasted words; every sentence adds value. Front-loaded with the primary 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?

Despite no output schema, the description fully documents the return shape with a JSON block, error handling, and usage examples. An agent has complete information to invoke the tool correctly.

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

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

Schema coverage is 100% (one parameter fully described). The description adds usage examples and context ('markdown' vs 'json'), providing value beyond the schema's enum and default.

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

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

The description clearly states 'Retrieve full details for the authenticated user's own YouTube channel,' specifying both the verb (retrieve) and the resource (own channel). This distinguishes it from sibling 'youtube_get_channel' which gets other users' channels.

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 vs alternatives like 'youtube_get_channel.' The description only states what it does, without context on when not to use it or which sibling to prefer.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds valuable behavioral context: the return JSON shape, error codes (404, 403), and output format flexibility. No contradiction with 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 well-organized into Args, Returns, Examples, Errors sections. Every sentence is informative and necessary. No extraneous content. The structure aids quick comprehension.

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 no output schema, the description provides a detailed JSON shape. It covers purpose, parameters (with examples), return values, and error handling. For a simple 2-parameter read tool, this is fully complete.

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

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

Schema description coverage is 100% for both parameters. The description adds value beyond the schema by providing an example playlistId format and default value for response_format. However, the schema already adequately describes each parameter.

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

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

The description clearly states 'Fetch full details for a single playlist by its ID.' It uses a specific verb ('Fetch'), a specific resource ('playlist'), and distinguishes from siblings like youtube_list_playlists (which lists multiple playlists) and youtube_get_video (different resource).

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 examples, error codes, and output format options, but does not explicitly state when to use this tool vs alternatives like youtube_list_playlists. However, the purpose is clear enough that an agent would infer the appropriate usage scenario.

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?

Annotations already indicate readOnly, destructive, idempotent, and openWorld hints. The description adds significant value by detailing the underlying API call (videos.list with specific parts), the return JSON shape, and error handling, providing complete behavioral transparency.

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

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

The description is well-structured with sections (Args, Returns, Examples, Errors). It is front-loaded with the purpose, and every sentence adds value without redundancy. Appropriate length for the tool's complexity.

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

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

The description covers all essential aspects: purpose, parameters, return format (including full JSON shape), examples, and error handling. Since there is no output schema, the return description is crucial and well-provided.

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?

Input schema has 100% coverage, so baseline is 3. The description adds example values and clarifies the response_format, but this is only marginal beyond the schema's descriptions. The schema already provides necessary 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 full metadata for a single video by ID,' using a specific verb and resource. It distinguishes from sibling tools like youtube_search (which lists multiple videos) and youtube_get_channel (which fetches channel details) by focusing on a single video's comprehensive metadata.

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 error conditions (404, 403) that help determine when the tool can be used. However, it does not explicitly state when not to use it or suggest alternative tools for scenarios like fetching multiple videos or searching.

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?

Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context about required scopes (youtube.force-ssl) and error scenarios, which goes beyond the 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 well-organized with clear sections for Args, Returns, Examples, and Errors. It is concise yet comprehensive, with 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 the tool has only 2 parameters and no output schema, the description provides a complete JSON shape for returns, covers errors, and gives examples. This fully equips an AI agent to use the tool correctly.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds examples and explains the default for response_format, providing additional meaning beyond the schema.

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

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

The description clearly states 'List all caption tracks available for a YouTube video', providing a specific verb (list) and resource (caption tracks). This distinguishes it from sibling tools like download, upload, update, or delete caption.

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 arguments, examples, and error conditions (403 Forbidden, 404 Not Found) that guide when the tool can be used. It does not explicitly name alternative tools but provides enough context for correct usage.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds significant behavioral context: explains the return shape, response_format options, and common errors. No contradiction with 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 well-structured with clear sections (Args, Returns, Examples, Errors). It is concise, using bullet points and a JSON example, with no redundant information.

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

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

Given the low complexity (1 optional param) and the presence of comprehensive annotations, the description is complete. It covers return values via the JSON shape, error handling, and usage examples. No output schema is needed because the JSON shape is provided.

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 single parameter (response_format) is fully documented in the schema (100% coverage). The description enriches it by explaining the default and the difference between 'markdown' (readable table) and 'json' (raw API array), going beyond the enum values.

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

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

The description clearly states the tool lists all sections on the authenticated user's YouTube channel home page, with examples of section types. This distinguishes it from sibling list tools like youtube_list_playlists or youtube_list_subscriptions by specifying the unique resource.

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 examples of when to call the tool and includes an Errors section with troubleshooting steps (e.g., re-running auth). However, it does not explicitly state when not to use this tool or mention alternative tools for similar tasks.

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

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

The description goes beyond annotations by detailing pagination behavior, the exact return structure (including fields like id, authorDisplayName, text, etc.), error conditions (404, 403), and default values. Annotations already indicate read-only, non-destructive, idempotent, and open-world, so the description adds significant context on how the tool behaves.

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 (Args, Returns, Examples, Errors). It is concise, using only necessary information without redundancy. Every sentence serves a 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?

Despite having no output schema in structured form, the description provides a detailed JSON return schema, pagination details, error handling, and a usage example. This fully compensates for the lack of formal output schema and makes the tool's behavior completely transparent.

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

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

All four parameters are fully described in the input schema (100% coverage). The description reiterates the same information with added context like defaults and an example, but does not provide new meaning beyond what the schema already conveys. Baseline of 3 is appropriate.

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

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

The description clearly states 'Retrieves a page of replies to a specific top-level comment thread,' specifying the verb, resource, and scope. It distinguishes from siblings like 'youtube_list_comment_threads' (which lists top-level threads) and 'youtube_reply_to_comment' (which creates replies).

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

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

The description provides clear context for when to use the tool (when you have a parent comment ID and want replies) and includes examples and error cases. However, it does not explicitly state when not to use it or directly contrast with sibling tools, though this is implied by the name and sibling list.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds useful behavioral insights beyond annotations, such as error conditions (400 if neither/both IDs, 403 if comments disabled or quota exceeded, 404 if resource not found) and the return structure. This provides sufficient transparency for safe agent behavior.

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

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

The description is well-structured with clear sections (Args, Returns, Examples, Errors) and is front-loaded with a concise summary. Every sentence adds value; there is no repetition or fluff. It's 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 the tool has 7 parameters (none required), 100% schema coverage, and no output schema, the description covers all essential aspects: usage constraints, parameter details, return format with example, and common errors. It is comprehensive enough for an agent to correctly invoke and interpret results.

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 documents all parameters. The description adds value by clarifying the mutual exclusivity requirement for videoId and channelId, listing the default for order, and providing a full JSON example of the return structure (not in schema). This goes beyond the schema's descriptions.

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

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

The description clearly states it retrieves a page of top-level comment threads for a YouTube video or channel, explicitly mentioning the two mutually exclusive IDs (videoId, channelId). This distinguishes it from siblings like youtube_list_comment_replies (which gets replies for a specific thread) and youtube_create_comment_thread (which creates).

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

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

The description provides clear instructions on how to use the tool: exactly one of videoId or channelId must be supplied, with explanations of what each does. It includes examples for listing by video and paging through channel comments. However, it does not explicitly mention when not to use this tool or suggest alternatives like youtube_list_comment_replies for replies.

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?

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds significant behavioral details: it explains the internal process (fetches uploads playlist ID, pages through playlistItems), provides the exact JSON return shape, lists possible errors (401/403, 404), and gives usage examples. No contradiction with 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 well-structured with sections (Args, Returns, Examples, Errors) and front-loaded with the core purpose. Every sentence adds value, no fluff. It is appropriately sized for the tool's complexity.

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

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

The tool has 3 parameters, no output schema, but the description provides a full JSON shape for the return value, lists error conditions, and includes examples. Combined with annotations, this is complete and leaves no ambiguity for an AI agent.

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

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

Schema coverage is 100%, but the description adds substantial meaning: it explains maxResults as 'items per page', pageToken as 'cursor token', and response_format options. It also provides default values and examples for parameter usage. This goes beyond the schema's own descriptions.

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

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

The description clearly states 'List videos uploaded to the authenticated channel,' indicating a specific verb (list) and resource (videos of the authenticated user). It distinguishes from siblings like youtube_search (which searches all videos) and youtube_get_video (single video).

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 context that this tool fetches the user's own uploads via channel list and playlistItems. It includes examples and error handling, but does not explicitly state when to use it over alternatives like youtube_search or youtube_list_playlist_items. Still, the purpose is clear enough.

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?

Adds pagination details, output shape, and error codes beyond annotations (readOnlyHint, etc.). No contradiction.

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-organized with sections (Args, Returns, Examples, Errors). Concise and informative.

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?

Complete for a listing tool: covers all parameters, pagination, errors, output format. No missing context.

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

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

Schema covers all 4 params; description adds usage examples and output format, enhancing meaning beyond schema.

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

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

Clearly states 'List the videos inside a playlist, with pagination support.' Distinguishes from sibling tools like youtube_list_playlists by specifying playlist items.

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?

Implicitly conveys when to use (listing playlist items) with examples and error handling, but could explicitly differentiate from add/remove operations.

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?

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds valuable context: authentication requirement, pagination details, output format, and specific error codes (401/403). 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 well-organized with clear sections (Args, Returns, Examples, Errors). It is concise, each sentence adds value, and it avoids unnecessary details.

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

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

Despite lacking an output schema in the tool definition, the description provides a complete JSON shape of the return value. It includes examples, error handling, and covers all parameters. The tool is fully documented for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description's parameter explanations largely repeat the schema (e.g., 'Number of playlists to return per page'), adding little beyond the schema's definitions. No new semantic information is provided.

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

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

The description clearly states 'List all playlists belonging to the authenticated channel,' which is a specific verb and resource. It distinguishes from siblings like youtube_get_playlist (single playlist) and youtube_create_playlist (create).

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

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

The description provides clear context for when to use the tool: listing all playlists of the authenticated user. It does not explicitly mention alternatives, but the scope and examples imply appropriate use. Error handling hints at prerequisites (authentication).

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?

Annotations already indicate read-only, idempotent, and open-world behavior. The description adds valuable context about the API endpoint, common errors (auth errors, permissions), and pagination, enhancing transparency beyond 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 well-structured with sections (Args, Returns, Examples, Common Errors), front-loaded with the core purpose, and every sentence is meaningful. No redundant or wasteful content.

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

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

Given no output schema, the description provides a complete JSON return shape. It covers all parameters, pagination, error handling, and examples. The tool's complexity is fully addressed.

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 descriptions, but the description adds extra value by explaining effects of each parameter (e.g., order meanings), providing examples, and detailing return shape. This goes beyond the schema's basic descriptions.

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

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

The description clearly states the tool lists all channels the authenticated user is subscribed to, using a specific verb and resource ("List all channels"). It distinguishes from sibling tools, as no other tool lists subscriptions.

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

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

The description provides clear context on how to use the tool (e.g., pagination, parameter examples) but does not explicitly state when to use this tool vs alternatives. However, the purpose is unique among siblings, so the lack of exclusions is acceptable.

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?

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. The description adds context by naming the API method, possible error (400), and return structure. No contradiction with 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 well-structured with clear sections (Args, Returns, Examples, Errors) but is somewhat lengthy. Every sentence adds value, but it could be slightly more 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?

For a simple 2-parameter tool with annotations, the description fully covers purpose, usage, return format (via JSON example), and error handling. No output schema, but the provided return structure suffices.

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

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

Schema covers 100% of parameters with descriptions. The description adds value by providing examples, explaining the default 'US', and mentioning error conditions for regionCode. This goes beyond the schema.

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

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

The description clearly states the tool lists available YouTube video categories for a region, specifying the API call (videoCategories.list) and the purpose of IDs for other tools. It is specific and distinguishes from siblings like youtube_upload_video.

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 the tool (to obtain category IDs for uploads/updates) and provides examples. However, it does not explicitly state when not to use it or contrast with alternatives among 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?

Annotations already indicate non-destructive and idempotent behavior. The description adds value by specifying the return format, error codes (403, 404), and the effect on the comment, enhancing transparency beyond 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 extremely concise with clear sections (Args, Returns, Examples, Errors). Every sentence adds value, no fluff, and it is front-loaded with the core 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?

For a simple one-parameter tool with no output schema, the description covers the action, usage, errors, and example sufficiently. No gaps are apparent 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?

With 100% schema description coverage, the description adds only an example and error context, which is baseline. No additional semantic nuance beyond the schema is provided.

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

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

The description clearly states the action: 'Marks a comment as spam, flagging it for review.' This is a specific verb+resource that distinguishes it from siblings like delete_comment or set_comment_moderation.

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 an example and error conditions, but does not explicitly guide when to use this tool versus alternative moderation tools like youtube_set_comment_moderation. Usage context is implied but not direct.

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

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

The description goes beyond annotations by disclosing the underlying API call (videos.rate), the behavior of applying or removing ratings, and error conditions. Annotations indicate idempotent and non-destructive behavior, which is consistent. The description adds clarity on the return value and authentication 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 well-structured with sections for description, args, returns, examples, and errors. It is informative but could be slightly more concise. 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 simple tool with two parameters and no output schema, the description covers all necessary aspects: inputs, behavior, return value, examples, and errors. No information gaps remain.

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 the parameters. The description adds value with an 'Args' section restating parameters, examples showing concrete usage, and clarifying that 'none' removes the rating. This reinforces understanding beyond the schema.

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

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

The description clearly states the tool's purpose: to like, dislike, or remove a rating from a video. The title 'Rate Video' aligns with this. Among many sibling tools for various YouTube actions, this is the only one for rating, so it is well-distinguished.

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 indicates the rating applies to the authenticated user's account, providing context. However, it does not explicitly state when to use this tool versus alternatives, nor when not to use it. The examples help clarify usage but lack explicit usage boundaries.

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?

Annotations declare destructiveHint=true and readOnlyHint=false; the description confirms irreversibility and details the confirm parameter requirement. No contradictions, and it adds clarity on the destructive action.

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-organized with sections for Args, Returns, Examples, and Errors. Every sentence adds value, and it is concise 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?

The tool is simple with two parameters. The description covers purpose, parameter details, return value, error cases, and provides an example. It is complete for effective 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?

Schema coverage is 100%. The description adds meaning by stating the confirm parameter must be true to execute, and explains the error behavior if omitted. It also notes the irreversible nature of the action.

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 'Remove a video from a playlist (deletes the playlist item).' It uses a specific verb and resource, distinguishing it from sibling tools like youtube_delete_playlist or youtube_update_playlist_item.

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?

It advises using youtube_list_playlist_items to find the playlistItemId and provides error scenarios. It lacks explicit when-not-to-use but gives sufficient context for correct invocation.

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?

Annotations already indicate readOnlyHint=false, destructiveHint=false, idempotentHint=false. The description adds context: it operates on behalf of the authenticated channel, supports basic HTML entities in text, and lists specific error codes (400, 403, 404), which are beyond the 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 well-structured with sections (Args, Returns, Example, Errors). Each part serves a purpose, though it could be slightly more concise. It is efficiently organized 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 no output schema, the description provides a return schema example. It covers parameters, errors, and the action. It does not explain authentication beyond 'authenticated channel,' but that is sufficient. Overall, it is complete for the tool's 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% with descriptions. The description adds value by noting that text supports basic HTML entities and providing an example (parentId example). This extra semantic detail justifies a 4.

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

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

The description clearly states it posts a reply to an existing top-level comment thread, which is a specific verb+resource. It distinguishes itself from sibling tools like youtube_create_comment_thread (creates a new thread) and youtube_update_comment (edits a reply).

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

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

The description implicitly indicates when to use (to reply to a comment) but does not explicitly mention when not to use or compare with alternatives. It lacks guidance like 'use this for replying, use youtube_create_comment_thread for new top-level comments'.

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?

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds authentication prerequisites, YPP requirement, error conditions, default date ranges, and effect of dimension parameter on output. No contradictions; adds substantial behavioral context.

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

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

The description is well-structured with clear sections: introduction, important notes, args, returns, examples, errors. Every sentence provides value; no redundancy. It is detailed yet concise, and front-loaded with key purpose and requirements.

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

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

Given no output schema, the description provides the JSON shape with columns and rows, explains dimension effects, covers errors, and includes examples. All necessary information for an AI agent to use the tool correctly is present.

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

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

Schema coverage is 100%, and the description enriches each parameter with defaults, allowed values, format requirements (e.g., ISO 4217 for currency), and examples. It clarifies the dimension parameter behavior and response_format options, adding beyond the schema.

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

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

The description explicitly states it retrieves revenue and monetization metrics for the authenticated channel, listing specific metrics like estimated revenue, CPM, etc. It clearly identifies the resource (channel) and the action (retrieve), 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 critical usage requirements: OAuth scope and YPP enrollment, with a clear note about 403 errors. It provides examples and default behaviors. However, it lacks explicit comparison to sibling tools like 'youtube_video_performance' or 'youtube_run_analytics_query', which could help agents choose the right 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?

Annotations already provide readOnly, destructive, idempotent, and openWorld hints. The description adds valuable context: return shape (columns and rows), default date range, error codes, and example queries. 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 well-structured: purpose, defaults, parameter list, return format, examples, errors. Every section is necessary and no redundant information. Front-loaded with key 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?

For a complex tool with 9 parameters and no output schema, the description covers all aspects: parameter details, defaults, return shape, error handling, and usage examples. It fully equips an agent to use the tool correctly.

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

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

Schema has 100% coverage, so baseline is 3. The description adds value with concrete examples, error scenarios, and format details (e.g., default currency, markdown vs json) that go beyond the schema descriptions.

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

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

The description clearly states it executes a YouTube Analytics API query against the authenticated channel, distinguishing it from sibling tools that handle videos, playlists, comments, etc. Specific verb 'Execute' and resource 'Analytics API query' provide high clarity.

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 flexible query support, defaults, and provides examples, but does not explicitly contrast with sibling analytics tools like `youtube_audience_demographics` or `youtube_revenue`, leaving some ambiguity about when to use this generic query vs specialized 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?

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds critical behavioral details: quota cost per call, endpoint used (search.list), and common errors (e.g., quotaExceeded). 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 well-structured with clear sections (warning, args, returns, examples, errors), is front-loaded with the important quota warning, and every 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 12 parameters, 100% schema coverage, and no output schema, the description provides return JSON shape, examples, and common errors, making it fully complete for effective 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?

With 100% schema description coverage, the description adds even more: default values, allowed enums, constraints (e.g., forMine requires type=video), examples, and a full JSON return shape.

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 ('Search') and identifies the resource ('YouTube for videos, channels, and/or playlists'), clearly differentiating it from sibling tools like youtube_get_video or youtube_list_playlists.

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 warns about high quota cost (100 units per call) and advises to use sparingly and narrow queries. However, it does not explicitly state when to use alternatives like youtube_get_video for single videos.

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?

Annotations indicate idempotent and non-destructive. Description adds substantial behavioral details: bulk moderation via comma-separated IDs, specific moderation statuses, optional author banning, return format, and error codes. No contradiction with 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?

Well-structured with separate Arg/Returns/Examples/Errors sections. Comprehensive but not overly verbose. A minor redundancy with schema parameter descriptions, but overall efficient.

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

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

Complete coverage: explains all parameters, return value JSON, error codes, and multiple examples. Lacks output schema but the description provides full return type. Sufficient for agent to use correctly.

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

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

Schema details are 100% covered, but the description adds deeper meaning: explains comma-separated IDs for bulk, enumerates moderation statuses with descriptions, clarifies default for banAuthor, and provides concrete examples. Goes well beyond the schema.

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

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

The description clearly states it sets the moderation status of comments, with optional author banning. It distinguishes from sibling tools like youtube_delete_comment and youtube_mark_comment_as_spam, which handle related but distinct actions.

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

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

Provides clear usage context with examples and error cases, but does not explicitly state when not to use this tool or mention alternatives beyond the sibling list. Slightly lacking exclusion guidance.

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

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

The description discloses file streaming behavior, MIME type inference, allowed formats, max file size (2 MB), and error conditions. Annotations already indicate non-destructive nature, but the description adds operational details beyond 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 well-structured with clear sections (Args, Returns, Examples, Errors). It is concise, with every sentence adding value—no redundancy or irrelevant content.

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

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

For a tool with 2 parameters and no output schema, the description covers purpose, usage, parameters, return format (confirmation + thumbnail resource with URLs), examples, and error conditions. It meets the needs of an AI agent selecting and invoking the 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 descriptions already cover both parameters. The description adds context (e.g., absolute path requirement, file extensions) and ties parameters to error conditions (file not found, unsupported format). Schema coverage is 100%, so baseline is 3; the extra context pushes it to 4.

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

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

The description clearly states 'Upload and set a custom thumbnail for a video,' which is a specific verb and resource. It distinguishes from sibling tools like youtube_upload_video (uploads video) and youtube_update_video (updates video metadata) by focusing on thumbnail operations.

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 context through examples and error conditions, but does not explicitly state when to use this tool over alternatives. It is clear for the intended use case, but lacks explicit when-not-to-use guidance.

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

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

Beyond annotations (mutation, not destructive), the description details the return JSON shape, common errors, and authentication requirements. This is extensive and helpful for the agent to understand all behavioral aspects.

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 (Args, Returns, Examples, Common Errors). It is concise yet comprehensive, with 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 no output schema, the description includes a full JSON shape. It covers errors, examples, and parameter details. Everything needed for correct invocation is present.

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

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

Schema coverage is 100%, but the description adds crucial context: channelId is a resource ID not a handle, and how to obtain it. This exceeds the schema description and fully clarifies the parameter.

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

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

The description clearly states the action ('Subscribe the authenticated user to a YouTube channel') and the endpoint used (youtube#subscriptions.insert). It distinguishes itself from siblings like youtube_unsubscribe and youtube_search by specifying the exact operation.

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 context on how to obtain the channelId (from youtube_search or youtube_list_subscriptions) and lists common errors (duplicate subscription, invalid channelId, auth issues). It doesn't explicitly contrast with alternatives but offers enough guidance for correct usage.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds significant behavioral details: it 'returns enriched' data, cites specific error codes (403 scope, 400 invalid date), and describes the exact JSON output shape. No contradictions with 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 well-structured with clear sections (Args, Returns, Examples, Errors). The first sentence is direct. However, it is somewhat verbose (e.g., 'enriched with video titles fetched from the YouTube Data API' is partially redundant given the return shape). Could be slightly trimmed without losing clarity.

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

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

Given no output schema, the description provides a detailed JSON shape showing columns and rows. It covers all parameters, default values, range constraints, example usage, and common error messages. Includes the authenticated channel scope. No obvious gaps for a query tool.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description repeats the same parameter descriptions and adds a response_format explanation with enum values. However, it does not add meaning beyond what the schema provides, such as example values or constraints beyond the schema's min/max. 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 returns 'top-performing videos on the authenticated channel ranked by views, enriched with video titles.' This is a specific verb+resource combination that distinguishes it from siblings like youtube_list_my_videos (which lists all videos) and youtube_video_performance (which focuses on specific video 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 example queries (e.g., 'What are my top 5 videos this month?') that imply when to use the tool, but it lacks explicit guidance on when not to use it or which alternative tools to consider. The examples help but the absence of exclusions or sibling references leaves some ambiguity.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds valuable behavioral context: the JSON return shape, possible error (403 with resolution), and that response_format can be markdown (default) or json. No contradictions with 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 well-organized with clear sections: purpose, args, returns, examples, errors. Every sentence adds value, and the structure is easy to scan. 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?

With no output schema, the description compensates by providing the exact JSON structure, column names, and example rows. It covers all parameters, error handling, and usage examples. This makes the description fully complete for an AI agent to invoke the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds extra meaning by showing default values (e.g., 'Defaults to 28 days ago'), providing examples, and clarifying the response_format enum. This enhances understanding beyond the schema alone.

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: 'Show how viewers find the channel's videos, broken down by traffic source type' and lists example sources. This is a specific verb+resource combination that distinguishes it from sibling analytics tools like youtube_top_videos or youtube_video_performance.

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 example use cases ('Where do my viewers come from?') and demonstrates custom date usage. While it does not explicitly state when not to use or compare to alternatives, the context of sibling tools and the specific focus on traffic sources gives clear 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?

The description discloses destructive and irreversible nature, requiring confirm=true to execute. It explains dry-run behavior (refuses with explanation) and the 204 response on success. Annotations already set destructiveHint=true; description adds context about what gets destroyed and how to safely test.

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: overview, important note, args, returns, examples, common errors. Every sentence provides value, no redundancy. It's 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?

For a 2-parameter tool with no output schema, the description fully covers behavior, returns (204 with confirmation message), and error handling. It anticipates common mistakes (wrong ID, auth issues) and provides resolution steps.

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?

Input schema has 2 parameters with detailed descriptions. The description adds meaning beyond schema: explains that subscriptionId is the 'id' field from list results (not channelId), and confirm default false is a safety. Examples illustrate usage. Schema coverage is 100%, but description adds critical 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 action ('Remove a subscription') and the resource ('for the authenticated user') using the YouTube Data API v3 subscriptions.delete endpoint. It distinguishes from sibling tools like youtube_subscribe by specifying it's for unsubscribing and requiring a subscription resource ID, not a channel ID.

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

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

The description provides explicit when-to-use instructions (must have subscription ID from youtube_list_subscriptions, not channel ID), when-not-to-use (wrong ID leads to 404), and alternatives (call youtube_list_subscriptions first). It includes important prerequisites, dry-run behavior, and common errors.

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?

Annotations indicate readOnlyHint=false and destructiveHint=false, consistent with the update operation. The description adds that file replacement happens via streaming from disk, and that updating only isDraft is a metadata-only change. No contradictions; the description adds useful behavioral context beyond 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 well-structured: a concise summary, detailed explanation, separate Args/Returns/Examples/Errors sections. Every sentence adds value, 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?

Despite no output schema, the description provides a JSON example of the return value. It covers all 3 parameters, includes error handling, and gives examples covering key use cases. An agent can confidently use the tool based on this description.

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?

Input schema coverage is 100%, so baseline is 3. The description adds value by explaining the behavioral implications of using isDraft vs filePath, and includes examples. This goes beyond repeating schema descriptions.

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

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

The description clearly states the action: update an existing caption track's draft status and/or replace its content. It specifies the resource (caption track) and distinct actions, distinguishing it from siblings like upload_caption (create) and delete_caption (delete).

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

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

The description provides clear context on when to use filePath vs isDraft, and includes examples for different scenarios. It also lists error codes (403, 404) indicating prerequisites, but does not explicitly contrast with sibling tools like upload or delete.

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?

Describes merge behavior (fetches current branding, merges, omitted fields preserved). Lists error codes (400, 401/403). No contradiction with annotations (readOnlyHint false, destructiveHint false). Adds significant value beyond 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?

Well-structured with summary, parameter list, return shape, examples, errors. Every sentence is useful. Slightly long but justified by thoroughness.

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?

Covers all aspects: purpose, parameters, merge behavior, return shape, examples, errors. No output schema but return shape provided. Complete for a mutation tool with 5 optional params.

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

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

Schema coverage is 100%, but description adds usage details for each parameter (e.g., max chars for description, format for keywords, examples). Adds meaning beyond schema.

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

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

Clearly states 'Update one or more branding fields on the authenticated user's YouTube channel'. The verb 'update' and resource 'channel branding' are explicit. Distinguishes from sibling tools which focus on videos, playlists, comments, etc.

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?

States that at least one field must be supplied and explains merge behavior. No explicit 'when not to use' but the tool name and sibling context make the scope clear. Adequate guidance.

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

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

Annotations indicate a write operation (readOnlyHint=false) and non-destructive (destructiveHint=false). The description adds behavioral details: updates text, requires ownership, and lists error conditions (400, 403, 404). No contradiction with 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 concise and well-structured with clear sections (Args, Returns, Examples, Errors). Every sentence adds value, and the purpose 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?

Despite lacking a formal output schema in structured fields, the description provides a JSON example of the return value. It covers parameters, errors, and ownership constraint, making the tool fully understandable for a simple update operation.

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 descriptions for both parameters. The description repeats them but adds an example ('Fix a typo') that provides semantic usage context beyond the schema, justifying a slight improvement over baseline.

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

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

The description clearly states it updates the text of an existing comment owned by the authenticated channel. It distinguishes from sibling tools like youtube_delete_comment or youtube_mark_comment_as_spam by specifying the action and ownership requirement.

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

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

The description implies when to use (to update your own comment's text) through ownership language and error codes (403 if not owned). It does not explicitly state when not to use or name alternatives, but the context is clear.

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