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

No annotations are provided, so the description carries full burden. It warns about destructiveness, admin-only, and irreversibility, and describes the return value. This is adequate, though additional details (e.g., behavior on missing namespace) could improve transparency.

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

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

The description is concise with a clear header, warning symbols, and structured Args/Returns sections. No unnecessary content, though the formatting could be slightly more compact.

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 two-parameter admin tool with no annotations, the description covers purpose, parameters, return value, and critical warnings. It lacks some details like error handling or concurrency, but is largely complete for its 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 0%, but the description includes an 'Args:' section that explains both parameters: namespace with an example ('user:alice'), and include_children with its default. This adds meaning beyond the schema's type and default 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 'Clear all references in a namespace.' The verb 'clear' and resource 'references in a namespace' are specific. It distinguishes from sibling tools like admin_delete_reference (delete single) and admin_list_references (list).

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

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

The description includes warnings about admin-only and destructive nature but provides no explicit guidance on when to use this tool versus alternatives like admin_delete_reference. Usage context is implied but not clearly articulated.

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

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

With no annotations provided, the description carries full burden. It states the tool is destructive and irreversible, but lacks details on side effects, error conditions (e.g., if ref_id does not exist), or confirmation behavior. The mention of 'Returns: Confirmation of deletion or error' adds some transparency, but overall depth is moderate.

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

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

The description is concise, with a one-line purpose followed by prominent warnings. The args/returns section is structured but slightly verbose for a single parameter. Overall, it is front-loaded and efficient, though the args section could be condensed.

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

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

For a single-parameter deletion tool, the description covers core aspects: purpose, admin requirement, destructiveness, and expected return. However, it does not address edge cases (e.g., invalid ref_id, permission failures) or verify consistency with an output schema (known to exist but not provided). This leaves gaps 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?

The description provides minimal additional meaning for the 'ref_id' parameter beyond the schema, merely stating 'The reference ID to delete.' With 0% schema coverage, the description does not explain how to obtain a reference ID or any constraints (e.g., format, allowed characters).

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 deletes a specific cached reference. The verb 'delete' and resource 'cached reference' are specific, distinguishing it from sibling tools like admin_clear_namespace or admin_get_reference_info.

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 explicit warnings 'ADMIN ONLY' and 'DESTRUCTIVE - This action cannot be undone', which guide usage context. However, it does not explicitly mention when to use this tool over alternatives like admin_clear_namespace, though the specificity of 'specific cached reference' implies its intended scope.

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

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

No annotations provided, so description carries full burden. Discloses elevated permissions and lists return fields. As a read-only operation, no side effects are expected, and the description is transparent.

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

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

Description is appropriately sized with front-loaded purpose. Contains no fluff, though the bullet list could be condensed. Every sentence adds value.

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

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

Given zero parameters and presence of output schema, the description covers purpose, access requirements, and return content. Minor gaps (e.g., frequency restrictions) are acceptable for a simple stats 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?

No parameters exist; baseline 4 per rubric. Description adds no param info, but schema coverage is trivially 100%.

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

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

The description clearly states 'Get detailed cache statistics', using a specific verb and resource that differentiates it from sibling tools like admin_clear_namespace and admin_delete_reference.

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 states 'ADMIN ONLY - Requires elevated permissions', indicating when to use. Does not explicitly mention alternatives, but the context of other admin tools makes usage clear.

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 indicates the tool reads a 'cached reference' and returns a dictionary excluding the value, implying a read-only operation. With no annotations provided, this covers basic behavioral expectations but omits details on error handling, performance, or whether the cache is local or distributed. The description is adequate but not comprehensive.

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

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

The description is concise with two sentences plus an Args/Returns block. Every sentence adds value: the first states the purpose, the second warns of admin restriction, and the parameter/return descriptions are precise. No wasted 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 0% schema coverage and no annotations, the description covers the purpose, parameter meaning, admin restriction, and return structure (dictionary, value not included). It does not mention possible exceptions or state whether the reference must exist, but for a simple retrieval tool, it is largely complete.

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

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

The input schema provides only a parameter name and type (string) with 0% description coverage. The description compensates by explaining the parameter: 'ref_id: The reference ID to look up.' This adds meaningful context beyond the schema, though it could elaborate on the format or expected length.

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

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

The description clearly states 'Get detailed information about a cached reference,' which is a specific verb+resource pair. Among sibling admin tools like admin_list_references (lists many) and admin_delete_reference (deletes), this tool uniquely retrieves details for a single reference, distinguishing it effectively.

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

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

The description explicitly notes 'ADMIN ONLY - Requires elevated permissions,' providing a critical usage constraint. It also outlines the return as a dictionary without the value. However, it does not explicitly compare to alternatives or state when not to use, but the admin flag provides sufficient guidance for intended audience.

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

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

No annotations provided, so description carries full burden. It lists parameters and return type but does not disclose behavioral traits like idempotency, caching behavior, or rate limits. The admin-only note adds some safety 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?

Concise, front-loaded purpose, followed by clear parameter list and return info. Every sentence adds value without unnecessary detail.

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

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

Given output schema exists, the return info is adequate. However, the concept of 'references' is not explained, and pagination details are minimal. Still, for a list tool with optional filters, it covers the core needs.

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 0%, but the description adds meaning for each parameter: e.g., 'namespace: Filter by namespace (e.g., 'public', 'user:alice')'. This clarifies usage beyond the bare 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 cached references with optional filtering. The 'ADMIN ONLY' tag distinguishes it from non-admin list tools, and the verb 'list' and resource 'cached references' are specific.

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 'ADMIN ONLY - Requires elevated permissions', guiding when to use. Does not compare to sibling admin tools, but the permission warning effectively excludes non-admin 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?

With no annotations, the description carries full transparency burden. It discloses the destructive nature of the operation and details what is removed (transcripts, comments, both). The return values are documented. Missing: error handling (e.g., video not found) and authorization needs.

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

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

The description is well-structured with a clear first sentence, but the Args/Returns section adds verbosity. Every part is useful, but could be slightly trimmed for efficiency. Still effective.

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

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

Given the tool's simplicity (2 params, deletion action) and presence of output schema in description, it covers purpose, parameters, and return values. Lacks error scenarios and prerequisites, but is mostly complete for an agent.

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

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

Schema coverage is 0%, so description must fully explain parameters. It does: video_id is the YouTube video ID, content_type is optional (transcript or comment), and explains behavior when None. This adds essential meaning beyond the bare 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 deletes a video's indexed content, specifying the resource (semantic search index) and the verb (delete). It details what gets removed (transcripts, comments, or both), distinguishing it from siblings like get_indexed_videos or semantic_search tools.

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

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

Explicitly mentions the tool is useful for re-indexing or cleaning up the index, providing clear context. However, it does not specify when not to use it or list alternative tools for related tasks, which would improve guidance further.

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

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

With no annotations, the description carries full burden. It explains that enabling the mode affects traces by adding user_id, session_id, and metadata from MockContext, and describes the return as a status dict. This provides moderate transparency, though side effects or reversibility are not detailed.

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, uses a clear title, explanatory paragraph, and structured Args/Returns sections. Every sentence adds value with no waste.

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 adequately covers the tool's purpose and behavior. Given its simplicity and the presence of an output schema (though not shown), it is sufficiently complete for an agent to use correctly, though it does not address potential conflicts with sibling tools like set_test_context.

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

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

The description adds meaning to the single boolean parameter 'enabled' by stating its purpose and default value. With 0% schema description coverage, this is necessary but minimal, as the parameter is simple.

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 enables/disables test context mode for Langfuse attribution demos, specifying the verb and resource. It does not explicitly distinguish from sibling tools like set_test_context or reset_test_context, but the purpose is clear.

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

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

The description gives context for use (testing without real authentication) but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives or exclusions.

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

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

With no annotations, the description carries the full burden. It discloses that cache operations are traced to Langfuse with hit/miss status, explains that large results are returned as references with previews, and covers pagination and full retrieval behaviors. 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 bullet points and clear sections. It is mostly concise, though there is slight redundancy between the usage list and later details. Overall efficient and 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 complexity (5 params, no annotations, output schema present), the description covers usage, parameters, caching behavior, and tracing. It mentions cross-tool reference with ref_id. It is sufficiently complete 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?

The description explains each parameter in the Args section, adding meaning beyond the schema's types and defaults (e.g., ref_id is 'Reference ID to look up', full 'bypasses all preview generation'). This compensates for the 0% schema description coverage.

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

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

The description clearly states 'Retrieve a cached result' with a specific verb and resource, and lists concrete use cases (preview, pagination, full value). It distinguishes itself from sibling cache management tools by focusing on retrieval and referencing ref_id from previous calls.

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 guidance on when to use the tool: for previews, pagination through large lists, and full retrieval. However, it does not explicitly mention when not to use it or name 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?

Discloses caching behavior (24 hours), quota cost (1 unit per request), and return structure. Without annotations, this adds important behavioral context. Could mention error handling for missing channels.

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 main description is well-structured with Args, Returns, Example, and Note. However, a large boilerplate section about general caching behavior is appended, which is not tool-specific and reduces conciseness, potentially confusing LLM agents.

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 presence of an output schema, the description covers purpose, usage timing, parameter details, caching, quota, and includes an example. It is complete and actionable for an LLM 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?

With 0% schema coverage, the description fully compensates by explaining the parameter's source (from search results), giving a concrete example, and specifying its role. This exceeds baseline expectations.

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 gets detailed information about a YouTube channel, listing metadata fields and providing an example. It also suggests using after search, distinguishing it from sibling tools like search_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?

Explicitly says 'Use after channel search to get full details' and notes cost savings, providing clear context for when to use. Does not list when-not or alternatives explicitly, but the context is sufficient.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behaviors: no YouTube API quota usage, permanent caching in youtube.content namespace, RefCache preview for large transcripts, and the return format. It does not contradict any annotations.

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

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

The description is reasonably concise and well-structured, starting with a clear purpose, then args, returns, example, and notes. The generic caching behavior at the end slightly reduces conciseness but is still valuable.

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 presence of an output schema, the description properly explains return values and caching behavior. It covers all major aspects: purpose, parameters, returns, caching, and alternatives, making it complete for a tool of moderate complexity.

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

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

Schema description coverage is 0%, but the description thoroughly explains both parameters: video_id with an example, and language with default behavior. This adds significant value beyond the parameter names 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 'Get the complete transcript for a YouTube video' with a specific verb and resource. It distinguishes from siblings like get_transcript_chunk and get_cached_result, which handle pagination or partial retrieval.

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 (retrieve full transcript) and mentions an alternative (get_transcript_chunk for entry-by-entry pagination). It also covers caching behavior and RefCache preview, but does not explicitly list 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?

No annotations are present, so the description carries full burden. It discloses that the tool retrieves indexed video IDs with optional filtering and details the return dictionary structure. However, it does not mention potential performance implications or that it only returns indexed videos, not all videos.

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 a brief intro followed by clearly labeled Args and Returns sections. Every sentence adds value, and the information is front-loaded. No wasted 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?

The description covers the two optional parameters and details the return dictionary fields. An output schema exists, reducing burden on the description for return values. Minor gap: it could clarify that only indexed videos are returned and that content_type filter values are examples, not an enum.

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 0%, but the description explicitly explains both parameters: channel_id as an optional filter by YouTube channel ID, and content_type as an optional filter with example values 'transcript' or 'comment'. This adds significant meaning beyond the schema's property 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?

The description clearly states 'Get list of videos that have been indexed for semantic search,' specifying verb (get), resource (indexed videos), and scope (for semantic search). It effectively distinguishes from sibling tools like semantic_search_* which perform searches rather than listing.

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 says 'Useful for understanding what content is available,' implying when to use, but lacks explicit guidance on when not to use or comparisons with alternative tools. No exclusions or context about prerequisites are provided.

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

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

The description discloses the caching behavior (5-minute cache), quota cost (1 unit), and error conditions (if not live or chat disabled). It also details the return schema. However, the generic caching boilerplate appended at the end is not specific to this tool and may add confusion, but does not contradict the explicit statements.

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 core description for the tool is concise, but the appended generic 'Caching Behavior' and 'Ref input compatibility' sections are largely irrelevant and repetitive, bloating the text. These boilerplate additions obscure the tool-specific information and reduce conciseness.

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

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

While the tool-specific parts are complete (purpose, param, output, caching, quota, error handling), the inclusion of generic caching boilerplate that mentions ref_id compatibility and pagination is misleading for this tool, which does not document such behavior. This undermines completeness for the specific 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?

The input schema only specifies video_id as a string with no description. The description compensates by explaining that video_id is the YouTube video ID of the live stream, and provides an example with a specific ID. This adds necessary meaning beyond the bare 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 gets the live chat ID for a currently streaming video, and explains it is required for fetching chat messages. It distinguishes from siblings like get_live_chat_messages and is_live by noting the ID's purpose and suggesting to check is_live first.

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

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

The description provides clear usage context: it should be used for live streams to obtain the chat ID, and suggests using is_live() first to verify the video is broadcasting. It also notes that it raises an error if the video is not live or chat is disabled, implicitly indicating 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?

No annotations are provided, so the description carries full burden. It discloses caching (30 seconds), quota cost, and the MCP limitation. However, the generic 'Caching Behavior' block at the end introduces ref_id concepts that do not apply to this tool's paramaters or output, potentially misleading agents.

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 overly long with redundant sections. The generic caching boilerplate at the end is unnecessary and not specific to this tool. While structured with headings, the extraneous text hurts conciseness.

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

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

The tool has an output schema (described in Returns) and the description covers usage, examples, and notes. However, the confusing generic caching section detracts from completeness, and the absence of output schema details in JSON may leave some ambiguity.

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 0%, but the description's 'Args' section provides clear, detailed explanations for all three parameters (video_id, max_results, page_token) with defaults and usage context, fully compensating for the schema gap.

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

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

The description clearly states 'Get recent live chat messages from a streaming video' with a specific verb and resource. It distinguishes from siblings like 'get_live_chat_id' and 'get_video_comments' by focusing on live chat messages with pagination.

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 the polling pattern, cost, caching, and when to use page_token. It provides a detailed step-by-step note on the polling cycle. However, it does not explicitly mention when not to use this tool or alternatives for other chat-related data.

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

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

No annotations provided, so description carries burden. Describes return data but does not explicitly state it is read-only or non-destructive. Could mention no side effects.

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?

Short, front-loaded description with no extraneous words. Every sentence adds value.

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

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

For a zero-parameter tool with output schema, description is complete. It states purpose and return type, fitting its simplicity.

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?

No parameters in input schema, schema coverage 100%. Baseline score 4 as per rule for 0 parameters.

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

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

Clearly states it gets information about Langfuse trace and context, with specific verb and resource. Differentiates from sibling tools which are mostly admin or YouTube-related.

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

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

No guidance on when to use this tool versus alternatives. Implied usage for debugging tracing, but no explicit when-to-use 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?

With no annotations provided, the description carries the full burden and excels by disclosing key behaviors: caching ('Cached permanently'), no API quota usage, and the return structure including pagination details. It also notes that transcript content does not change, justifying the caching.

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 clear lead sentence, parameter docs, return format, example, and notes. However, the generic 'Caching Behavior' section adds verbosity; it could be more concise 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 the output schema exists, the description still adds valuable context about the return fields (e.g., 'has_more'), pagination pattern, caching, and usage for large transcripts. It is complete for a pagination tool with 4 parameters.

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

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

Schema coverage is 0%, but the description compensates fully by explaining each parameter with examples ('video_id' example), defaults, and behavior ('language' defaults to first available). It also details the return fields, adding significant 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 the tool retrieves a 'chunk' of transcript entries for pagination. This distinguishes it from siblings like 'get_full_transcript' and 'get_video_transcript_preview' by focusing on iterative access over large transcripts.

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

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

The description explicitly says it is 'useful for iterating through transcripts entry-by-entry' and 'for large transcripts', providing clear context for when to use it. However, it does not explicitly mention when not to use it or name alternatives like 'get_full_transcript' for full retrieval.

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

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

No annotations are provided, so the description fully covers behavioral aspects: caching for 5 minutes, quota cost of 1 unit, empty list return when comments disabled, and reference IDs. The generic caching boilerplate adds context about the caching system, but is not tool-specific.

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, Example, Note). However, it includes a lengthy generic caching block repeated across tools, which reduces conciseness. The specific part is 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?

The description covers parameters, output structure (dictionary with comments list and fields), caching, and quota. It does not mention error handling for invalid video_id, but overall completeness is high given the presence of an output schema (not shown but described).

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

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

The schema has 0% coverage, but the description adds full semantics: video_id format with example, max_results range (1-100) and default (20). This is superior to a typical cryptic 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 function: retrieving top-level comments for a specific YouTube video with engagement metrics, sorted by relevance. It explicitly distinguishes from sibling tools like semantic_search_comments by specifying top-level comments only.

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 when to use (getting comments for a video), what is returned (top-level, no replies), and caching behavior. It lacks explicit exclusion criteria or references to alternative tools for replies, but the specificity is adequate.

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 caching (24h), quota cost (1 unit), and retrieval behavior. No contradictions with annotations (none provided). However, the generic caching behavior section may introduce ambiguity about ref_id usage.

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

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

Front-loaded main description is concise, but the appended caching behavior section is verbose and generic, adding unnecessary length. Could be trimmed to focus on tool-specific 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?

Covers all essential aspects: purpose, parameters, return fields, quota, caching, and usage order. Output schema exists, so detailed return format is appropriate. Slightly less complete on edge cases like auth or error handling.

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 meaning beyond schema: explains video_id format (from URL/search), provides example, and clarifies usage context. Schema coverage is 0% but description fully compensates.

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 'Get detailed information about a YouTube video' and lists specific metadata. Distinguishes from search with quota cost comparison and usage order.

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 advises 'Use after search to get full details' and notes caching and quota. Does not list alternative tools or specific when-not-to-use scenarios, but context is clear.

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 caching behavior (permanent, no quota), return structure (TranscriptPreview dictionary), and includes example. The generic caching boilerplate at the end is not specific to this tool but doesn't contradict.

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?

Main description is clear and structured, but the appended generic caching behavior block is long and not tool-specific, reducing conciseness.

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

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

Given output schema exists and description covers inputs, outputs, caching, quota, and includes examples, it is complete for an AI agent to use effectively.

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

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

All three parameters (video_id, language, max_chars) are explained with examples and default behaviors, adding significant value beyond the schema with 0% description coverage.

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

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

Clearly states it retrieves a preview (first N characters) of a YouTube video transcript. Distinguishes from siblings like get_full_transcript and list_available_transcripts.

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 says 'Useful for deciding if you need the full transcript' and 'Use list_available_transcripts first to see language options'. Also notes permanent caching.

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 that the tool returns health status information including Langfuse tracing status. With no annotations provided, the description carries the full burden, and it sufficiently describes the read-only nature of the operation. It does not mention side effects because there are none expected.

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

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

The description is two sentences long with no redundant information. Every word adds value, making it highly concise and well-structured.

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

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

For a simple health check with no parameters and an existing output schema, the description is complete. It provides all necessary context without needing to explain return values or additional details.

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?

There are no parameters, so the baseline is 4. The description does not need to add anything beyond the schema, which already covers 100%.

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 checks server health status. It uses a specific verb and resource, and the purpose distinguishes it from sibling tools which are focused on admin, search, and video 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 context for using this tool is clear: to check health status. While no alternatives or when-not-to-use are explicitly mentioned, the simplicity and zero parameters make it self-explanatory. A slightly lower score because there is no mention of alternatives, but it remains adequate.

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

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

No annotations provided, but description discloses time estimates (1-2 min for 50 videos), API quota usage (1 unit per video), and benefits (fast subsequent searches). Also details return structure.

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, Note). Front-loaded with purpose. No extraneous information.

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

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

Given the tool's complexity and the presence of an output schema, the description covers all essential aspects: purpose, usage context, parameters, return values, behavioral notes, and limitations.

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?

Despite 0% schema coverage, the description includes a docstring with explanations for each parameter (channel_id, max_videos, language, force_reindex) including examples and defaults, adding 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?

Description clearly states it pre-indexes video transcripts from a YouTube channel for pre-warming the semantic search index. It distinguishes itself from sibling tools like semantic_search_transcripts by noting it's optional and for faster first searches.

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 states when to use (pre-warming) and when not to use (not needed before searching). References alternative (semantic_search_transcripts) and provides context for faster searches.

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

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

Discloses caching (30 seconds), quota cost (1 unit), and return fields. As a read-only check, no side effects are implied. Without annotations, this is fairly transparent, though could explicitly state read-only.

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?

Core content is good, but appended generic caching/ref_id boilerplate adds unnecessary length. Could be more concise by removing system-wide notes specific to the server context.

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

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

Covers purpose, parameter, return format, example, caching, and alternative tool. Missing error handling scenarios and prerequisites (e.g., API key), but for a simple check, this is largely complete.

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

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

Adds significant meaning beyond schema: provides description and example for video_id ('YouTube video ID to check, e.g., dQw4w9WgXcQ'). This fully compensates for the 0% schema coverage.

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

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

The description clearly states it checks if a YouTube video is currently live, uses specific verb and resource, and distinguishes from sibling tools like search_live_videos which find live streams.

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 context for when to use (checking a specific video's live status) and mentions alternative search_live_videos(). Lacks explicit when-not-to-use, but overall guidance is clear.

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

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

With no annotations, the description covers key behaviors: permanent caching, no YouTube API quota, and the return type structure. It does not mention potential errors (e.g., invalid video ID) but overall provides good 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 sections, example, and notes. However, the caching boilerplate (ref_id, pagination) appears generic and may not apply to this small-result tool, adding slight verbosity. Otherwise concise and 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 low complexity (1 param) and presence of output schema, the description covers purpose, usage order, caching, and quota. It could mention error handling but is largely 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?

The single parameter 'video_id' is fully explained with an example value ('dQw4w9WgXcQ') and context (from URL or search), adding meaning beyond the schema's bare type definition. Schema coverage is 0% but description compensates fully.

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 lists available transcript languages for a YouTube video, with specific verbs ('list', 'discovers') and resource ('transcript languages'). It distinguishes itself from siblings like 'get_full_transcript' by focusing on discovery and availability.

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 advises calling this tool first before requesting a specific transcript, and explains caching and quota benefits. However, it does not explicitly state when not to use it or mention alternatives, leaving some implicit 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?

No annotations are provided, so the description carries full burden. It states it resets to default values and returns 'Reset context state', but does not disclose side effects, prerequisites, or what the return value entails. Minimal behavioral insight.

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

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

Two succinct sentences with no extraneous information. The structure is front-loaded with the main action. Could be slightly more efficient but is acceptable.

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 low complexity, the description lacks completeness. It does not explain when to reset, whether it affects other contexts, or detail the output (though an output schema exists). The return mention ('Reset context state') is vague. Sibling context suggests this is part of a test management flow, but no integration hints are given.

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 no parameters (100% coverage), so baseline is 3. The description adds no parameter info beyond the empty schema, but with zero parameters, no additional clarification is necessary. Score 4 for appropriate simplicity.

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 resets test context to default demo values, with a specific verb and target. It distinguishes from siblings like set_test_context by implying a revert operation, though not explicitly naming alternatives.

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

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

No guidance on when to use this tool versus siblings (set_test_context, enable_test_context). No exclusions or prerequisites mentioned. Usage is only implied by the action description.

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

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

With no annotations provided, the description fully discloses behavioral traits: results cached for 6 hours, search costs 100 quota units per request, pagination via ref_id, and caching namespace. This exceeds expectations for 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 main description is clear, but the later section about caching behavior and ref input compatibility appears generic and verbose, making the overall text longer than necessary. It could be more concise without losing essential 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 two parameters, the presence of an output schema (not shown but indicated), and no annotations, the description covers purpose, parameters, caching, quota, pagination, and output format. It is complete for a search tool.

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

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

Schema coverage is 0%, so the description must compensate. It explains 'query' with examples like 'Vimjoyer', and 'max_results' with range (1-50) and default (5). The description adds meaningful context beyond the raw 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 'Search for YouTube channels by query string.' and specifies the returned fields (names, descriptions, thumbnails, URLs). It distinguishes from sibling search tools like search_videos or search_live_videos by focusing on 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?

The description includes an example, mentions caching duration, quota cost, and how to paginate using get_cached_result. It does not explicitly mention when to avoid this tool (e.g., if exact channel info is needed via get_channel_info), but the context 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?

With no annotations, the description fully discloses behavior: results are cached for 6 hours, search costs 100 quota units, and the caching namespace. It also mentions reference ID handling and preview sizes. However, some caching details appear generic and may not be tool-specific.

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 includes a clear structured intro, Args, Returns, Example, and Note. However, it appends a large block of generic caching behavior text that is not specific to this tool, reducing conciseness.

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

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

The description covers purpose, parameters, return format (with output schema present), caching, quota, and related tools. It is complete for a simple tool with two parameters, though some cached behavior text may be extraneous.

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 0%, but the description's Args section adds meaning: query includes example queries, max_results explains range and default. Both parameters are well-explained beyond the schema types.

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

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

The description clearly states 'Search for currently live YouTube videos.' It specifies filtering to active broadcasts, uses a clear verb+resource format, and distinguishes from sibling tools like 'search_videos' by emphasizing live-only results.

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 when to use the tool, including examples and notes about caching and quota costs. It mentions related tools ('is_live' to check specific videos, 'get_live_chat_messages' for chat monitoring), but does not explicitly state when not to use it or compare directly with 'search_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?

No annotations are provided, so the description carries the full burden. It discloses caching, quota cost, and result fields, but includes a generic 'Caching Behavior' section that references ref_id and pagination mechanisms not present in the input schema. This introduces confusion and potential contradictions, reducing 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 front-loaded with the main purpose, but includes a lengthy, generic 'Caching Behavior' section that is likely copied and not specific to this tool. This adds unnecessary verbosity and 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 has an output schema, and the description explains return fields, caching, and quota costs. However, the generic caching section detracts from completeness by adding irrelevant details about ref_id and pagination that do not apply to this tool's parameters.

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

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

With 0% schema description coverage, the description fully compensates by providing clear, example-rich explanations for both parameters: query and max_results, including default and allowed range.

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 'search' and the resource 'YouTube videos', and distinguishes itself from siblings like search_live_videos and semantic_search_transcripts by focusing on standard video search.

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 about caching and quota costs, but does not explicitly guide when to use this tool versus alternatives like search_channels or semantic_search_all. Usage is implied rather than explicitly differentiated.

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

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

No annotations provided, so description carries full burden. It discloses the automatic indexing side effect and describes return format. It does not mention permission needs or rate limits, but for a search tool this is acceptable. It could be more explicit about resource usage.

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 Args and Returns sections and front-loaded purpose. It is longer than necessary but every sentence adds value. 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?

Given 9 parameters and an output schema, the description covers all parameters and explains return structure. It also mentions auto-indexing. It is complete but could include more on sorting behavior or examples.

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 0%, so description must add meaning. The Args section provides clear explanations for each parameter (e.g., query as 'Natural language search query', content_types as list of types), which fully compensates for the lack of 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 'Search across all content types (transcripts and comments)' and 'unified semantic search', using specific verbs and resource. It distinguishes from siblings like semantic_search_transcripts and semantic_search_comments by being the combined version.

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 it searches all content types and auto-indexes, and provides parameter examples. However, it lacks explicit guidance on when to use this tool vs the single-type semantic search tools (e.g., 'if you want both, use this; if only one, use the specific one').

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

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

No annotations, so description fully discloses auto-indexing, performance characteristics (first search slower), and scoping. Transparent about the process.

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 Args, Returns, Note sections. Slightly lengthy but front-loaded with summary. Minor redundancy (e.g., automatic indexing mentioned twice).

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 7 parameters, output structure, and important notes. Complete given the tool's complexity and presence of output schema.

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

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

Schema coverage is 0%, but description explains each parameter in detail with examples and defaults. Adds meaning beyond schema (e.g., 'Natural language search query', scoping).

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 it performs semantic similarity search over video comments with automatic indexing. Differentiates from sibling tools like semantic_search_transcripts by specifying 'comments'.

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 when to use (searching comments) and notes scoping behavior with channel_ids/video_ids. Does not explicitly state when not to use, but context with siblings implies it.

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

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It reveals auto-indexing, performance characteristics (slower first search), and scope details. It does not discuss permission requirements or potential side effects beyond indexing, leaving minor gaps.

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

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

The description is well-structured with a clear headline, functional description, parameter documentation, return value summary, and notes. Every sentence adds value, and the length is appropriate 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's moderate complexity (7 params, auto-indexing, multiple scope options) and the presence of an output schema (which excuses full return value specification), the description covers all essential aspects: purpose, parameters, behavior, performance, and scope. No critical 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 description coverage is 0%, but the description includes a detailed Args section with explanations and examples for all 7 parameters, adding significant meaning beyond the schema definitions (e.g., query example, default values, optionality).

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 searches transcripts using natural language with semantic similarity, and distinguishes it from sibling tools like semantic_search_all and semantic_search_comments by specifying the scope (video transcripts) and automatic indexing.

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 usage: when to use (natural language search over video transcripts), performance expectations (first search slower), and scope behavior (if no channel_ids/video_ids, searches all indexed content). However, it does not explicitly exclude alternatives or state when not to use this tool versus sibling search tools.

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

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

With no annotations, the description carries full burden. It explains that changes affect trace attributes and returns updated context state. However, it does not clarify whether the context is overwritten entirely or merged, nor its persistence or scope. Adequate but lacks depth.

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

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

The description is front-loaded with purpose, followed by a compact Args section and Returns. It is well-structured for an agent, though slightly verbose with examples. Every sentence adds value.

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

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

Given 4 optional parameters, an output schema, and sibling tools (enable, reset), the description covers the tool's role in the test context flow. It omits prerequisites (e.g., whether enable_test_context must be called first) but is otherwise complete for selection and invocation.

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

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

Schema description coverage is 0%, so the description compensates by describing each parameter (user_id, org_id, session_id, agent_id) with examples. This adds meaning beyond the schema's null/string types. However, the descriptions are embedded in prose rather than a dedicated parameter doc.

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

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

The description explicitly states the tool sets test context values for Langfuse attribution demos and identifies the specific attributes affected (user_id, session_id, metadata). It clearly distinguishes from siblings like reset_test_context and enable_test_context.

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

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

The description provides a clear use case: 'Use this to test filtering by different users or sessions in the Langfuse dashboard.' It implies when to use it but does not explicitly state when not to use it or mention alternatives beyond implied sibling names.

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

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

No annotations are provided, so the description carries full burden. It thoroughly discloses the warmup process (download, load, test embedding, init vector store), mentions model size (~270MB), time estimates (30-60 seconds), caching behavior, and memory persistence. Output fields are fully described, leaving no behavioral ambiguity.

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

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

The description is well-structured with clear sections (main purpose, process steps, return values, notes). Every sentence adds value, and the information is front-loaded. There is no redundancy.

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

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

Given no parameters and a detailed output schema, the description is complete. It explains why to use it, what happens during execution, and what the return values mean. It addresses performance implications and caching, making it fully informative.

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?

There are zero parameters, so baseline is 4. The description does not need to add additional parameter information, and it appropriately focuses on the tool's behavior and return 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's purpose: pre-loading the embedding model and vector store for semantic search. It uses specific verbs ('Pre-load', 'Downloads and initializes') and distinguishes itself from sibling semantic search tools by being a setup step to avoid timeouts.

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

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

The description explicitly says to call this before the first semantic search to avoid timeout, providing clear usage context. It does not explicitly state when not to use, but the context strongly implies it is only needed as a one-time warmup, making the guidance adequate.

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