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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return value ('Returns: knowledge base ID') but doesn't describe mutation behavior (this is clearly a write operation), permission requirements, rate limits, error conditions, or what happens when multiple knowledge sources are provided. For a tool with 5 parameters that modifies agent configuration, this is insufficient.

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

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

The description is appropriately concise with three clear components: action, return value, and usage context. It's front-loaded with the primary purpose. Every sentence earns its place, though the structure could be slightly improved by explicitly separating the three components.

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

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

Given 5 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameter relationships (e.g., whether url, input_file_path, and text are mutually exclusive or can be combined), doesn't describe the mutation's side effects, and provides minimal guidance on the knowledge base creation process. For a tool that modifies agent capabilities, this leaves significant 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 description coverage is 0%, so the description must compensate. It mentions 'documents or information' which hints at the url/input_file_path/text parameters, but doesn't explain their relationships, exclusivity, or how they map to knowledge base creation. The description doesn't address agent_id or knowledge_base_name parameters at all. With 5 parameters completely undocumented in schema, the description adds minimal semantic value.

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

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

The description clearly states the tool's purpose: 'Adds knowledge to agent' specifies the verb and resource. It distinguishes from siblings by focusing on knowledge base attachment rather than agent creation or conversation management. However, it doesn't explicitly differentiate from potential knowledge-related tools that might exist (though none are listed among siblings).

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

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

The description provides explicit usage context: 'Use when: giving agent access to documents or information.' This gives clear guidance on when to invoke this tool. However, it doesn't mention alternatives or exclusions (e.g., when not to use it, or how it differs from other knowledge management approaches).

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 the tool 'Returns: subscription details and usage' which describes output behavior. However, it doesn't disclose authentication requirements, rate limits, error conditions, or whether this is a read-only operation (though 'check' implies non-destructive). The description adds basic behavioral context but lacks completeness for a tool with zero annotation coverage.

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

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

The description is perfectly concise: two sentences with zero waste. The first states purpose and return values, the second provides usage guidelines. Every word earns its place, and information is front-loaded effectively.

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 no output schema, the description provides adequate coverage of purpose and usage. However, without annotations and with no output schema, it should ideally describe the return format more specifically (what 'subscription details and usage' includes) and mention any authentication or permission requirements. The description is complete enough for basic understanding but has room for improvement.

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

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

With 0 parameters and 100% schema description coverage, the baseline is 4. The description doesn't need to explain parameters since there are none, and it appropriately focuses on what the tool does rather than parameter details.

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: 'Checks account subscription' specifies the verb and resource. It distinguishes from siblings like 'list_agents' or 'get_conversation' by focusing on subscription status rather than listing or retrieving specific resources. However, it doesn't explicitly differentiate from potential subscription-related tools that might exist in other contexts.

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

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

The description provides explicit usage context: 'Use when: monitoring API usage and limits.' This gives clear guidance on when to invoke this tool. It doesn't specify when NOT to use it or name alternatives, but the context is sufficiently clear for a zero-parameter tool that returns subscription details.

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

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

No annotations are provided, so the description carries full burden. It mentions the tool 'Creates' (implying mutation/write operation) and 'Returns: agent ID and details' (output behavior), but lacks crucial behavioral context: permissions required, whether creation is idempotent, rate limits, error conditions, or what 'details' includes. For a complex creation tool with 17 parameters, this is insufficient.

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

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

The description is extremely concise (two sentences) and front-loaded with the core purpose. Every word earns its place: first sentence states action and return, second provides usage context. No wasted words or redundancy.

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

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

Given the complexity (17 parameters, creation/mutation operation), lack of annotations, and no output schema, the description is incomplete. It doesn't explain parameter meanings, creation constraints, error handling, or what 'details' in the return includes. For a tool that creates a complex resource with many configuration options, this leaves significant gaps in understanding.

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

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

Schema description coverage is 0% (no parameter descriptions in schema), and the tool has 17 parameters (3 required). The description provides NO parameter information whatsoever - it doesn't explain what 'name', 'first_message', 'system_prompt', or any of the 14 optional parameters mean or how they affect agent creation. This leaves the agent guessing about parameter purposes and interactions.

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: 'Creates conversational AI agent' with specific verb+resource. It distinguishes from siblings like 'get_agent' (read) and 'list_agents' (list), but doesn't explicitly contrast with other creation tools like 'create_voice_from_preview'.

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

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

The description provides explicit usage context: 'Use when: setting up voice-enabled chatbot or assistant.' This gives clear guidance on when to invoke the tool. However, it doesn't mention when NOT to use it or alternatives (e.g., using existing agents vs. creating new ones).

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

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

No annotations are provided, so the description carries full burden. It mentions the tool 'Saves' (implying a write operation) and returns a 'permanent voice ID', but doesn't disclose critical behavioral traits like permissions needed, whether the operation is idempotent, rate limits, or what happens if the generated_voice_id is invalid. For a write operation with zero annotation coverage, this is inadequate.

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 two sentences that each serve distinct purposes: stating the action and specifying usage context. It's front-loaded with the core functionality and wastes no words.

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

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

Given the tool has 3 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameter semantics, behavioral details, or return format beyond a brief mention of 'permanent voice ID'. For a write operation with multiple inputs, more context is needed.

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

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It doesn't explain any of the three parameters (generated_voice_id, voice_name, voice_description) beyond what's in their titles. No additional meaning is provided about format, constraints, or examples, leaving significant gaps.

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

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

The description clearly states the tool's purpose with specific verbs ('Saves generated voice to library') and identifies the resource ('voice from text_to_voice previews'). It distinguishes from siblings by specifying it's for saving previews, unlike tools like 'get_voice' or 'search_voice_library'. However, it doesn't explicitly differentiate from 'voice_clone' or other voice creation tools.

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

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

The description provides explicit usage context with 'Use when: keeping voice from text_to_voice previews', clearly indicating when this tool should be used. It doesn't specify when NOT to use it or name specific alternatives, but the context is sufficient for basic 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 the full burden. It states the tool 'Returns: enhanced text with v3 tags,' which partially describes output behavior. However, it lacks critical details: whether this is a read-only or mutating operation, any rate limits, authentication requirements, or error conditions. For a tool with no annotations, this leaves significant behavioral 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 extremely concise and front-loaded: three brief sentences that cover purpose, output, and usage. Every sentence adds value without redundancy. The structure is efficient, making it easy for an agent to parse quickly.

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

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

Given no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers basic purpose and usage but lacks details on parameters, behavioral traits (e.g., side effects), and output specifics beyond 'v3 tags.' For a tool with one parameter but no structured documentation, this leaves too many gaps for reliable agent 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 description coverage is 0%, so the description must compensate. It mentions 'dialogue' but doesn't explain the 'dialogue_blocks' parameter's format, content expectations, or constraints (e.g., length, language). The description adds minimal semantic value beyond the schema's title ('Dialogue Blocks'), failing to address the coverage gap adequately.

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: 'Adds audio tags to dialogue' specifies the verb (adds) and resource (audio tags to dialogue). It distinguishes from most siblings (e.g., text_to_speech, get_v3_tags) by focusing on enhancement rather than generation or retrieval. However, it doesn't explicitly differentiate from text_to_dialogue or text_to_sound_effects, which might have overlapping functions.

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

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

The description provides explicit usage guidance: 'Use when: improving dialogue with emotions and effects.' This gives clear context for when to apply the tool. It doesn't specify when NOT to use it or name alternatives (e.g., text_to_dialogue), but the context is sufficiently clear for an agent to understand its primary use case.

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

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

With no annotations provided, the description carries the full burden. It indicates this is a read operation ('Lists') and hints at the output ('comprehensive tag list'), but lacks details on rate limits, authentication needs, or error handling. It's adequate but has clear gaps in behavioral disclosure.

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

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

The description is highly concise and front-loaded, with two sentences that efficiently convey purpose and usage without any wasted words. Every sentence adds clear value, making it easy for an agent to parse.

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

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete for a list operation. However, it lacks details on output format (e.g., structure of the tag list) and doesn't address potential limitations or dependencies, leaving room for improvement.

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

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

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description doesn't need to add parameter information, and it appropriately avoids redundancy, earning a baseline score of 4 for this 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 ('Lists') and resource ('v3 audio tags'), making the purpose understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_v3_audio_tags_guide', which appears to serve a similar purpose, preventing a perfect score.

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

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

The description provides explicit usage context ('Use when: preparing text for text_to_speech_v3 or text_to_dialogue with emotions and effects'), which is helpful for guiding the agent. It doesn't mention when not to use it or alternatives like the sibling tool, so it falls short of a 5.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a read operation ('Gets') and returns configuration details, but doesn't cover aspects like authentication needs, rate limits, error handling, or whether it's idempotent. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational traits.

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

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

The description is two concise sentences that are front-loaded with the core purpose and usage guideline. Every word earns its place without redundancy, making it easy to parse quickly. The structure efficiently communicates essential information in a minimal 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?

Given no annotations, no output schema, and low parameter schema coverage, the description is incomplete. It covers the basic purpose and usage but lacks details on behavior, parameter meaning, return format, or error conditions. For a tool that retrieves configuration data, more context is needed to ensure reliable agent 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?

The input schema has 1 parameter with 0% description coverage, so the schema provides no semantic context. The description doesn't mention the 'agent_id' parameter at all, failing to compensate for the low coverage. However, with only one parameter, the baseline is higher; the description implies a specific agent is needed but doesn't explain what 'agent_id' represents or its format, leaving minimal added value.

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

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

The description clearly states the verb ('Gets') and resource ('agent details'), specifying it returns 'agent configuration'. It distinguishes from siblings like 'list_agents' by focusing on a single agent, but doesn't explicitly contrast with 'create_agent' or others. The purpose is specific and actionable, though sibling differentiation is implied rather than explicit.

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

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

The description includes a 'Use when' clause ('viewing specific agent settings and capabilities'), providing clear context for when to invoke this tool. It implies usage for detailed inspection rather than listing, but doesn't explicitly state when not to use it or name alternatives like 'list_agents' for broader queries. The guidance is helpful but lacks exclusions or direct sibling comparisons.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool returns conversation details and a full transcript, which is helpful, but it doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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 extremely concise and well-structured, with two clear sentences: one stating the purpose and return values, and another providing usage guidelines. Every word earns its place, and there is no unnecessary information or redundancy.

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

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

Given the complexity of a tool that retrieves conversation data with three parameters, no annotations, and no output schema, the description is incomplete. It lacks details on parameter meanings, behavioral traits (e.g., read-only status, error handling), and output structure, making it inadequate for the agent to fully understand and 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?

The schema description coverage is 0%, meaning none of the three parameters (conversation_id, wait_for_completion, include_analysis) are documented in the schema. The description adds no information about these parameters, failing to compensate for the lack of schema documentation. This leaves the agent with no guidance on what these parameters mean or how to use them effectively.

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

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

The description clearly states the tool's purpose with a specific verb ('Gets') and resource ('conversation with transcript'), and distinguishes it from the sibling tool 'get_conversation_transcript' by mentioning it returns both conversation details and full transcript. However, it doesn't explicitly differentiate from other conversation-related tools like 'list_conversations'.

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

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

The description provides explicit usage guidance with 'Use when: analyzing completed agent conversations,' which clearly indicates the intended context. It doesn't specify when not to use it or name alternatives, but the context is sufficiently clear for effective tool selection.

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

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

No annotations are provided, so the description carries full burden. It mentions 'in chunks' and 'large conversation transcripts', hinting at pagination or size handling, but lacks details on rate limits, authentication needs, error conditions, or what 'metadata' includes. For a tool with 4 parameters and no annotations, this is insufficient behavioral disclosure.

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

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

The description is front-loaded and highly concise: two sentences with zero waste. The first sentence states the purpose and output, the second provides usage guidance—every word earns its place 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 complexity (4 parameters, no annotations, no output schema), the description is incomplete. It lacks details on parameter meanings, behavioral traits like pagination or errors, and output specifics. For a tool that handles 'large' data in chunks, more context is needed to guide 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 description coverage is 0%, so the description must compensate. It doesn't explain any parameters beyond implying 'chunk' and 'chunk_size' relate to chunking, but doesn't clarify their roles, units, or interactions. With 4 parameters (conversation_id, format, chunk, chunk_size) and no schema descriptions, this leaves significant gaps in 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 the verb ('Gets') and resource ('conversation transcript'), specifying it returns data 'in chunks' with 'transcript chunk with metadata'. It distinguishes from siblings like 'get_conversation' by focusing on transcripts rather than general conversation data. However, it doesn't explicitly contrast with all siblings, keeping it at 4 instead of 5.

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 provides explicit usage guidance with 'Use when: retrieving large conversation transcripts', which clearly indicates the context for this tool. However, it doesn't mention when NOT to use it or name specific alternatives (e.g., 'get_conversation' for non-transcript data), so it falls short of a perfect 5.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return type ('detailed v3 documentation') but lacks details on format, structure, or any behavioral traits like error handling or performance. This is a significant gap for a tool with no annotation coverage.

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

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

The description is front-loaded and concise, with two sentences that efficiently convey purpose and usage without wasted words. Every sentence adds value, making it highly 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?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers purpose and usage but lacks details on return format or behavioral context, which could be helpful for an agent to understand what to expect.

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

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

The tool has 0 parameters with 100% schema description coverage, so the baseline is high. The description doesn't need to add parameter information, and it doesn't introduce any confusion, making it appropriate for the 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's purpose with a specific verb ('Gets') and resource ('v3 tag usage guide'), and distinguishes it from sibling tools like 'fetch_v3_tags' by focusing on documentation rather than tag data. However, it doesn't explicitly differentiate from all siblings, keeping it at a 4 instead of 5.

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

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

The description provides explicit usage guidance with 'Use when: learning how to use v3 audio tags effectively,' which clearly indicates the context for invoking this tool. It doesn't specify when not to use it or name alternatives, so it's not a full 5.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool returns 'voice metadata and settings' which provides some output context, but doesn't address important behavioral aspects like whether this is a read-only operation, authentication requirements, rate limits, error conditions, or what happens with invalid voice IDs. The description adds minimal behavioral context beyond the basic purpose.

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 just two sentences that are front-loaded with purpose and usage guidance. Every word serves a purpose with zero wasted text. The structure efficiently separates purpose, returns, and usage context in minimal 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?

For a tool with no annotations, no output schema, and 0% parameter documentation coverage, the description is incomplete. While it states the purpose and usage context, it doesn't adequately explain what 'voice details' includes, how to obtain valid voice IDs, what authentication is required, or what the return structure looks like. The description leaves too many operational questions unanswered.

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 for the single parameter 'voice_id', the description provides no information about what constitutes a valid voice ID, where to find voice IDs, format requirements, or examples. The description mentions 'specific voice' which implies the parameter identifies a voice, but this is already obvious from the parameter name. The description fails to compensate for the complete lack of schema documentation.

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

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

The description states the tool 'gets voice details' which is a clear verb+resource combination, but it's somewhat vague about what constitutes 'details' and doesn't distinguish this tool from sibling tools like 'get_voice_id_by_name' or 'search_voices' that also retrieve voice information. The description avoids tautology but lacks specificity about what makes this tool unique.

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

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

The description provides explicit usage guidance with 'Use when: need detailed information about a specific voice,' which clearly indicates the appropriate context. However, it doesn't mention when NOT to use this tool or explicitly name alternatives like 'get_voice_id_by_name' or 'search_voices' for different use cases.

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

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

With no annotations provided, the description carries the full burden and does well by disclosing the return format ('JSON with voice_id and confidence') and behavioral trait ('fuzzy matching'). It doesn't mention error handling, rate limits, or authentication needs, but covers core functionality adequately for a lookup tool.

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

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

The description is highly concise and front-loaded, with two sentences that efficiently cover purpose, output, and usage guidelines without any wasted words, making it easy for an agent to parse quickly.

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

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

For a simple lookup tool with 1 parameter, no annotations, and no output schema, the description is reasonably complete: it explains what it does, when to use it, the return format, and a key behavior (fuzzy matching). It could improve by mentioning error cases or input format expectations, but it's sufficient for basic use.

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

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

The schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by explaining that 'voice_name' is used for 'fuzzy matching' to resolve to an ID, which clarifies the parameter's role beyond the schema's basic type definition, though it doesn't detail format constraints or examples.

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

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

The description clearly states the tool's purpose with a specific verb ('Resolves') and resource ('voice name to ID'), distinguishing it from siblings like 'get_voice', 'search_voice_library', or 'search_voices' by focusing on name-to-ID resolution rather than broader voice retrieval or search 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?

It explicitly provides usage guidance with 'Use when: need voice ID from name with fuzzy matching', specifying the context (name-to-ID conversion) and a key feature (fuzzy matching) that helps differentiate it from alternatives, though it doesn't name specific sibling tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return value ('Returns: cleaned audio file path') which is helpful, but doesn't address important behavioral aspects like processing time, file format requirements, error conditions, or whether the operation modifies the original file. The description adds some value but leaves significant 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 extremely concise with just two sentences that each serve distinct purposes: stating the core functionality and providing usage guidance. There's zero wasted language, and the information is front-loaded effectively.

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 an audio processing tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It explains the purpose and when to use it but lacks critical details about parameters, file requirements, processing behavior, and error handling that would be needed for reliable tool 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?

With 0% schema description coverage and 2 parameters, the description provides no information about what 'input_file_path' or 'output_directory' mean, their expected formats, or constraints. The schema alone shows basic types but no semantic context, and the description fails to compensate for this significant 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 the specific action ('Removes background noise from audio') and the resource being acted upon ('audio'), with a distinct purpose from sibling tools like 'enhance_dialogue' or 'speech_to_text' that focus on different audio processing tasks.

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

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

The description provides explicit usage context with 'Use when: extracting voice from noisy recordings', which clearly indicates when to apply this tool. However, it doesn't specify when NOT to use it or mention alternatives among the many sibling audio processing 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?

No annotations are provided, so the description carries full burden. It states 'Returns: agent list with IDs', which gives basic output information, but lacks details on behavioral traits like pagination, sorting, filtering, rate limits, authentication requirements, or error handling. This is insufficient for a tool with no annotation coverage.

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

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

The description is extremely concise and well-structured: it states the action, output, and usage context in just two short sentences with zero wasted words. It's front-loaded with the core purpose, making it easy to scan and understand quickly.

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

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

Given no annotations and no output schema, the description is incomplete. It mentions the return includes 'agent list with IDs', but doesn't describe the structure of the list, what fields are included beyond IDs, or any metadata. For a list operation with zero structured data support, more detail on output format is needed.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and output. A baseline of 4 is applied as it efficiently handles the lack of parameters.

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

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

The description clearly states the action ('Lists all agents') and the resource ('agents'), making the purpose immediately understandable. It distinguishes from siblings like 'get_agent' (singular) by specifying 'all agents', but doesn't explicitly contrast with other list tools like 'list_conversations' or 'list_models' beyond the resource name.

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 a 'Use when:' clause ('viewing available conversational AI agents'), which provides basic context for when to invoke this tool. However, it doesn't specify when NOT to use it or mention alternatives (e.g., 'get_agent' for details on a specific agent), leaving some ambiguity in usage scenarios.

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 states it 'Returns: conversation list with metadata', which gives basic output information, but lacks critical behavioral details: it doesn't mention pagination behavior (implied by limit/offset parameters but not explained), authentication requirements, rate limits, error conditions, or whether it's read-only (though 'Lists' implies it). For a tool with 4 parameters and no annotation coverage, this is insufficient.

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

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

The description is extremely concise (two brief sentences) and front-loaded with the core purpose. Every word earns its place: the first sentence states what it does and returns, the second provides usage guidance. There's no redundancy or unnecessary elaboration.

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

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

Given the complexity (4 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and usage context but misses parameter explanations, detailed behavioral transparency (like pagination or auth), and output specifics beyond 'conversation list with metadata'. For a listing tool with filtering parameters, this leaves significant gaps for an AI agent to use it 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 0%, so the schema provides only parameter names and types without descriptions. The description adds no parameter semantics whatsoever—it doesn't explain what 'agent_id', 'status', 'limit', or 'offset' mean, their expected formats, or how they affect the listing. With 4 undocumented parameters, the description fails to compensate 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 the verb ('Lists') and resource ('agent conversations'), making the purpose immediately understandable. It distinguishes from sibling tools like 'get_conversation' (which retrieves a specific conversation) by indicating it returns a list. However, it doesn't explicitly differentiate from other list tools like 'list_agents' or 'list_phone_numbers' beyond the resource type.

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 a 'Use when:' clause ('browsing conversation history'), which provides clear context for when to invoke this tool. This helps distinguish it from 'get_conversation' (for detailed view of a single conversation). However, it doesn't specify when NOT to use it or mention alternatives like filtering conversations by other criteria not covered by the parameters.

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 that the tool returns 'model list with capabilities' which gives some behavioral context about the output. However, it doesn't mention important behavioral traits like whether this is a read-only operation, if there are rate limits, authentication requirements, or what format the capabilities information takes.

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

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

The description is perfectly concise and well-structured: two sentences that each serve distinct purposes. The first states what the tool does and what it returns. The second provides explicit usage guidance. There is zero wasted language or redundancy.

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

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

Given the tool has no parameters and no output schema, the description provides adequate but minimal information. It explains the purpose and when to use it, but doesn't describe the return format in detail ('model list with capabilities' is vague) or mention any prerequisites or constraints. For a simple list operation, this is acceptable but could be more 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 tool has 0 parameters with 100% schema description coverage. The description doesn't need to explain any parameters, which is appropriate. The baseline for 0 parameters is 4, and the description correctly focuses on the tool's purpose and usage rather than parameter details.

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: 'Lists available TTS models' specifies the verb (list) and resource (TTS models). It distinguishes from siblings like list_agents or list_conversations by focusing on TTS models specifically. However, it doesn't explicitly differentiate from text_to_speech or text_to_speech_v3 tools which might also involve model selection.

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

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

The description provides explicit usage guidance: 'Use when: choosing between v2, v3, or other models.' This gives clear context for when to invoke this tool. However, it doesn't specify when NOT to use it or mention alternatives like checking subscription status or fetching tags that might also inform model selection.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists phone numbers and returns a list, but lacks details on permissions required, rate limits, pagination, or error conditions. For a read operation with zero annotation coverage, this is a significant gap in transparency about how the tool behaves beyond its basic function.

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 and front-loaded: it states the purpose in the first clause, the return value in the second, and usage guidelines in the third. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

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

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description covers the basics: what it does and when to use it. However, it lacks details on return format (e.g., structure of the 'phone number list'), error handling, or any behavioral constraints. For a simple list tool, this is minimally adequate but leaves gaps an agent might need to infer.

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

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

The tool has 0 parameters, and schema description coverage is 100% (though empty). The description doesn't need to explain parameters, so it appropriately focuses on the tool's purpose and usage. No parameter information is missing or needed, making this adequate for a parameterless tool.

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

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

The description clearly states the action ('Lists') and resource ('account phone numbers'), making the purpose immediately understandable. It distinguishes itself from siblings like 'make_outbound_call' or 'get_voice' by focusing on listing phone numbers rather than using them or managing other resources. However, it doesn't explicitly differentiate from potential similar listing tools (though none exist in the sibling 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 provides clear context for when to use this tool: 'viewing available numbers for outbound calls.' This gives the agent a specific scenario for invocation. It doesn't explicitly state when NOT to use it or name alternatives, but the context is sufficient for basic guidance given the tool's straightforward nature.

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

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

No annotations are provided, so the description carries the full burden. It mentions that the tool 'Returns: call details,' which adds some behavioral context, but it fails to disclose critical traits like whether this is a read-only or destructive operation, authentication needs, rate limits, or error handling. For a tool that initiates calls, this is a significant gap.

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

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

The description is front-loaded and efficiently structured in two sentences: one stating the purpose and return, and another providing usage context. Every sentence adds value without redundancy, making it 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's complexity (initiating calls with 3 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, parameter meanings, and expected outputs, making it inadequate for an agent to fully understand how to use this 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 0%, so the description must compensate for undocumented parameters. It does not add any meaning beyond what the input schema provides (e.g., explaining what 'agent_id' or 'to_number' represent). The description only mentions the tool's purpose and usage, leaving all three parameters semantically unclear.

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 ('Initiates phone call') and the resource ('with agent'), making the purpose understandable. It distinguishes this tool from siblings by focusing on outbound calling, though it doesn't explicitly contrast with similar tools (e.g., 'play_audio' or 'text_to_speech').

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 provides clear context for when to use the tool ('making automated calls via Twilio integration'), which helps differentiate it from other voice-related tools. However, it lacks explicit exclusions or alternatives, such as when not to use it or what other tools might be better for different scenarios.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the action ('Plays audio file locally') and return value ('playback confirmation'), but lacks details about audio formats supported, playback duration limits, error conditions, or system requirements. It provides basic context but misses important behavioral traits.

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

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

The description is extremely concise with just two sentences that each serve distinct purposes: stating the action and return value, then providing usage guidance. Every word earns its place with zero wasted text, and the 'Use when:' structure is particularly 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?

For a single-parameter tool with no annotations and no output schema, the description provides adequate basic context about what the tool does and when to use it. However, it lacks important details about parameter requirements, audio format support, and behavioral constraints that would be needed for reliable use by 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?

With 0% schema description coverage for the single parameter 'input_file_path', the description provides no parameter information beyond what the schema already shows. It doesn't explain what format the file path should be, what audio formats are supported, or any constraints on the input file. The description fails to compensate for the schema's lack of documentation.

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

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

The description clearly states the tool's purpose with a specific verb ('Plays') and resource ('audio file locally'), and distinguishes it from download operations. However, it doesn't explicitly differentiate from sibling audio tools like 'text_to_speech' or 'speech_to_speech' beyond the 'previewing' 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 explicit usage context ('Use when: previewing generated audio without downloading'), which clearly indicates when to use this tool. However, it doesn't specify when NOT to use it or name alternatives for similar audio playback scenarios 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return type ('shared voices list') but doesn't describe pagination behavior, rate limits, authentication requirements, or whether this is a read-only operation. For a search tool with zero annotation coverage, this leaves significant behavioral 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 extremely concise with just two sentences that each serve distinct purposes: stating the action/return value and providing usage guidance. It's front-loaded with the core functionality and wastes no words. Every sentence earns its place.

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

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

Given 3 parameters with 0% schema coverage, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain parameters, return format details, pagination behavior, or error conditions. For a search tool with multiple parameters, this leaves too much unspecified for effective agent use.

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

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. The description mentions no parameters at all - it doesn't explain what 'search', 'page', or 'page_size' parameters do or how they affect results. With 3 parameters completely undocumented in both schema and description, this is inadequate.

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 ('Searches') and resource ('global voice library'), and specifies the scope ('across entire ElevenLabs platform'). It distinguishes from sibling 'search_voices' by emphasizing global scope. However, it doesn't explicitly contrast with 'get_voice' or 'get_voice_id_by_name', leaving some ambiguity about when to use those 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?

The description provides explicit usage context with 'Use when: finding voices across entire ElevenLabs platform', which clearly indicates the primary scenario. It doesn't specify when NOT to use it or explicitly name alternatives like 'search_voices', but the global scope implication provides reasonable guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns 'JSON with voice details' but lacks critical information about pagination, rate limits, authentication requirements, error conditions, or what specific voice details are included. For a search tool with zero annotation coverage, this leaves significant behavioral 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 perfectly concise with two sentences that each earn their place: the first states the purpose and return format, the second provides usage guidelines. It's front-loaded with essential information and contains zero wasted words or redundant phrasing.

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

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

Given the tool's moderate complexity (4 parameters, search functionality), no annotations, and no output schema, the description is incomplete. It doesn't explain the return structure beyond 'JSON with voice details,' doesn't address parameter interactions or defaults, and leaves behavioral aspects like error handling undocumented. The description should provide more context for effective tool 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 description coverage is 0%, so the description must compensate for undocumented parameters. It mentions searching 'by name, gender, or characteristics,' which partially explains the 'search' parameter but ignores the three other parameters (sort, sort_direction, return_format) entirely. The description adds minimal value beyond what's implied by parameter names and enums in 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 verb ('Searches') and resource ('available voices'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'get_voice' or 'get_voice_id_by_name' by emphasizing search functionality rather than retrieval of specific voices. However, it doesn't explicitly contrast with 'search_voice_library', which appears to be a similar sibling tool.

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

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

The description provides explicit usage guidance with 'Use when: finding voices by name, gender, or characteristics,' giving clear context for when to invoke this tool. It doesn't mention when NOT to use it or name specific alternatives like 'search_voice_library', but the guidance is sufficiently detailed for basic decision-making.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns 'audio file with new voice', which hints at output behavior, but doesn't cover critical aspects like required permissions, rate limits, file format support, or whether the transformation is reversible. For a tool that modifies audio, this lack of detail on behavioral traits is a significant gap.

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

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

The description is appropriately sized and front-loaded: it starts with the core action ('Transforms voice in audio'), states the return value, and provides usage guidance in a single, efficient sentence. Every part earns its place without redundancy or unnecessary elaboration.

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

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

Given the tool's complexity (audio transformation with 3 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on behavioral traits, parameter meanings, and output specifics. While concise, it doesn't provide enough context for safe and effective use, especially for a mutation tool with undocumented inputs.

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%, meaning parameters are undocumented in the schema. The description adds no information about parameters beyond what's implied by the tool's purpose. It doesn't explain what 'input_file_path', 'voice_name', or 'output_directory' mean, their formats, or constraints. With 3 parameters and low coverage, the description fails to compensate, leaving semantics unclear.

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: 'Transforms voice in audio' and 'changing speaker voice in existing audio', which specifies the verb (transform/change) and resource (voice in audio). It distinguishes from siblings like 'speech_to_text' or 'voice_clone' by focusing on voice transformation rather than transcription or cloning. However, it doesn't explicitly differentiate from 'text_to_speech' or 'text_to_voice', which involve voice generation rather than modification.

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 'Use when: changing speaker voice in existing audio', which provides clear context for when to use this tool. However, it doesn't specify when not to use it or mention alternatives among siblings, such as using 'voice_clone' for creating new voices or 'speech_to_text' for transcription instead. This implied usage is helpful but lacks explicit exclusions or comparisons.

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

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

No annotations are provided, so the description carries the full burden. It mentions return behavior ('Returns: transcript text or file path'), which adds some value beyond the input schema. However, it doesn't disclose critical behavioral traits like rate limits, authentication needs, error conditions, or how the 'save_transcript_to_file' and 'return_transcript_to_client_directly' parameters interact. For a tool with 6 parameters and no annotations, this leaves significant 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 appropriately sized with two sentences that are front-loaded: the first states the purpose, and the second provides usage and return info. There's no wasted text, and it's structured for quick comprehension. However, it could be slightly more polished (e.g., combining clauses), but it's efficient overall.

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

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

Given the complexity (6 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and return types but lacks details on parameter usage, behavioral constraints, error handling, and output format. Without annotations or an output schema, the description should do more to guide the agent, especially for a tool with multiple configuration options like diarization and output 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?

Schema description coverage is 0%, so the description must compensate. It doesn't explain any of the 6 parameters beyond what the schema titles provide (e.g., 'input_file_path' is implied by 'audio' but not detailed). The description mentions return types related to 'save_transcript_to_file' and 'return_transcript_to_client_directly', but doesn't clarify their semantics or interactions. With low coverage and no parameter explanation, it fails to add meaningful value.

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

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

The description clearly states the tool's purpose: 'Transcribes audio to text' with a specific verb ('transcribes') and resource ('audio to text'). It distinguishes itself from sibling tools like 'text_to_speech' or 'isolate_audio' by focusing on transcription. However, it doesn't explicitly differentiate from potential similar tools (none in the sibling list), so it's not a perfect 5.

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 a 'Use when:' clause: 'converting speech recordings to text,' which provides basic context for when to use it. However, it lacks explicit guidance on when not to use it (e.g., vs. 'get_conversation_transcript' for existing transcripts) or alternatives, and doesn't mention prerequisites like file format support. This is implied usage rather than comprehensive 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 the tool returns 'dialogue audio file paths' (output format) but lacks critical behavioral details: whether it's a read-only or mutating operation, authentication requirements, rate limits, file format specifics, or error handling. For a tool with 4 parameters and no annotations, this is insufficient.

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

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

The description is extremely concise (two sentences) and front-loaded with the core purpose first, followed by usage guidance. Every word earns its place with zero 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?

Given 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers basic purpose and usage but lacks parameter explanations, behavioral context (e.g., whether it's a heavy computation), and detailed output information. For a complex audio generation tool, this leaves significant 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 description coverage is 0%, so the description must compensate. It mentions 'multi-speaker text' (hinting at 'inputs' parameter) and 'audio file paths' (related to output), but provides no details about 'output_directory', 'stability', or 'similarity_boost' parameters. The description adds minimal value 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's purpose: 'Converts multi-speaker text to audio' (specific verb+resource). It distinguishes from siblings like text_to_speech (single voice) and text_to_sound_effects (non-dialogue audio). However, it doesn't explicitly contrast with text_to_voice or text_to_speech_v3, which might have overlapping functionality.

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

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

The description provides explicit usage guidance: 'Use when: creating conversations with multiple voices.' This clearly indicates the intended context (multi-speaker scenarios). However, it doesn't specify when NOT to use it (e.g., vs. text_to_speech for single voice) or name specific 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'Returns: audio file path,' which is useful context about the output. However, it doesn't disclose important behavioral traits like whether this is a read-only or destructive operation, potential rate limits, authentication needs, or error conditions. For a tool with no annotations, this leaves significant 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 appropriately concise with two sentences that each serve a clear purpose: the first states what the tool does, the second provides usage guidance. It's front-loaded with the core functionality. However, the 'Returns: audio file path' could be integrated more smoothly rather than as a fragment.

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

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

Given the complexity (4 parameters, no output schema, no annotations), the description is incomplete. It explains the basic purpose and provides minimal usage guidance but fails to document parameters, behavioral details, or output format. For a tool that generates audio files with multiple configuration options, this leaves too much unspecified.

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%, meaning none of the 4 parameters have descriptions in the schema. The tool description doesn't mention any parameters at all, failing to compensate for the schema's lack of documentation. Parameters like 'text,' 'duration_seconds,' 'output_directory,' and 'output_format' remain completely unexplained in both the schema and description.

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: 'Generates sound effects from text' specifies both the verb (generates) and resource (sound effects). It distinguishes from siblings like text_to_speech or text_to_dialogue by focusing specifically on sound effects rather than speech or dialogue. However, it doesn't explicitly contrast with all possible 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?

The description includes a 'Use when:' clause: 'creating custom sound effects from descriptions,' which provides some context for when to use this tool. However, it doesn't specify when NOT to use it or mention alternatives like text_to_speech for voice generation or other audio tools. The guidance is helpful but incomplete.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the return value ('Returns: audio file path') and model support, but lacks critical behavioral details: whether this is a read-only or write operation, authentication requirements, rate limits, error conditions, or what happens with the generated audio file. For a tool with 12 parameters and no annotation coverage, this is insufficient.

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

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

The description is reasonably concise with three short clauses. It's front-loaded with the core purpose, though could be more structured. Every sentence contributes information, though the 'Supports v3 with audio tags' feels tacked on rather than integrated into a coherent narrative.

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

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

Given the complexity (12 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the relationship between parameters, doesn't clarify what 'audio file path' means in practice, and doesn't address how the numerous configuration options interact. For a sophisticated TTS tool with many tuning parameters, this description leaves too much unexplained.

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 for 12 parameters, the description fails to compensate adequately. It mentions 'v2/v3/flash models' which relates to the 'model' parameter, and 'audio tags' which might relate to v3 features, but provides no explanation for the other 11 parameters like 'stability', 'similarity_boost', 'style', etc. The description adds minimal value beyond what's in 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's purpose: 'Converts text to speech' with specific model versions mentioned (v2/v3/flash). It distinguishes from some siblings like 'text_to_dialogue' or 'text_to_sound_effects' by focusing on single-speaker narration, though it doesn't explicitly differentiate from 'text_to_speech_v3' or 'text_to_voice' which appear to be similar tools.

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

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

The description provides some usage guidance with 'Use when: single speaker narration' and mentions support for 'v3 with audio tags', which implies a specific use case. However, it doesn't explain when to choose this tool over alternatives like 'text_to_speech_v3' or 'text_to_voice' from the sibling list, nor does it provide exclusion criteria or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the return value ('Returns: audio file path') which is helpful, but doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what happens if the output directory doesn't exist. The description adds some value but leaves significant 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 extremely concise and well-structured: purpose statement, return value, and usage guidance all in three clear, front-loaded sentences. Every sentence earns its place with 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 the complexity (6 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and when to use it, but doesn't explain parameter meanings, error conditions, or detailed behavioral characteristics. The return value is mentioned, but without an output schema, more detail about the audio file format or potential errors would be helpful.

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

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

The schema description coverage is 0%, meaning none of the 6 parameters have descriptions in the schema. The tool description mentions 'v3 model and tags' which hints at some parameters, but doesn't explain what any of the specific parameters (text, voice_name, output_directory, voice_id, stability, similarity_boost) actually mean or how they affect the output. This is inadequate for a tool with 6 parameters.

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

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

The description clearly states the tool's purpose: 'Converts text to speech with v3 model and tags.' It specifies the action (converts), resource (text to speech), and model version (v3). However, it doesn't explicitly differentiate from sibling tools like 'text_to_speech' or 'text_to_dialogue' that might offer different functionality.

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

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

The description provides explicit usage guidance: 'Use when: single speaker needs emotions, pauses, or sound effects.' This gives clear context about when this tool is appropriate. However, it doesn't mention when NOT to use it or name specific alternatives among the sibling tools.

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

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

No annotations are provided, so the description carries full burden. It discloses the return value ('Returns: three voice preview files'), which is valuable behavioral information. However, it doesn't mention other important traits like whether this creates permanent resources, requires authentication, has rate limits, or what format the preview files are in. The description adds some context but leaves significant 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 extremely concise and front-loaded: three short sentences that each earn their place (purpose, return value, usage context). Zero wasted words or redundancy.

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

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

Given 3 parameters with 0% schema coverage, no annotations, and no output schema, the description provides basic purpose and return information but lacks parameter explanations, behavioral details, and output format specifics. It's minimally adequate for understanding what the tool does but insufficient for confident usage without additional documentation.

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 must compensate. It mentions 'voice_description' and 'text prompt' contextually, but doesn't explain any of the three parameters (voice_description, text, output_directory) beyond what's in their titles. The description adds minimal semantic value 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's purpose: 'Creates voice from description' specifies the action (creates) and resource (voice). It distinguishes from siblings like text_to_speech (which likely generates speech from text rather than creating a voice model) and voice_clone (which might clone existing voices). However, it doesn't explicitly contrast with all similar siblings.

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

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

The description provides explicit usage guidance: 'Use when: designing custom voice from text prompt.' This clearly indicates the intended context. It doesn't specify when NOT to use it or name specific alternatives, but the context is well-defined for the given 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?

With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the return value ('Returns: new voice ID'), it doesn't address important behavioral aspects: required permissions, whether this is a long-running operation, rate limits, audio format requirements, or what happens if creation fails. For a creation tool with zero annotation coverage, this leaves significant 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 extremely concise and well-structured: three short phrases separated by periods, each serving a distinct purpose (action, return value, usage context). No wasted words, and the most important information (what it does) comes first.

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

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

For a creation tool with 3 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. While it states the basic purpose and return, it lacks crucial details about parameter requirements, behavioral constraints, error conditions, and what the voice ID can be used for. The agent would need to guess about many operational aspects.

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 schema provides no parameter documentation. The description mentions 'audio' and 'recordings' which hints at the 'files' parameter, but doesn't explain the 'name' or 'description' parameters at all. It doesn't specify audio format requirements, file size limits, or minimum recording quality needed for voice cloning.

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: 'Creates voice clone from audio' specifies both the action (creates) and resource (voice clone from audio). It distinguishes from obvious siblings like 'get_voice' or 'text_to_speech' by focusing on creation from recordings. However, it doesn't explicitly differentiate from 'create_voice_from_preview' which might be a closer alternative.

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

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

The description provides explicit usage context: 'Use when: creating custom voice from recordings' gives clear guidance on when this tool is appropriate. It doesn't specify when NOT to use it or name specific alternatives, but the 'when' clause is helpful and distinguishes it from general voice tools.

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