ElevenLabs MCP Server

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

With no annotations provided, the description carries the full burden. It discloses the cost implication (API call that may incur costs) which is valuable behavioral context. However, it doesn't mention other important behavioral aspects like required permissions, rate limits, error conditions, or what happens on success/failure.

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

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

The description is well-structured with purpose statement, allowed types, cost warning, and parameter list. It's appropriately sized for a 5-parameter tool. The cost warning is appropriately emphasized. Some redundancy exists between the purpose statement and parameter list.

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 5-parameter mutation tool with no annotations and no output schema, the description provides good usage guidance and cost warning but lacks details about return values, error handling, and parameter relationships. The parameter semantics are insufficient given the 0% schema coverage.

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 lists all 5 parameters in the Args section, which provides basic parameter names but minimal semantic context. It doesn't explain relationships between parameters (e.g., that url, input_file_path, and text appear to be alternative ways to provide content). Baseline would be lower, but the explicit listing of parameters raises it to 3.

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

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

The description clearly states the action ('Add a knowledge base') and resource ('to ElevenLabs workspace'), and specifies allowed file types (epub, pdf, docx, txt, html). It doesn't explicitly distinguish from sibling tools, but the purpose is specific and unambiguous.

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

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

The description provides explicit usage guidance with a cost warning ('⚠️ COST WARNING: This tool makes an API call to ElevenLabs which may incur costs. Only use when explicitly requested by the user'), which clearly indicates when to use and when to exercise caution.

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 checking subscription status and measuring API usage, but does not disclose critical traits such as whether this requires authentication, rate limits, response format, or potential side effects. For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is appropriately sized with two concise sentences that directly address the tool's purpose and a potential use case. It is front-loaded with the core function, and both sentences earn their place by adding value without redundancy. However, it could be slightly more structured by explicitly stating the tool's output or constraints.

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 lack of annotations and no output schema, the description is incomplete. It does not explain what the subscription status includes (e.g., limits, expiration), how the measurement of API usage is performed, or the return format. For a tool that likely provides critical system information, more context is needed to guide the agent effectively.

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

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

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter semantics, so it meets the baseline for tools with no parameters. No additional parameter information is required or provided.

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

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

The description clearly states the tool's purpose as 'Check the current subscription status,' which is a specific verb ('check') and resource ('subscription status'). It distinguishes itself from sibling tools like list_models or get_agent by focusing on subscription status rather than listing or retrieving other resources. However, it does not explicitly differentiate from all siblings, as some might indirectly relate to subscription usage.

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 minimal guidance with 'Could be used to measure the usage of the API,' which implies a context for usage but does not specify when to use this tool versus alternatives or any prerequisites. No explicit when/when-not instructions or named alternatives are provided, leaving the agent with little direction on optimal 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it mentions the API call to ElevenLabs with cost implications, specifies the default output directory ($HOME/Desktop), and outlines parameter constraints. However, it lacks details on error handling or response format, which would enhance transparency.

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

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

The description is well-structured and appropriately sized: it starts with the core purpose, lists parameters with clear guidelines, and ends with a cost warning. Every sentence adds value, though the parameter explanations could be slightly more concise without losing clarity.

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

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

For a tool with 4 parameters, no annotations, and no output schema, the description is largely complete: it covers purpose, usage, parameters, and behavioral aspects like costs. However, it omits details on the output (e.g., file format or success indicators), which would be helpful given the lack of output schema.

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

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

Given 0% schema description coverage, the description fully compensates by explaining all four parameters: it defines 'prompt' and 'composition_plan' as mutually exclusive inputs, clarifies 'output_directory' as optional with a default, and notes that 'music_length_ms' cannot be used with 'composition_plan'. This adds essential meaning beyond the bare schema.

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

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

The description clearly states the tool's purpose: 'Convert a prompt to music and save the output audio file to a given directory.' It specifies both the action (convert to music) and the resource (audio file), and distinguishes itself from sibling tools like 'create_composition_plan' by focusing on music generation rather than planning.

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

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

The description provides explicit usage guidelines: it explains when to use the tool ('Only use when explicitly requested by the user'), includes a cost warning, and details parameter dependencies (e.g., 'Must provide either prompt or composition_plan' and 'Cannot be used if composition_plan is provided'). This offers clear context for when and how to invoke the tool.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively reveals key behavioral traits: it's a creation/mutation tool (implied by 'Create'), discloses cost implications (API call to ElevenLabs), and provides practical guidance about user requests. It doesn't cover rate limits, authentication needs, or error handling, but offers substantial context beyond basic functionality.

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

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

The description is well-structured with a clear purpose statement upfront, followed by a cost warning, then detailed parameter documentation. While comprehensive, it's appropriately sized for a complex tool with 17 parameters. Every sentence adds value, though the parameter section is lengthy but necessary given the schema coverage gap.

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 (17 parameters, mutation operation, no annotations, no output schema), the description does an excellent job providing context. It covers purpose, usage warnings, and detailed parameter semantics. The main gap is lack of information about return values or what happens after creation, but this is understandable without an output schema.

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

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

With 0% schema description coverage for 17 parameters, the description fully compensates by providing detailed semantic explanations for every parameter. Each parameter gets clear documentation explaining what it represents, including ranges (e.g., 'Range is 0 to 1'), defaults (e.g., 'Defaults to 7 seconds'), and practical meanings (e.g., 'The lower the temperature, the more deterministic the agent's responses will be').

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: 'Create a conversational AI agent with custom configuration.' It specifies the exact action (create) and resource (agent), and distinguishes it from sibling tools like 'get_agent' or 'list_agents' by focusing on creation rather than retrieval.

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

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

The description provides explicit usage guidance with a cost warning: '⚠️ COST WARNING: This tool makes an API call to ElevenLabs which may incur costs. Only use when explicitly requested by the user.' This gives clear context about when to use it (when explicitly requested) and highlights cost implications. However, it doesn't mention alternatives or when not to use it beyond cost concerns.

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 key behavioral traits: it states that usage does not cost credits but is subject to rate limiting based on tier. It also implies this is a creation operation (not read-only) and hints at integration with another tool. It lacks details on error handling or response format.

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 with the core purpose and key behavioral details (credit cost, rate limiting, sibling tool relation). The parameter explanations are clear but slightly verbose; every sentence adds value, though it could be more streamlined.

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 3 parameters with 0% schema coverage, the description does a decent job but has gaps. It covers purpose, usage, and parameters well, but lacks information on return values, error conditions, or more detailed rate limiting specifics, which are important for a creation tool.

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

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

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all three parameters: 'prompt' is explained as the input for creating the plan, 'music_length_ms' includes range constraints and optional behavior, and 'source_composition_plan' clarifies it's optional and used as a source. This goes beyond the bare schema, though it could provide more on data types 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 specific action ('Create a composition plan for music generation') and distinguishes it from the sibling 'compose_music' tool by explaining that composition plans are used when generating music with that tool. It provides a verb+resource combination that is unambiguous.

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

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

The description explicitly states when to use this tool ('can be used when generating music with the compose_music tool'), providing clear context. However, it does not specify when NOT to use it or mention alternatives among siblings, which prevents a perfect score.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates key behavioral traits: it's a write operation ('Add a generated voice'), has external dependencies ('API call to ElevenLabs'), and includes important cost implications. The description doesn't cover rate limits, authentication needs, or what happens on failure, but provides substantial practical guidance beyond basic functionality.

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 structured with two focused sentences: the first states the core functionality, the second provides critical cost warning. Every word earns its place, with no redundancy or unnecessary elaboration. The warning emoji and formatting enhance clarity without adding bulk.

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 3-parameter write tool with no annotations and no output schema, the description provides good behavioral context (cost warning, dependency) but insufficient parameter guidance. It covers the 'why' and 'when' well but leaves gaps in the 'how' regarding parameter usage. Given the complexity of creating a voice resource, more detail on parameter expectations would be beneficial.

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. While it mentions 'voice ID from the `text_to_voice` tool' (mapping to generated_voice_id), it provides no context for voice_name or voice_description parameters. The description adds minimal value beyond what's inferable from parameter titles, leaving two of three parameters without semantic explanation.

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 ('Add a generated voice to the voice library') and resource ('voice library'), with specific reference to the source ('Uses the voice ID from the `text_to_voice` tool'). It distinguishes this tool from siblings like `get_voice` or `search_voice_library` by focusing on creation rather than retrieval. However, it doesn't explicitly contrast with other voice-related tools like `voice_clone` 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?

The description provides clear context for when to use this tool ('Uses the voice ID from the `text_to_voice` tool') and includes an explicit warning about costs ('⚠️ COST WARNING... Only use when explicitly requested by the user'). This gives strong guidance on prerequisites and user confirmation requirements. It doesn't explicitly name alternatives or specify when not to use it beyond the cost warning.

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 it 'gets details' which implies a read-only operation, but doesn't specify if it requires authentication, rate limits, error conditions, or what format/details are returned. This is a significant gap for a tool with no annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and every part earns its place, making it highly concise and well-structured.

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

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' are returned, potential errors, or behavioral traits. For a tool that retrieves specific agent information, 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?

The description adds no parameter semantics beyond what the input schema provides (a single required 'agent_id' parameter). With 0% schema description coverage, the description doesn't compensate by explaining what 'agent_id' represents or its format. However, since there's only one parameter, the baseline is 4, but the lack of any parameter context reduces it to 3.

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

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

The description clearly states the verb ('Get details') and resource ('about a specific conversational AI agent'), making the purpose understandable. It distinguishes from siblings like 'list_agents' by focusing on a single agent rather than listing multiple. However, it doesn't specify what details are included, keeping it from 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 no guidance on when to use this tool versus alternatives like 'list_agents' or 'get_conversation'. It mentions 'specific' agent but doesn't clarify prerequisites or contexts, leaving the agent to infer usage based on the name alone.

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

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 that the tool retrieves data ('Gets') and returns specific outputs, implying a read-only operation without destructive effects. However, it lacks details on permissions, rate limits, or error handling, which are important for a tool with no annotation coverage, making it adequate but with gaps.

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

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

The description is well-structured and front-loaded, starting with the core purpose and usage, followed by parameter details. It uses two sentences efficiently, with no wasted words. However, the parameter explanation could be slightly more integrated into the flow, and the title is null, which slightly impacts structure.

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 (a read operation with one parameter), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers purpose, usage, and parameter semantics adequately, but lacks details on output format, error cases, or behavioral constraints like pagination or authentication needs, which are important for full contextual 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?

The input schema has 0% description coverage, so the description must compensate. It explains the 'conversation_id' parameter by specifying its purpose ('unique identifier of the conversation to retrieve') and how to obtain it ('you can get the ids from the list_conversations tool'), adding meaningful context beyond the schema. This effectively covers the single parameter, though it could include format or validation details for a higher score.

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 it distinguishes what it returns ('conversation details and full transcript'). However, it doesn't explicitly differentiate from potential siblings like 'list_conversations' beyond mentioning that tool as a source for IDs, which keeps it from 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 guidance with 'Use when: analyzing completed agent conversations,' which clearly indicates the context for invoking this tool. It also references 'list_conversations' as a source for IDs, offering some alternative context. However, it doesn't specify when NOT to use it or detail other alternatives, preventing a score of 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 the tool retrieves details, implying a read-only operation, but does not cover aspects like authentication requirements, rate limits, error handling, or response format. This is inadequate for a tool with no annotation coverage, leaving key behavioral traits unspecified.

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

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

The description is a single, efficient sentence that is front-loaded and wastes no words. It directly states the tool's purpose without unnecessary elaboration, making it appropriately sized for its simplicity. Every part of the sentence earns its place by conveying the core function.

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

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

Given the tool's complexity (a read operation with one parameter) and the lack of annotations and output schema, the description is incomplete. It does not specify what details are returned, error conditions, or how it differs from sibling tools. This leaves significant gaps for an AI agent to understand and use the tool effectively.

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

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

The description adds minimal meaning beyond the input schema. It implies the 'voice_id' parameter is used to identify a specific voice, but with 0% schema description coverage, it does not explain what a voice ID is, its format, or how to obtain it. Since there is only one parameter, the baseline is 4, but the lack of compensation for the coverage gap reduces the score.

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's purpose ('Get details of a specific voice'), which is clear but vague. It specifies the verb ('Get') and resource ('voice'), but does not differentiate from siblings like 'search_voice_library' or 'search_voices', nor does it clarify what 'details' entail. This leaves the scope ambiguous compared to 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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention prerequisites, such as needing a specific voice ID, or contrast it with sibling tools like 'search_voices' for broader queries. Usage is implied only by the tool name and description, with no explicit context or exclusions.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully reveals critical behavioral traits: the tool makes external API calls to ElevenLabs, incurs potential costs, has a default output location ($HOME/Desktop), and performs audio isolation. However, it doesn't specify error handling, rate limits, or authentication requirements.

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

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

The description is perfectly front-loaded with the core functionality in the first sentence, followed by important behavioral details. Every sentence earns its place: the first explains the purpose, the second clarifies optional parameter behavior, and the third provides critical usage guidance. 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 tool's complexity (external API calls, file operations) and lack of both annotations and output schema, the description does well by covering purpose, parameters, cost implications, and default behavior. However, it doesn't describe the output format, success/failure responses, or error conditions that would be helpful for a tool with no output schema.

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

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

With 0% schema description coverage, the description must compensate for the lack of parameter documentation. It explains that 'input_file_path' is for the source file and 'output_directory' is optional with a default location, adding meaningful context beyond the bare schema. However, it doesn't specify file format requirements or path validation rules.

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 ('isolate audio from a file') and resource ('save the output audio file to a given directory'), distinguishing it from siblings like 'speech_to_text' or 'play_audio' which handle different audio processing tasks. It precisely defines what the tool does without being vague or tautological.

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

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

The description provides explicit guidance on when to use this tool ('Only use when explicitly requested by the user') and includes a cost warning that helps differentiate it from free alternatives. It clearly defines the trigger condition and potential exclusions based on cost considerations.

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 a read operation ('List'), implying non-destructive behavior, but fails to disclose critical details like pagination, sorting, rate limits, or authentication requirements, which are essential for an agent to use it effectively.

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

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

The description is a single, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core purpose without redundancy or fluff, making it highly concise and well-structured.

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

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

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate. However, without annotations or output details, it lacks completeness for behavioral aspects like response format or error handling, which could aid the agent in proper 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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately avoids discussing parameters, aligning with the schema, which justifies a baseline score of 4 for not adding unnecessary information.

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 ('List') and the resource ('all available conversational AI agents'), making the purpose unambiguous. However, it does not differentiate from sibling tools like 'get_agent' or 'list_models', which reduces specificity.

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

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

The description provides no guidance on when to use this tool versus alternatives such as 'get_agent' for detailed information or 'list_models' for other resources. It lacks context about prerequisites or exclusions, leaving usage unclear.

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 pagination ('cursor for retrieving next page') and default values for page_size and max_length, which adds useful context. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, rate limits, authentication requirements, error conditions, or what the metadata includes. For a list tool with 6 parameters, 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 well-structured and appropriately sized. It front-loads the core purpose, then provides usage guidance, followed by a clear parameter documentation section. Every sentence earns its place, though the 'Args:' section could be slightly more integrated with the main description. The information density is high without being verbose.

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

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

Given the tool's complexity (6 parameters, no annotations, no output schema), the description is moderately complete. It covers parameters well but lacks information about return format details, error handling, authentication needs, and rate limits. The absence of an output schema means the description should ideally explain the structure of returned conversations and metadata, which it doesn't do beyond mentioning they exist.

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%, so the description must compensate - and it does so effectively. It provides clear semantic explanations for all 6 parameters, including optional filtering by agent_id, pagination with cursor, time-based filtering with Unix timestamps, and defaults for page_size and max_length. The description adds substantial value beyond what the bare schema provides, though it doesn't explain parameter interactions or constraints beyond the listed ranges.

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 agent conversations' with a specific verb and resource. It distinguishes from sibling tools like 'get_conversation' (singular) and 'list_agents', but doesn't explicitly contrast with other list tools like 'list_phone_numbers' or 'list_models'. The description is specific about what it returns: 'conversation list with metadata'.

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

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

The description provides some usage guidance with 'Use when: asked about conversation history', which gives context for when this tool is appropriate. However, it doesn't specify when NOT to use it or mention alternatives like 'get_conversation' for single conversations or other filtering approaches. The guidance is implied rather than explicit about 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 the full burden of behavioral disclosure. It states the action ('List all available models') but doesn't describe any behavioral traits such as pagination, rate limits, authentication requirements, or what 'available' means (e.g., filtered by permissions). This is a significant gap for a tool with zero annotation coverage.

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

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

The description is a single, efficient sentence with no wasted words. It is appropriately sized for a simple tool and front-loads the key information, making it easy to parse quickly.

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

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

Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate but lacks context. It doesn't explain what 'models' refers to (e.g., AI models, voice models) or provide any output details, which could be helpful since there's no output schema. This leaves gaps in understanding the tool's scope and results.

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

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

The tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for tools with no parameters, as it doesn't introduce confusion or omissions regarding inputs.

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 ('List') and resource ('all available models'), making the purpose unambiguous. However, it doesn't distinguish this tool from its siblings (like 'list_agents' or 'list_conversations'), which would require specifying what type of models it lists or in what 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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or related tools (e.g., whether this is for AI models, voice models, or another type), leaving the agent to infer usage from the tool name alone.

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

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

No annotations are provided, so the description carries full burden. It states it's a list operation, implying read-only behavior, but doesn't disclose any behavioral traits like pagination, rate limits, authentication needs, or what 'associated with the ElevenLabs account' entails (e.g., owned vs. available). This is a significant gap for a tool with no annotation coverage.

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

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

The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it highly efficient and easy to parse for an 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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on behavior, output format, or integration with siblings. Without annotations or output schema, more detail would improve completeness, but it's not entirely incomplete for a basic list tool.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could have mentioned implicit constraints like authentication. Baseline is 4 for zero parameters, as it doesn't mislead or omit required info.

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 ('List all') and resource ('phone numbers associated with the ElevenLabs account'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'list_agents' or 'list_conversations' beyond the resource type, which prevents 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 no guidance on when to use this tool versus alternatives, such as whether it's for account management, billing, or voice-related tasks. With many sibling tools available, this lack of context leaves the agent without clear usage criteria.

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

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: automatic provider detection (Twilio/SIP trunk), cost implications, and that it returns text content. However, it lacks details on error handling, rate limits, authentication needs, or what specific information is returned, leaving gaps for a mutation tool.

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

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

The description is well-structured and appropriately sized. It front-loads the core purpose, includes a critical warning, and then clearly lists args and returns. Every sentence adds value with no redundancy or fluff.

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

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

Given no annotations and no output schema, the description provides adequate basics (purpose, parameters, cost warning) but is incomplete for a mutation tool. It lacks details on side effects, error cases, return structure, or prerequisites, leaving the agent with significant unknowns about behavioral outcomes.

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 adds meaningful semantics for all 3 parameters: agent_id ('ID of the agent that will handle the call'), agent_phone_number_id ('ID of the phone number to use'), and to_number ('phone number to call in E.164 format'). This goes beyond the bare schema, but doesn't explain parameter interactions or constraints like valid ID formats.

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: 'Make an outbound call using an ElevenLabs agent.' It specifies the verb ('Make'), resource ('outbound call'), and technology context ('ElevenLabs agent'). However, it doesn't explicitly differentiate from sibling tools like 'play_audio' or 'text_to_speech' that might involve audio/call functionality, so it doesn't reach 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 provides clear usage context with the cost warning: 'Only use when explicitly requested by the user.' This gives explicit guidance on when to use the tool. However, it doesn't mention when NOT to use it or name specific alternatives among the siblings, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions supported formats (WAV and MP3) but lacks critical details such as whether playback is synchronous/asynchronous, if it requires specific permissions, potential side effects (e.g., audio output to device), or error handling. This leaves significant gaps for a tool that interacts with system resources.

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 directly address core functionality and format support. Every word earns its place, and it's front-loaded with the primary action. No unnecessary details or redundancy are present.

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 (interacting with audio playback, system resources) and lack of annotations or output schema, the description is insufficient. It misses behavioral traits, error conditions, output expectations, and deeper usage context. For a tool with potential side effects and no structured safety hints, this leaves the agent poorly informed.

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%, with one parameter ('input_file_path') undocumented in the schema. The description adds minimal value by implying the parameter should point to an audio file, but doesn't specify path format, relative/absolute requirements, or file accessibility constraints. This partially compensates but remains inadequate for full parameter 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 action ('Play') and resource ('an audio file'), specifying the tool's purpose. It distinguishes from siblings like 'text_to_speech' or 'compose_music' by focusing on playback of existing files rather than generation or conversion. However, it doesn't explicitly differentiate from all audio-related siblings (e.g., 'isolate_audio'), keeping it from 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 no guidance on when to use this tool versus alternatives like 'text_to_speech' or 'speech_to_speech', nor does it mention prerequisites or exclusions. It only states what the tool does, leaving the agent to infer usage context from the tool name and sibling list.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions pagination behavior and search filtering, which is helpful, but doesn't cover important aspects like rate limits, authentication requirements, error conditions, or what specific information is returned in the 'TextContent'. For a search tool with 3 parameters, 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 structured and concise. It begins with a clear purpose statement, then provides organized parameter documentation with helpful constraints, and concludes with return value information. Every sentence adds value with zero 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 no annotations and no output schema, the description does reasonably well covering the basics: purpose, parameters, and return type. However, for a search tool with pagination and filtering capabilities, it should ideally mention more about the search behavior (e.g., case sensitivity, partial matching), result format details, or error handling to be fully complete.

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

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

With 0% schema description coverage, the description must fully compensate, and it does so effectively. It clearly explains all three parameters: 'page' (0-indexed page number), 'page_size' (range 1-100 voices per page), and 'search' (search term for filtering). This provides meaningful context beyond the basic schema types and defaults.

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 ('Search for a voice') and resource ('across the entire ElevenLabs voice library'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_voices' or 'get_voice', which appears to be a similar voice-related tool, leaving some ambiguity about when to use each.

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

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'search_voices' and 'get_voice' present, there's no indication of how this tool differs in scope, functionality, or appropriate use cases, leaving the agent to guess about 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 searching across multiple fields and returns a list, but lacks behavioral details like pagination, rate limits, authentication needs, or error handling. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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

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

The description is well-structured with clear sections (purpose, args, returns) and uses 4 sentences efficiently. Each sentence adds value: the first states purpose, the second clarifies scope, and the args/returns sections provide necessary details without redundancy. It could be slightly more concise by integrating the search scope into the purpose sentence.

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 and no annotations or output schema, the description does an adequate job explaining parameters and return value. However, it lacks context on behavioral aspects like pagination, error cases, or performance limits, which are important for a search tool. It's minimally viable but has clear 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 explains all three parameters: 'search' filters across multiple fields, 'sort' specifies sorting field with a note about 'created_at_unix' availability, and 'sort_direction' defines order. This adds meaningful context beyond the bare schema, though it doesn't detail default behaviors or constraints fully.

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

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

The description clearly states the tool searches for existing voices in the user's ElevenLabs voice library, specifying the verb 'search' and resource 'voices'. It distinguishes from siblings like 'get_voice' (singular retrieval) and 'voice_clone' (creation), though not explicitly named. However, it doesn't fully differentiate from 'search_voice_library', which appears similar.

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

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

The description implies usage when searching across multiple voice attributes (name, description, labels, category), suggesting it's for broader queries. It doesn't explicitly state when to use this vs. alternatives like 'get_voice' (for specific voice retrieval) or 'search_voice_library' (unclear distinction), and provides no exclusions or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively warns about API costs and external dependency (ElevenLabs), which are crucial behavioral traits. However, it lacks details on error handling, rate limits, supported audio formats, or output behavior (e.g., file generation specifics). The description adds value but doesn't fully compensate for the absence of annotations.

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

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

The description is extremely concise and well-structured: a clear purpose statement followed by a critical warning. Both sentences earn their place by providing essential information without redundancy. The warning is appropriately front-loaded with an emoji for emphasis, 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 the tool's complexity (audio transformation with external API), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It covers the core purpose and cost warning well but misses details on parameters, output format, error cases, and technical constraints. The cost warning is valuable but doesn't fully address the gaps for a tool with 3 parameters and no structured 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 for parameter documentation. It mentions 'using provided audio files' which hints at 'input_file_path', but doesn't explain what 'voice_name' or 'output_directory' do, their formats, or constraints. With 3 parameters and no schema descriptions, this leaves significant gaps in understanding parameter roles and usage.

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: 'Transform audio from one voice to another using provided audio files.' This specifies the verb ('transform'), resource ('audio'), and transformation type ('from one voice to another'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'voice_clone' or 'text_to_voice', which prevents 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 guidance with the cost warning: 'Only use when explicitly requested by the user.' This clearly defines when to use the tool (when user explicitly requests voice transformation) and when not to use it (for other audio processing needs), addressing the critical cost implication. No alternatives are named, but the constraint is sufficiently directive.

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

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

With no annotations provided, the description carries full burden and does well: it discloses cost implications (API call to ElevenLabs with potential costs), describes two output behaviors (save to file or return directly), mentions automatic language detection fallback, and explains default behavior for output_directory. It doesn't cover rate limits, error conditions, or authentication needs, but provides substantial behavioral context beyond basic functionality.

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

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

The description is well-structured with purpose statement, cost warning, parameter explanations, and return behavior. Each sentence adds value, though the parameter explanations could be slightly more concise. The warning is appropriately highlighted, and information is logically organized from general to specific.

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 6-parameter tool with no annotations and no output schema, the description provides comprehensive coverage: clear purpose, cost warning, detailed parameter semantics, and return behavior explanation. The main gap is lack of output format details (what TextContent contains, structure of diarized output), but given the tool's moderate complexity, this is mostly 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 description provides excellent parameter semantics despite 0% schema description coverage. It explains all 6 parameters clearly: file_path purpose, language_code format (ISO 639-3) and auto-detection behavior, diarize functionality (speaker annotation), save_transcript_to_file and return_transcript_to_client_directly purposes and interaction, and output_directory default value. This fully compensates for the schema coverage 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 tool's purpose: 'Transcribe speech from an audio file' with specific actions (save to file or return to client). It distinguishes from siblings like 'text_to_speech' or 'voice_clone' by focusing on transcription from audio. However, it doesn't explicitly differentiate from 'speech_to_speech' which might involve similar input processing.

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 cost warning ('⚠️ COST WARNING: This tool makes an API call to ElevenLabs which may incur costs. Only use when explicitly requested by the user'), which provides some usage context. However, it lacks explicit guidance on when to use this tool versus alternatives like 'speech_to_speech' or 'isolate_audio', and doesn't mention prerequisites or typical scenarios for transcription.

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 and does so effectively. It reveals critical behavioral traits: external API dependency (ElevenLabs), cost implications, file system operations (saving to directory), default behaviors (directory defaults to $HOME/Desktop), and subscription tier requirements for certain formats. The only minor gap is lack of information about error handling or rate limits.

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

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

The description is well-structured with purpose statement first, followed by warnings, then detailed parameter documentation. While comprehensive, some sentences could be more concise (e.g., the output_format explanation is quite lengthy). However, all content serves clear purposes: operational guidance, cost warnings, and parameter clarification.

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 5-parameter tool with no annotations and no output schema, the description provides excellent coverage of purpose, usage constraints, and parameter semantics. The main gap is lack of information about return values or output file naming conventions. However, given the complexity of the tool and absence of structured metadata, the description does remarkably well at providing operational context.

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

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

Despite 0% schema description coverage, the description provides comprehensive parameter semantics that fully compensate. Each parameter (text, duration_seconds, output_directory, loop, output_format) receives clear explanations including purpose, constraints, defaults, and format specifications. The output_format parameter gets particularly detailed treatment with format explanation, tier requirements, and complete enumeration of valid values.

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

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

The description clearly states the tool's purpose with specific verbs ('convert text description to sound effect' and 'save the output audio file') and identifies the resource ('sound effect'). It distinguishes itself from sibling tools like 'text_to_speech' and 'compose_music' by focusing specifically on sound effects generation rather than speech synthesis or music composition.

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 the ⚠️ COST WARNING that clearly states when to use ('only use when explicitly requested by the user') and when to avoid (due to API costs). It also specifies duration constraints ('must be between 0.5 and 5 seconds') and tier requirements for certain output formats, giving clear operational boundaries.

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

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

With no annotations provided, the description carries full burden and does an excellent job disclosing behavioral traits. It clearly explains the cost implications (API call to ElevenLabs), file saving behavior (default directory, optional parameter), voice selection logic (mutual exclusivity rules), and various audio quality parameters with their effects on performance and quality.

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?

While comprehensive, the description is quite lengthy with extensive parameter documentation that might be better placed in a separate reference. The core purpose and critical warnings are front-loaded appropriately, but the detailed parameter explanations (while valuable) make it less concise than ideal for quick scanning.

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 (12 parameters, no annotations, no output schema), the description provides complete context. It covers purpose, usage constraints, behavioral details, parameter semantics, and even specifies the return format ('path to the output file and name of the voice used'), making it fully self-contained despite the lack of structured metadata.

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 12 parameters, the description provides exceptional value by explaining every parameter's purpose, constraints, defaults, and practical implications. It goes far beyond what the bare schema provides, offering detailed explanations for model_id options, range constraints, computational trade-offs, and format requirements with tier restrictions.

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 ('convert text to speech', 'save the output audio file') and resources (text, voice, directory). It distinguishes itself from siblings like 'text_to_voice' or 'text_to_sound_effects' by focusing on speech synthesis with ElevenLabs API and file saving 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 clear usage context with the cost warning and explicit instruction to 'only use when explicitly requested by the user.' However, it doesn't explicitly compare with alternatives like 'text_to_voice' or 'speech_to_speech' to guide when to choose this specific tool over 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 and excels. It discloses key behavioral traits: creates three previews with variations, saves files with specific naming pattern (voice_design_(generated_voice_id)_(timestamp).mp3), auto-generates text if none provided, and includes a critical cost warning about ElevenLabs API calls. This covers mutation effects, output format, and operational constraints.

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

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

The description is well-structured and concise. It front-loads core functionality, follows with file naming details and example, and ends with critical warnings. Every sentence adds value: no repetition or fluff, making it efficient for agent comprehension.

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

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

Given no annotations, 0% schema coverage, and no output schema, the description does an excellent job covering purpose, behavior, and usage. It explains what the tool does, how it behaves, and critical costs. However, it doesn't detail the return value or error handling, which could be useful for a mutation tool with external API calls.

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 adds meaningful context for parameters: 'text' is clarified with auto-generation behavior, 'output_directory' is explained for saving files, and 'voice_description' is implied as required for voice generation. However, it doesn't detail format constraints or examples for parameters like voice_description, leaving some 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: 'Create voice previews from a text prompt. Creates three previews with slight variations. Saves the previews to a given directory.' It specifies the action (create), resource (voice previews), and key details (three variations, saving behavior), distinguishing it from sibling tools like text_to_speech or voice_clone.

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: 'If no text is provided, the tool will auto-generate text' clarifies optional behavior, and '⚠️ COST WARNING: This tool makes an API call to ElevenLabs which may incur costs. Only use when explicitly requested by the user' gives clear when-to-use and cost considerations, distinguishing it from free alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully adds context about external API costs ('makes an API call to ElevenLabs which may incur costs'), which is valuable behavioral information. However, it doesn't describe other important traits like whether this is a read/write operation, what happens to the created clone, error conditions, or response format.

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. The first sentence states the core purpose, and the second provides critical usage guidance with a warning symbol for emphasis. Every sentence earns its place, and the information is front-loaded appropriately.

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 voice cloning (a write operation with cost implications), no annotations, no output schema, and 0% schema description coverage, the description is moderately complete. It covers the core purpose and critical cost warning but lacks details about parameters, behavioral outcomes, error handling, and what the tool returns. For a tool with significant implications, more completeness would be expected.

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 'using provided audio files' which maps to the 'files' parameter, but doesn't explain the 'name' or 'description' parameters at all. With 3 parameters and no schema descriptions, the description adds minimal semantic value beyond what's obvious from parameter names.

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: 'Create an instant voice clone of a voice using provided audio files.' It specifies the verb ('create'), resource ('voice clone'), and method ('using provided audio files'). However, it doesn't explicitly differentiate from sibling tools like 'create_voice_from_preview' or 'text_to_voice', which might have overlapping functionality in voice creation 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 guidance with the cost warning: 'Only use when explicitly requested by the user.' This gives clear context for when to use the tool. However, it doesn't mention when NOT to use it (e.g., for simple text-to-speech vs. cloning) 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.