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 crawler 'expands like a graph' and can be controlled for depth/width/focus, but fails to disclose critical behaviors: whether it respects robots.txt, rate limits, authentication needs, error handling, output format details, or what 'process' means for links. This leaves significant gaps for a mutation tool (crawling implies data retrieval).

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 (three sentences) and front-loaded with the core purpose. Each sentence adds value: the first defines the tool, the second explains expansion behavior, and the third outlines controllability. There's no redundant or wasted language, though it could be slightly more structured for clarity.

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

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

Given the tool's complexity (11 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits (e.g., rate limits, permissions), output format, error handling, and explicit differentiation from siblings. While the schema covers parameters well, the description doesn't compensate for missing annotation and output information, making it inadequate for safe and 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 100%, so the schema fully documents all 11 parameters. The description adds minimal value beyond the schema, only vaguely referencing 'control how deep and wide it goes' and 'guide it to focus on specific sections,' which loosely maps to max_depth, max_breadth, and instructions/select_paths. No additional syntax, format, or interaction details are provided beyond what's 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 tool's purpose as 'initiates a structured web crawl starting from a specified base URL' with specific verbs ('crawl', 'expands', 'following internal links'). It distinguishes from sibling tools by focusing on crawling rather than extraction, mapping, or searching. However, it doesn't explicitly contrast with each sibling's specific function.

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 context by mentioning 'guide it to focus on specific sections of the site' and controlling depth/breadth, suggesting when to use this tool for structured exploration. However, it lacks explicit guidance on when to choose this over alternatives like tavily-search or tavily-extract, nor does it mention prerequisites 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 full burden for behavioral disclosure. It mentions the tool 'retrieves and processes raw content' but doesn't disclose critical behavioral traits: whether it requires authentication, rate limits, error handling, pagination, or what the response structure looks like. The description adds minimal context beyond the basic operation.

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

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

The description is appropriately sized with two concise sentences. The first sentence states the core functionality, and the second provides use cases. There's no wasted text, though it could be slightly more front-loaded with sibling differentiation.

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

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

Given 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, or behavioral constraints. For a web extraction tool with multiple configuration options and no structured output documentation, the description should provide more context about the extraction results and limitations.

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

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

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions general purpose but no parameter semantics. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'retrieves and processes raw content from specified URLs' with specific verbs and resource. It mentions use cases like 'data collection, content analysis, and research tasks' which helps understanding. However, it doesn't explicitly differentiate from sibling tools like tavily-crawl or tavily-search, which likely have overlapping web-related 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 no guidance on when to use this tool versus its siblings (tavily-crawl, tavily-map, tavily-search). It mentions the tool is 'ideal for data collection, content analysis, and research tasks' but doesn't specify contexts where alternatives might be better. There's no explicit when/when-not guidance or named 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool 'creates a structured map' and is 'powerful,' it does not disclose critical behavioral traits such as rate limits, authentication needs, potential for destructive actions, or what the output looks like. This leaves significant gaps for an AI agent to understand how the tool behaves in practice.

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 first sentence clearly stating the core functionality. Every sentence adds value by elaborating on use cases without redundancy. It efficiently communicates the tool's purpose and applications in just two sentences.

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

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

Given the tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It adequately explains the purpose and usage context but lacks details on behavioral aspects and output format, which are crucial for an AI agent to invoke the tool correctly. The description does not fully compensate for the absence of annotations and 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?

The input schema has 100% description coverage, so the schema already documents all 8 parameters thoroughly. The description does not add any specific parameter semantics beyond what the schema provides, such as explaining how parameters interact or providing usage examples. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose with specific verbs ('creates a structured map of website URLs') and resources ('website URLs'), and distinguishes it from sibling tools by focusing on mapping site structure rather than crawling, extracting, or searching. It explicitly mentions use cases like site audits and understanding website architecture.

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 ('Perfect for site audits, content discovery, and understanding website architecture'), but it does not explicitly mention when not to use it or name alternatives like 'tavily-crawl' for different purposes. The guidance is helpful but lacks explicit exclusions or sibling tool 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real-time results' and 'customizable parameters,' but doesn't address critical behavioral aspects like rate limits, authentication requirements, error conditions, pagination, or what the response structure looks like. For a search tool with 15 parameters, this leaves significant gaps in understanding how the tool behaves 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 concise with three well-structured sentences. The first sentence establishes core functionality, the second explains key features, and the third provides usage context. Every sentence earns its place with no wasted words, and information is appropriately front-loaded.

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

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

Given the tool's complexity (15 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't explain the response format, error handling, rate limits, or how results are structured. For a search tool that likely returns rich data, the description should provide more context about what the agent can expect from the tool's behavior and outputs.

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 mentions 'customizable parameters for result count, content type, and domain filtering,' which adds some context beyond the schema. However, with 100% schema description coverage, the schema already comprehensively documents all 15 parameters. The description provides high-level grouping but doesn't add meaningful semantic value beyond what's in the detailed schema descriptions.

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

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

The description clearly states the tool's purpose as a 'web search tool' that 'provides comprehensive, real-time results' using Tavily's AI engine. It specifies the verb ('search') and resource ('web content'), but doesn't explicitly differentiate from sibling tools like tavily-crawl or tavily-extract, which likely have different functions (crawling vs. extracting vs. searching).

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 implied usage guidance by stating it's 'ideal for gathering current information, news, and detailed web content analysis.' However, it doesn't explicitly state when to use this tool versus its siblings (tavily-crawl, tavily-extract, tavily-map) or when not to use it. The guidance is helpful but not comprehensive for sibling differentiation.

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