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 fetching and returning HTML but omits critical details such as error handling (e.g., for invalid URLs or network failures), authentication needs, rate limits, or whether it performs any processing (like sanitization). This is a significant gap for a tool that interacts with external 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 a single, efficient sentence with zero waste—it directly states the action, resource, and output. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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

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

Given the complexity of fetching external websites (which involves network calls and potential errors), no annotations, and no output schema, the description is incomplete. It should address behavioral aspects like error cases, timeouts, or content limitations to help the agent use it correctly in varied contexts.

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 both parameters (url and headers) adequately. The description adds no additional meaning beyond what the schema provides, such as examples or constraints on URL formats or header usage. Baseline 3 is appropriate when the 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 with a specific verb ('fetch') and resource ('a website'), and indicates the output format ('return the content as HTML'). It distinguishes from sibling tools by specifying HTML output, though it doesn't explicitly contrast with fetch_json, fetch_markdown, and fetch_txt beyond the format difference.

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 fetch_json or fetch_markdown. It lacks context about scenarios where HTML is preferred over other formats, prerequisites, or exclusions, leaving the agent to infer usage based solely on output format.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'fetch' implies a read operation, it doesn't specify authentication requirements, rate limits, error handling, timeout behavior, or what happens with invalid URLs. The description is minimal and lacks crucial operational context for a network-dependent 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 perfectly concise - a single sentence with zero wasted words that communicates the core function. It's front-loaded with the essential information and doesn't include unnecessary elaboration.

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

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

For a network tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (parsed JSON object? raw string? status codes?), error conditions, authentication needs, or performance characteristics. The agent lacks crucial information to use this tool effectively in real scenarios.

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 both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain URL format requirements, header usage patterns, or provide examples. Baseline 3 is appropriate when schema does the documentation work.

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 ('fetch') and resource ('a JSON file from a URL'), making the purpose immediately understandable. It distinguishes from siblings like fetch_html and fetch_markdown by specifying JSON format, but doesn't explicitly contrast with them or mention what makes this tool unique beyond format.

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 like fetch_html or fetch_markdown. The description doesn't mention prerequisites, error conditions, or typical use cases, leaving the agent to infer usage context 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 the full burden. It mentions fetching and converting to Markdown, but lacks details on behavioral traits such as error handling, rate limits, authentication needs, or what happens with invalid URLs. For a tool with no annotations, 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 a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration. Every word earns its place.

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

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

Given no annotations, no output schema, and 2 parameters, the description is incomplete. It lacks details on return values, error cases, and behavioral context. For a tool that performs network operations and format conversion, more information is needed to be fully helpful to an agent.

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

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

Schema description coverage is 100%, so the schema documents both parameters (url and headers). The description adds no additional meaning beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is added.

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 ('fetch') and the resource ('a website'), and specifies the output format ('return the content as Markdown'). It distinguishes from siblings by mentioning Markdown conversion, unlike fetch_html, fetch_json, and fetch_txt which return different formats. However, it doesn't explicitly contrast with siblings beyond the output format.

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 like fetch_html, fetch_json, or fetch_txt. The description implies usage for fetching websites with Markdown output, but lacks explicit context, exclusions, or prerequisites. It doesn't mention scenarios where Markdown conversion is preferred over raw formats.

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

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

No annotations are provided, so the description carries the full burden. It mentions the tool fetches a website and returns plain text, but lacks details on error handling (e.g., what happens if the URL is invalid), performance (e.g., timeouts or rate limits), or side effects (e.g., whether it makes network requests). 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 efficiently conveys the core functionality and output format, making it easy to understand at a glance.

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 (a network fetch tool with no annotations and no output schema), the description is incomplete. It doesn't cover behavioral aspects like error conditions, response format details beyond 'plain text', or usage constraints, leaving gaps that could hinder an AI agent's ability to use the tool correctly.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (url and headers) fully. The description doesn't add any parameter-specific details beyond what the schema provides, such as examples or constraints, so it meets the baseline of 3 for high schema coverage.

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

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

The description clearly states the action ('fetch'), the resource ('a website'), and the specific output format ('plain text (no HTML)'). It distinguishes from sibling tools by specifying the output format, which differentiates it from fetch_html, fetch_json, and fetch_markdown.

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 by specifying the output format, suggesting it should be used when plain text is needed. However, it doesn't explicitly state when to use this tool versus the sibling tools (e.g., 'use fetch_txt when you need text without HTML tags, use fetch_html for raw HTML'), nor does it mention any prerequisites or exclusions.

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