captcha.cc Human Verification

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

Annotations already indicate this is a non-readOnly, non-destructive operation (readOnlyHint=false, destructiveHint=false). The description adds useful context about creating 'live' challenges for testing purposes, which helps the agent understand the tool's behavioral intent beyond the basic safety profile provided by 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 - just two sentences that directly state the tool's purpose and usage context. Every word earns its place with zero wasted text, and the most important information (what the tool does) comes first.

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

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

For a creation tool with no output schema, the description provides adequate basic information about what the tool does and its testing context. However, it doesn't explain what the created challenge looks like, how it can be used, or what format the response takes, leaving some gaps in 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?

With 100% schema description coverage, the input schema already fully documents both parameters (site_key, hostname). The description doesn't add any additional parameter semantics beyond what's in the schema, so it meets the baseline expectation but doesn't provide extra value.

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

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

The description clearly states the action ('Create a live captcha.cc challenge') and the target resources ('for a site key and allowed hostname'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings (make_siteverify_request, make_widget_snippet), which would be needed for 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 some usage context ('Useful for smoke tests and integration checks'), which implies when this tool might be appropriate. However, it doesn't offer explicit guidance about when to use this versus the sibling tools or any prerequisites for successful invocation.

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?

Annotations indicate this is a read-only, non-destructive, idempotent, and closed-world operation, which the description doesn't contradict. The description adds valuable behavioral context by specifying 'exactly once' (reinforcing idempotency) and 'server-side fetch example' (implying a safe, example-based output), enhancing understanding beyond annotations. However, it doesn't detail potential errors or response formats, leaving some 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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes essential information, earning a high score for conciseness.

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

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

Given the tool's moderate complexity (2 parameters, no output schema), the description is partially complete. It clarifies the tool's purpose and some behavior but lacks parameter explanations and output details. Annotations provide safety and idempotency info, but without an output schema, the description should ideally hint at the return value (e.g., a fetch example format), which it doesn't, leaving room for improvement.

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

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

With 0% schema description coverage and 2 parameters, the description fails to add meaning beyond the input schema. It doesn't explain what 'secret_env_var' or 'proof_token_expression' represent, their roles in the request, or how they interact. The schema provides defaults but no descriptions, so the description should compensate but doesn't, resulting in poor parameter clarity.

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 ('Return a server-side fetch example') and the specific resource ('redeeming a captcha.cc proof token exactly once'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'create_challenge' or 'make_widget_snippet', which might handle different aspects of captcha processing, preventing a perfect score.

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

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

The description provides minimal guidance by implying usage for redeeming a proof token, but it lacks explicit instructions on when to use this tool versus alternatives (e.g., 'create_challenge' for generating tokens or 'make_widget_snippet' for client-side integration). No prerequisites or exclusions are mentioned, leaving the agent with insufficient context for optimal 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?

Annotations already indicate read-only, non-destructive, and idempotent behavior, which the description does not contradict. The description adds valuable context by specifying that the snippet is for 'browser SDK' and involves 'mounting' and 'submitting,' which helps the agent understand the tool's operational scope beyond the 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 a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary details. It is front-loaded with the core action and resource, making it easy for an agent to parse 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 moderate complexity, rich annotations, and full schema coverage, the description is mostly complete. It lacks details on output format or error handling, but annotations cover safety aspects, and the purpose is clear. Without an output schema, some gaps remain, but the description provides sufficient context for 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%, providing detailed parameter information. The description does not add specific semantics beyond the schema, such as explaining parameter interactions or usage examples. However, it implicitly references parameters like 'site_key' and 'submit_path' through context, aligning with the baseline score 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 verb 'return' and the resource 'minimal browser SDK snippet' with specific functionality for 'mounting captcha.cc and submitting the proof token to an application endpoint.' It distinguishes from siblings like 'create_challenge' and 'make_siteverify_request' by focusing on snippet generation rather than challenge creation or verification requests.

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 for generating SDK snippets in browser contexts with captcha.cc integration, but it does not explicitly state when to use this tool versus alternatives like 'create_challenge' or 'make_siteverify_request.' No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool's purpose.

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