CaptureOneAI Web Perception

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses that the operation returns structured page content and frames the action as reading, which implies a non-destructive operation, but it does not mention page-loading behavior, rendering side effects, cookie handling, error conditions, or return details.

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 compact and front-loaded: the core result and its contents come first, followed by the intended use case. Both sentences earn their place with no filler or repetition.

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?

There is no output schema, so the description needs to convey more about what the agent can expect back, but it only enumerates content categories without format details. It also lacks disambiguation from find_elements and fails to explain viewport parameters or behavior for dynamic or cookie-banner pages.

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 only 50%, with width and height lacking descriptions. The tool description does not compensate for these undocumented parameters or add meaning beyond what the schema already provides for url and dismissCookies.

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 returns a structured representation of a web page and lists the kinds of content included. It is specific about verb and resource, though it does not explicitly distinguish itself from sibling tools like find_elements beyond the general framing of 'read and understand a page.'

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 gives an explicit use case: let an agent read and understand a page without parsing raw HTML. It does not mention when to prefer alternative sibling tools or when not to use it, but the intended context is reasonably clear.

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

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

With no annotations provided, the description carries the full burden. It discloses the immediate trigger behavior, the contrast with scheduled execution, and the fact that detected changes are returned. It doesn't specify whether the call is blocking or quota-consuming, but core behavior is transparent.

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?

A single sentence front-loads the action, adds the relevant behavioral nuance, and states the return value. Every clause earns its place, with no filler or repetition.

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 one-parameter tool with no output schema, the description adequately covers what the tool does and what it returns. The main gap is not describing the shape/format of the detected changes, but this is a minor omission given the tool's simplicity.

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%, and the monitorId parameter is already well-described in the schema with guidance on where to obtain it. The tool description adds no additional parameter semantics, so the baseline of 3 applies.

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 uses a specific verb and resource: 'Triggers an immediate check for a monitor.' It further distinguishes the tool from passive retrieval siblings by contrasting it with 'waiting for its next scheduled run,' making the operation unique and clear.

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 the use case—trigger a check now rather than waiting for the scheduled run—but it does not explicitly name sibling tools or state when not to use this tool. An agent gets some contextual guidance, but no direct alternative routing.

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 behavioral disclosure burden. It discloses that the monitor runs on a schedule, judges whether changes matter, and provides AI explanations rather than a pixel-diff percentage. It does not describe creation side effects, persistence, or what the response contains, which is why it is not a 5.

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

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

The description is two sentences with the core behavior front-loaded and a useful use-case list in the second sentence. Every phrase earns its place and there is no redundant filler.

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 5 parameters, no annotations, and no output schema, the description is not complete enough. It does not explain what the agent should expect after creation, whether a monitor identifier is returned, how changes are retrieved, or what the optional label parameter is for.

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 60%, with intent, schedule, and callbackUrl already documented in the schema. The description adds no parameter-level meaning and does not compensate for the undocumented url and label parameters; it only repeats the idea of watching a URL on a schedule.

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 says the tool watches a URL on a schedule and reports meaningful changes explained by AI, which gives a specific action and resource. It distinguishes itself from generic pixel-diff monitors, but it does not explicitly differentiate itself from sibling tools such as check_monitor or get_monitor_changes.

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 gives concrete use cases: competitor pricing pages, changelogs, status pages, and pages whose changes should trigger a workflow. It does not mention when not to use the tool or name alternative sibling tools, so it stops short of full exclusion guidance.

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

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool returns only interactive elements and that it is scoped to clickability and location, which is useful. However, it does not disclose behavior such as page navigation, viewport handling, or the dismissCookies side effect implied by the parameter, so some behavioral traits remain hidden.

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

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

The description is two sentences with no filler. It front-loads the return value and scope, then follows with usage guidance. Every clause earns its place and the structure is easy to scan.

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 function with four parameters info, no annotations, and no output schema, the description still misses essential operational context: parameter meanings, the dismissCookies behavior, and any caveats about loading or timing. It provides a solid high-level purpose and return shape but is not fully self-sufficient.

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, but it does not explain any of the four parameters. The url, width, height, and dismissCookies parameters are not mentioned in the description, leaving the agent to infer their meaning from schema-level names and types alone. This is a significant gap at this coverage level.

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 opens with a specific verb ('Returns') and a precise resource ('interactive elements of a page') followed by concrete element types (buttons, links, inputs). It also lists the exact output fields (role, label, CSS selector, pixel coordinates), and by naming capture_context as the alternative it clearly differentiates itself from a sibling tool.

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

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

The description explicitly states when to use the tool: 'before acting on a page' for computer-use/Set-of-Marks grounding. It also gives the exclusion condition: use it 'instead of capture_context when you only need to know what can be clicked and where.' This is direct, actionable routing with no ambiguity.

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, the description carries the burden of signaling behavior. It clearly indicates a read operation ('Returns') and describes the output contents, but it does not disclose ordering, time window, pagination behavior beyond the schema-provided limit, or any caveats about what 'recent' means.

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 focused sentence that front-loads the primary action and resource, then adds the key output detail. Every clause earns its place; there is no filler or redundancy.

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

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

For a simple two-parameter read tool, the description conveys the core purpose and output shape. However, it omits usage context relative to sibling tools and gives no indication of returned format, ordering, or any limits on history depth, so an agent has to infer some operational details.

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 only 50%: limit is documented, but monitorId is not. The description references 'a monitor' generically but does not explain how monitorId maps to a monitor or add any meaning beyond the schema. The description does not compensate for the undocumented required parameter.

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 uses a specific verb ('Returns') and resource ('recent check history for a monitor'), and adds detail about what that history includes ('detected changes with their relevance and explanation'). It clearly distinguishes itself from sibling tools like list_monitors and check_monitor, though it does not explicitly name them.

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 gives no guidance on when to use this tool versus alternatives such as check_monitor or capture_context. It implies a history/audit use case, but there is no explicit context, prerequisite, or exclusion to help an agent choose correctly.

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, the description carries the full burden of behavioral disclosure. It states the operation is a list, implying read-only, but does not explicitly confirm no side effects, authentication requirements, or potential limitations such as pagination or rate limits. The disclosure is minimal and does not go beyond the basic purpose.

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

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

The description is a single, tightly worded sentence that front-loads the action and result. Every word contributes; there is no fluff or redundancy.

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

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

For a simple list tool with no parameters and no output schema, the description specifies the returned fields (IDs, URLs, schedules). It is complete enough for an agent to know what to expect, though it does not mention ordering, limits, or whether all monitors are returned at once. These are minor gaps given the tool's simplicity.

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?

There are zero parameters, so the description does not need to explain parameter semantics. The schema coverage is trivially 100% (empty object). Per rubric, a tool with no parameters merits a baseline score of 4, and the description adequately focuses on the return 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 states a clear verb ('Lists'), a resource ('your active monitors'), and the data returned (IDs, URLs, schedules). It is specific and unambiguous, but it does not distinguish this tool from related siblings like check_monitor or get_monitor_changes, which could also involve monitor data.

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 given on when to use this tool versus alternatives. It does not mention when to prefer list_monitors over check_monitor or get_monitor_changes, nor does it state any conditions or exclusions. The agent is left to infer usage.

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