AgentScrape

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

Annotations provide only a title, so the description carries the burden of behavioral disclosure. It does mention that sessions persist cookies and localStorage, which is a key behavioral trait, but it does not disclose session expiration, resource implications, whether it returns a session handle, or any side effects beyond persistence.

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 that front-loads the action and key characteristics. It is concise, with no redundant or filler content.

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?

The tool has a simple interface with one optional parameter and no output schema. The description covers purpose and persistence behavior, but it omits how the created session is referenced in subsequent calls, which is crucial for using other tools with this session. The TTL parameter is only mentioned in the schema, not in the description.

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 single parameter 'ttl_seconds' is fully documented in the JSON schema with a description, default, and max value, so schema coverage is 100%. The description adds no additional meaning about the parameter; it only implies the session is stateful, which is not directly tied to the TTL.

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 'Create' and specifies the resource as a 'stateful browser session' that persists cookies and localStorage. This distinguishes it from sibling tools like scrape_webpage or extract_metadata, which are read-only or extraction-oriented.

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 scenarios requiring state persistence across multiple calls, but it does not explicitly state when not to use it or reference alternative tools. The context of 'across multiple scrape/workflow calls' provides some guidance, but no clear exclusions or prerequisites are given.

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?

There are no behavioral annotations beyond the title. The description does not disclose that the tool likely makes an HTTP request, how it handles errors, whether authentication is required, or what the response structure looks like. Since no annotations cover safety or side effects, the description carries the full burden and does not sufficiently address it.

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, front-loaded sentence with no filler. Every word adds value, and it efficiently lists the metadata types without 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?

The tool is simple with one parameter and no output schema, so the description gives a reasonable overview of what is extracted. However, it does not explain the return format, potential limitations (e.g., dynamic content), or whether the tool fetches the page itself. These gaps reduce its completeness.

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%, and the description does not mention the 'url' parameter at all. While the parameter name is self-explanatory, the description offers no additional semantics such as expected URL format, requirements, or constraints. The description fails to compensate for the lack of schema documentation.

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 extracts page metadata and enumerates specific items (title, description, Open Graph, Twitter cards, JSON-LD, canonical URL, and all meta tags). This distinguishes it from siblings like scrape_webpage and extract_structured_data by focusing on metadata rather than content or structured 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?

Usage is implied through the verb 'extract' and the resource 'page metadata', but there is no explicit guidance on when to choose this tool over siblings like extract_structured_data or scrape_webpage. No direct alternatives or exclusions are mentioned.

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 are minimal (only a title), so the description carries the full burden of behavioral disclosure. It mentions AI-powered and JSON return, but does not disclose potential non-determinism, error handling, rate limits, or side effects. This is insufficient for a tool with no other behavioral hints.

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 unnecessary words. It front-loads the core action and return type, and every word earns its place. This is an appropriately sized description.

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?

With no output schema, the description should explain return values thoroughly, but it only says 'returns JSON matching your prompt or schema.' It omits critical details about wait_ms, wait_for, error scenarios, and how to construct the schema, making it incomplete for a 5-parameter 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 60% (url, prompt, schema), but wait_ms and wait_for are undocumented. The description does not compensate for these gaps; it only references 'prompt or schema' and adds no meaning beyond the schema. Since coverage is not high, the description should add value, but it doesn't.

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 a specific action—AI-powered extraction of structured data from any webpage using natural language—and distinguishes it from siblings like scrape_webpage and extract_metadata. The verb 'extract' plus the resource 'structured data' makes the purpose 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 implies usage for structured data extraction but does not explicitly state when to use this tool over alternatives such as scrape_webpage or extract_metadata. No exclusions or alternative recommendations are provided, so it only reaches the 'implied usage' level.

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 provide no readOnly/destructive hints, so the description carries full responsibility for behavioral disclosure. It reveals 'atomically' and a 20-step limit, but leaves out critical details such as what happens on step failure (rollback? partial execution?), session handling, authentication needs, or side effects of actions like click/type. The word 'atomically' is ambiguous and not elaborated, and the description lacks essential behavioral context for a mutation-capable automation 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 two concise sentences, front-loaded with the core function and action list, with no fluff. Every word adds either specificity (multi-step, atomic) or useful limits (up to 20 steps), making it efficient and easy to parse.

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, low schema coverage, no output schema, and minimal annotations, the description is severely inadequate. It fails to explain the role of each parameter, execution context, error behavior, return value, or how steps interact, leaving the agent without essential information to correctly invoke the tool in complex 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 only 25% (solely 'steps'), and the description does not compensate. It mentions the action types, but these duplicate the action enum from the schema and do not explain the meaning of `viewport`, `session_id`, or `persist_session`. The description adds no value beyond the schema for parameter semantics, leaving key parameters underdocumented.

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 'Execute' and clearly identifies the resource as 'multi-step browser workflow' with a list of actions (navigate, click, type, wait, scroll, screenshot, extract, evaluate). It distinguishes from sibling tools like scrape_webpage and screenshot_webpage by emphasizing multi-step and atomic execution, making the purpose unmistakable.

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 multi-step workflows (e.g., 'multi-step browser workflow', 'Up to 20 steps') but does not explicitly state when to use this tool versus alternatives like scrape_webpage for single-page extraction or screenshot_webpage for single screenshots. No alternatives are named, so guidance is merely implied, not explicit.

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 only include a title, providing no read-only or destructive hints, so the description must bear the full burden. It discloses the pay-per-call cost model, which is a useful behavioral trait, but it omits critical behaviors like whether JavaScript is executed, how errors are handled, rate limits, or handling of dynamic content. For a scraping tool, these gaps are significant, making the description only minimally 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?

The description is two sentences long and front-loaded with the intended purpose. The first sentence states the core action and output formats; the second provides the pay-per-call context. Every word earns its place, with 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?

With no output schema and only a minimal annotation, the description carries the burden of explaining what the agent gets back. It only says 'return content' without detailing the JSON structure, error behavior, or limitations. While the parameter schema is rich, the description lacks essential context for a tool with 5 parameters, making it incomplete for reliable 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 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds no extra meaning beyond the schema: it only repeats the output formats already listed in the format parameter and provides no context for wait_ms, viewport, or wait_for. Thus, the parameter semantics add no value beyond the structured definition.

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 function: scraping any webpage and returning content in markdown, html, text, or json. It uses a specific verb (scrape) and resource (webpage), making the purpose unambiguous. However, it does not explicitly distinguish itself from sibling tools like extract_structured_data or extract_metadata, so it stops short of a 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 no explicit guidance on when to use this tool versus alternatives. It mentions 'pay-per-call web scraping for AI agents' which hints at cost-sensitive usage, but it doesn't state any context or exclusions relative to sibling tools such as screenshot_webpage or extract_structured_data. This leaves the agent without clear selection 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?

Description adds viewport modes and full-page behavior, but with no annotations covering safety, it does not disclose whether it renders JavaScript, waits for network idle, or any rate limits. Adequate but minimal.

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?

Two compact sentences, front-loaded with the core action, no unnecessary detail.

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?

Core function and primary options are covered, but wait_ms and wait_for semantics are missing, and no alternatives are cited. Since output schema is absent, description does not clarify return format, though 'PNG' implies it.

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?

Description explicitly maps viewport enum values to device types and explains full_page mode, compensating for the schema's lack of descriptions. However, wait_ms and wait_for are left undocumented, so not all parameters are clarified.

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?

Clear verb 'Capture' with resource 'webpage', specifies output format PNG. Distinguishes from sibling tools like scrape_webpage and extract_structured_data which imply text/dataset extraction.

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 on when to choose this tool over siblings like scrape_webpage or extract_metadata. Does not mention contexts such as needing visual verification or excluding dynamic pages.

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