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 that the tool returns a job ID for status checking, which implies an asynchronous operation. However, it doesn't cover critical aspects like rate limits, authentication requirements, error handling, or what happens if URLs fail. For a batch scraping tool with no annotation coverage, this is a significant gap.

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—two sentences that directly state the tool's function and output. It's front-loaded with the core purpose, and every sentence earns its place by adding value (batch mode and job ID return). No wasted words 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?

Given the complexity (batch scraping with multiple options and asynchronous behavior), no annotations, and no output schema, the description is incomplete. It doesn't explain the asynchronous nature in detail, error handling, or how to use the job ID with sibling tools like 'firecrawl_check_batch_status.' For a tool with rich input options and no structured safety hints, more context is needed.

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 50% (only the 'urls' parameter has a description). The description adds no parameter-specific information beyond what's implied by 'batch mode' and 'job ID.' It doesn't explain the 'options' object or its sub-properties (e.g., 'formats,' 'onlyMainContent'). With low schema coverage, the description fails to compensate adequately, but it doesn't contradict 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: 'Scrape multiple URLs in batch mode.' It specifies the verb (scrape), resource (URLs), and operational mode (batch). However, it doesn't explicitly differentiate from sibling tools like 'firecrawl_scrape' or 'firecrawl_crawl,' which likely handle single URLs or different crawling methods.

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: it mentions batch mode and that it returns a job ID for status checking. However, it doesn't explain when to use this tool versus alternatives like 'firecrawl_scrape' (likely for single URLs) or 'firecrawl_crawl' (possibly for different crawling approaches). No explicit when/when-not scenarios or prerequisites are included.

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 but offers minimal behavioral insight. It states the tool checks status but doesn't describe what status values mean (e.g., pending, completed, failed), whether it's idempotent, or any rate limits. For a status-checking tool, this leaves significant gaps in understanding its behavior.

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 purpose without unnecessary words. It's appropriately sized for a simple status-checking tool and front-loads the key information.

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 no annotations and no output schema, the description is insufficiently complete. It doesn't explain what status information is returned (e.g., progress percentage, error messages), how to interpret results, or dependencies on other tools. This leaves the agent with critical gaps in understanding the tool's full context.

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%, with the single parameter 'id' documented as 'Batch job ID to check'. The description adds no additional meaning beyond this, such as format examples or where to obtain the ID. Given 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 action ('check the status') and resource ('batch scraping job'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'firecrawl_check_crawl_status' (which likely checks individual crawl jobs), but the specificity to 'batch' jobs provides some implicit differentiation.

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. It doesn't mention prerequisites (e.g., needing a batch job ID from 'firecrawl_batch_scrape'), exclusions, or comparisons with similar tools like 'firecrawl_check_crawl_status' for non-batch jobs.

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. While 'check' implies a read-only operation, the description doesn't specify what status information is returned, whether there are rate limits, authentication requirements, or what happens with invalid job IDs. This leaves significant gaps for an agent trying to understand the tool's behavior.

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 gets straight to the point with zero wasted words. It's appropriately sized for a simple status-checking tool and front-loads the essential information.

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 no annotations and no output schema, the description is insufficiently complete. While the purpose is clear, there's no information about what status information is returned, possible status values, error conditions, or how this differs from the batch status checking sibling tool. The agent would need to guess about 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 schema has 100% description coverage, with the single parameter 'id' clearly documented as 'Crawl job ID to check'. The description doesn't add any additional semantic context beyond what the schema already provides, but since schema coverage is complete, 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 verb ('check') and resource ('status of a crawl job'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'firecrawl_check_batch_status', which appears to serve a similar status-checking function for batch operations rather than individual crawls.

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. With sibling tools like 'firecrawl_check_batch_status' and 'firecrawl_crawl' available, there's no indication whether this is for checking ongoing crawls, completed crawls, or how it differs from batch status checking.

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 the tool is 'asynchronous' and supports 'webhook notifications,' which are useful behavioral traits. However, it doesn't address critical aspects like rate limits, authentication requirements, error handling, or what happens when the crawl completes (e.g., where results are stored). For a complex tool with 12 parameters, this leaves significant 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, well-structured sentence that efficiently conveys the core purpose and key features without unnecessary words. It's front-loaded with the main action and resource, 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 tool's complexity (12 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and some capabilities but lacks details on behavioral traits, return values, or error handling. The high schema coverage helps, but for an asynchronous operation with many options, more context would be beneficial.

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 high at 92%, so the baseline is 3 even without parameter details in the description. The description adds minimal value beyond the schema by mentioning 'depth control, path filtering, and webhook notifications,' which loosely correspond to parameters like maxDepth, includePaths/excludePaths, and webhook, but doesn't provide additional semantic context or usage examples.

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 specific action ('start an asynchronous crawl'), the resource ('multiple pages from a starting URL'), and key capabilities ('depth control, path filtering, and webhook notifications'). It distinguishes itself from siblings like firecrawl_scrape or firecrawl_extract by focusing on multi-page crawling rather than single-page operations.

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 through 'asynchronous crawl of multiple pages' and mentions capabilities like depth control, which suggests when to use this tool. However, it doesn't explicitly state when to choose this over alternatives like firecrawl_map or firecrawl_batch_scrape, 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'deep research' and methods like 'web crawling, search, and AI analysis', but fails to detail critical behaviors such as rate limits, authentication needs, potential costs, or what 'deep' entails (e.g., iterative analysis, multi-source synthesis). This leaves significant gaps for a tool with complex operations.

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 front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately sized and well-structured.

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 implied by 'deep research' and the lack of annotations and output schema, the description is insufficient. It does not explain what the tool returns (e.g., a report, summarized findings, raw data), how results are formatted, or any error conditions, leaving the agent with incomplete 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%, meaning the input schema already documents all parameters well. The description does not add any additional meaning or context beyond what the schema provides (e.g., it doesn't explain how parameters like 'maxDepth' or 'timeLimit' affect the research process). Thus, it meets the baseline score 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 tool's purpose with specific verbs ('conduct deep research') and resources ('on a query'), and mentions the methods used ('web crawling, search, and AI analysis'). However, it does not explicitly differentiate from sibling tools like 'firecrawl_search' or 'firecrawl_crawl', which may have overlapping functionality, preventing a score of 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 guidance on when to use this tool versus alternatives, such as the sibling tools listed. It lacks explicit instructions on context, prerequisites, or exclusions, leaving the agent to infer usage based on the generic description 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 of behavioral disclosure. It states the tool uses LLM for extraction and supports different deployment modes, but lacks critical details: it doesn't specify whether this is a read-only or mutating operation, potential rate limits, authentication needs, error handling, or what the output looks like (since no output schema exists). For a tool with 7 parameters and no annotations, this is a significant gap.

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 concise with two sentences that directly state the tool's core functionality and a key feature. It's front-loaded with the main purpose, and every sentence adds value (the second sentence clarifies deployment options). There's no wasted verbiage 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?

Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It lacks output format details, error conditions, prerequisites (e.g., authentication), and behavioral constraints. For a tool that likely involves network calls and LLM processing, this leaves significant gaps for an agent to use it 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 all 7 parameters thoroughly. The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, default behaviors, or examples. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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: 'Extract structured information from web pages using LLM.' It specifies the verb ('extract'), resource ('structured information from web pages'), and method ('using LLM'). However, it doesn't explicitly differentiate from sibling tools like 'firecrawl_scrape' or 'firecrawl_deep_research,' which likely have overlapping 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 alternatives. It mentions support for 'cloud AI and self-hosted LLM extraction,' but this is a feature detail, not usage context. There are no explicit when/when-not instructions or references to sibling tools, leaving the agent to infer usage scenarios.

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 but provides minimal information. It mentions generating a 'standardized' file but doesn't describe what the generation process entails (e.g., does it crawl the site? analyze content? follow links?), what permissions might be needed, rate limits, or what the output looks like beyond the file name. For a tool with no annotation coverage, this leaves significant behavioral questions unanswered.

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 clearly states the tool's purpose without unnecessary words. It's appropriately sized for a straightforward tool and front-loads the core functionality ('Generate standardized LLMs.txt file') immediately.

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 3 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate basic purpose but lacks important context. It doesn't explain what an LLMs.txt file contains, how it differs from robots.txt, what the generation process involves, or what the agent should expect as a result. The description is minimally complete but leaves significant gaps for effective tool selection and 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?

Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter semantics beyond what's already in the schema descriptions. It mentions 'for a given URL' which aligns with the 'url' parameter but provides no extra context about parameter interactions, defaults, or usage patterns.

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 specific action ('Generate standardized LLMs.txt file') and resource ('for a given URL'), with explicit purpose ('provides context about how LLMs should interact with the website'). It distinguishes from sibling tools like 'scrape', 'crawl', or 'extract' by focusing on generating a specific standardized file format rather than general data 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?

The description provides no guidance on when to use this tool versus alternatives like 'firecrawl_scrape' or 'firecrawl_extract' for similar URL processing tasks. It doesn't mention prerequisites, limitations, or scenarios where this specific LLMs.txt generation is preferred over other data retrieval methods available in the sibling toolset.

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 discovery methods (sitemap.xml and HTML links) but lacks critical details: it doesn't specify if this is a read-only operation, potential rate limits, authentication needs, output format, or whether it performs recursive crawling. For a discovery tool with 6 parameters, this leaves significant behavioral 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 perfectly concise with two clear sentences that front-load the core purpose. Every word earns its place: the first sentence states what the tool does, and the second adds important technical context about discovery methods 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?

For a discovery tool with 6 parameters and no annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of URLs, metadata), how results are structured, potential limitations, or error conditions. Given the complexity implied by multiple discovery methods and filtering options, more behavioral context is needed.

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 good parameter documentation. The description adds minimal value beyond the schema by mentioning 'sitemap.xml and HTML link discovery,' which relates to the 'ignoreSitemap' and 'sitemapOnly' parameters. However, it doesn't explain parameter interactions or provide additional context beyond what's already in the 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: 'Discover URLs from a starting point' specifies the verb (discover) and resource (URLs). It distinguishes from siblings like 'scrape' or 'extract' by focusing on discovery rather than content extraction. However, it doesn't explicitly differentiate from 'firecrawl_crawl' which might have overlapping 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 implies usage context by mentioning 'both sitemap.xml and HTML link discovery,' suggesting this tool is for initial URL discovery rather than content processing. However, it doesn't provide explicit guidance on when to use this vs. alternatives like 'firecrawl_crawl' or 'firecrawl_search,' nor does it specify 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 the full burden of behavioral disclosure. It describes key capabilities (content extraction formats, action execution) and scope (single webpage), but lacks information about rate limits, authentication needs, error handling, or what happens with dynamic content. The mention of 'advanced options' is vague without specifics on limitations or performance characteristics.

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 sentences that efficiently convey core functionality. The first sentence states the primary purpose and key features, while the second adds important behavioral context. There's no wasted language, though it could be slightly more front-loaded with the most critical information.

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 complex tool with 13 parameters, nested objects, and no output schema or annotations, the description provides adequate but incomplete context. It covers the 'what' (scraping with options) but lacks information about return values, error conditions, performance expectations, or practical limitations. The absence of output schema means the description should ideally address what the tool returns, which it doesn't.

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 documents all 13 parameters thoroughly. The description adds minimal value beyond the schema, mentioning 'advanced options', 'various formats', and 'custom actions' which are already detailed in the schema properties. It doesn't provide additional syntax, examples, or constraints beyond what's in the structured data.

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 ('scrape', 'extract', 'execute') and resources ('single webpage', 'content extraction', 'custom actions'). It distinguishes from sibling tools by emphasizing 'single webpage' versus batch/crawl operations, and mentions advanced options not implied by the name alone.

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 ('scrape a single webpage with advanced options'), but does not explicitly state when not to use it or name specific alternatives. It implies usage for single-page scraping versus batch operations, but lacks explicit exclusions or comparisons to siblings like firecrawl_extract or firecrawl_crawl.

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 describes the two modes of operation (SERP results vs. full content scraping) and mentions optional scraping capabilities, but doesn't cover important behavioral aspects like rate limits, authentication requirements, error handling, or what happens when scrapeOptions are partially specified.

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 - two sentences that efficiently convey the tool's dual functionality. The first sentence states the core purpose, and the second explains the two output modes. Every word earns its place with zero waste 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 complex tool with 8 parameters, nested objects, and no output schema or annotations, the description is somewhat incomplete. While it explains the core functionality well, it doesn't address important contextual aspects like response format details, pagination, error conditions, or how the tool interacts with the many sibling scraping/crawling tools.

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 schema already documents all 8 parameters thoroughly. The description adds some value by explaining the relationship between scrapeOptions and the tool's behavior (full page content vs. SERP results), but doesn't provide additional parameter semantics beyond what's in the well-documented 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 with specific verbs ('search and retrieve content from web pages') and distinguishes it from siblings by specifying it returns SERP results by default or full page content with scraping. It differentiates from tools like firecrawl_scrape or firecrawl_extract by focusing on search-first 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 clear context about when to use this tool: for web search with optional scraping, returning SERP results by default or full content with scrapeOptions. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools (e.g., when to use firecrawl_scrape instead).

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