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 that the tool returns a job ID for status checking, which is useful context about the asynchronous nature. However, it lacks details on permissions, rate limits, error handling, or what the scraping entails (e.g., whether it's destructive or read-only). More behavioral traits are needed for a mutation 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 concise with two sentences that front-load the main action and outcome. There's no wasted text, but it could be slightly more informative without losing efficiency. It effectively communicates the core functionality in a structured manner.

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 parameters, no output schema, and no annotations), the description is incomplete. It doesn't cover parameter details, behavioral aspects like rate limits or errors, or how to interpret results beyond the job ID. For a tool with undocumented parameters and no structured output, 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 'urls' has a description), so the description must compensate. It implies 'urls' parameter usage but doesn't explain 'options' or its nested properties. The description adds minimal value beyond the schema, as it doesn't clarify parameter meanings or usage, leaving gaps for undocumented parameters.

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) and resource (URLs) with the batch mode distinction. However, it doesn't explicitly differentiate from sibling tools like 'firecrawl_scrape' or 'firecrawl_crawl,' which likely handle single URLs or different scraping modes.

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 batch mode but doesn't specify scenarios where batch is preferred over single URL scraping or other siblings like 'firecrawl_search' or 'firecrawl_deep_research.' No exclusions or prerequisites are stated.

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 states the tool checks status but doesn't describe what the status response includes (e.g., progress, errors, completion), whether it's idempotent, or any rate limits. For a status-checking tool with zero annotation coverage, this leaves significant gaps in understanding its behavior 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence earns its place by conveying essential information, achieving optimal 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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects like response format or error handling. Without annotations or an output schema, the description should do more to compensate, but it meets the minimum for a simple status-check tool, leaving room for improvement in 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?

The schema description coverage is 100%, with the single parameter 'id' clearly documented as 'Batch job ID to check.' The description doesn't add any additional meaning beyond this, such as format examples or source context. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately handles parameter semantics without extra description 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 ('check the status') and resource ('batch scraping job'), making the purpose immediately understandable. It distinguishes this tool from other status-checking siblings like 'firecrawl_check_crawl_status' by specifying it's for batch jobs rather than crawl jobs. However, it doesn't explicitly contrast with all possible alternatives, keeping it from 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 implies usage context by specifying 'batch scraping job,' suggesting this should be used after initiating a batch job. However, it doesn't explicitly state when to use this versus alternatives like 'firecrawl_check_crawl_status' or provide guidance on prerequisites (e.g., needing a job ID from 'firecrawl_batch_scrape'). The usage is clear but lacks explicit alternatives 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. While 'check' implies a read-only operation, the description doesn't address important behavioral aspects like authentication requirements, rate limits, what the status response looks like, whether it's real-time or cached, or error conditions. For a status-checking tool with zero annotation coverage, 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, 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 without 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 simple single-parameter status check tool with no output schema, the description is minimally adequate. It tells what the tool does but lacks important context about the status response format, error handling, and differentiation from sibling tools. With no annotations to provide behavioral context, the description should do more to compensate, but it meets the bare minimum for understanding the tool's basic function.

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' clearly documented as 'Crawl job ID to check' in the schema. The description doesn't add any additional parameter information beyond what the schema already provides, which is acceptable given the high schema coverage. The baseline of 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 action ('check') and resource ('status of a crawl job'), making the purpose immediately understandable. It doesn't explicitly distinguish from sibling tools like 'firecrawl_check_batch_status' or 'firecrawl_crawl', but the verb+resource combination is specific enough for basic understanding.

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 siblings like 'firecrawl_check_batch_status' and 'firecrawl_crawl' that likely involve similar status-checking or crawling operations, there's no indication of when this specific 'crawl job status' check is appropriate versus other status-related tools.

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 the tool is 'asynchronous' and supports webhook notifications, which is helpful. However, it doesn't cover critical aspects like rate limits, authentication needs, error handling, what happens if the crawl fails, or how results are returned (since there's no output schema). For a complex 12-parameter tool with no annotations, 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 communicates the core purpose and key features. Every word earns its place—there's no redundancy or 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?

Given the tool's complexity (12 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the crawl produces (e.g., URLs, content, status), how to retrieve results, error conditions, or performance implications. While concise, it lacks the depth needed for such a multifaceted 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 high at 92%, so the schema already documents most parameters well. The description adds some context by mentioning 'depth control' (relates to maxDepth), 'path filtering' (relates to includePaths/excludePaths), and 'webhook notifications' (relates to webhook), but doesn't provide additional syntax or format details beyond what the schema offers. Baseline 3 is appropriate given the strong 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 ('start an asynchronous crawl'), the resource ('multiple pages from a starting URL'), and distinguishes it from siblings by specifying it's for crawling (vs. scraping, extracting, mapping, etc.). It's specific about being asynchronous and handling multiple pages.

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 features like 'depth control, path filtering, and webhook notifications,' suggesting when this tool might be appropriate. However, it doesn't explicitly state when to use this vs. alternatives like firecrawl_scrape or firecrawl_map, 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 the methods (web crawling, search, AI analysis) but doesn't describe key behavioral traits: what 'deep research' entails operationally, whether it's resource-intensive, time-consuming, or has rate limits, what the output format looks like, or any error conditions. For a complex tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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: 'Conduct deep research on a query using web crawling, search, and AI analysis.' It's front-loaded with the core purpose and uses no unnecessary words. Every part of the sentence contributes to understanding the tool's function.

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 (involving multiple methods like crawling, search, and AI analysis), no annotations, no output schema, and 4 parameters, the description is incomplete. It doesn't explain what 'deep research' outputs, how results are structured, or any behavioral nuances. The agent lacks sufficient context to use this tool effectively compared to simpler siblings, making this inadequate for a tool of this scope.

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 four parameters (query, maxDepth, timeLimit, maxUrls) with descriptions and constraints. The description adds no additional meaning about parameters beyond implying they relate to 'deep research.' It doesn't explain how parameters interact (e.g., how depth affects research) or provide usage examples. 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: 'Conduct deep research on a query using web crawling, search, and AI analysis.' It specifies the verb ('conduct deep research') and resource ('a query'), but doesn't explicitly differentiate it from sibling tools like firecrawl_search or firecrawl_crawl, which likely have overlapping functionality. The mention of 'deep research' with multiple methods (crawling, search, AI analysis) provides some distinction but isn't specific about how it differs from simpler search or crawl 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools like firecrawl_search or firecrawl_crawl. The agent must infer usage based on the vague 'deep research' phrasing, which could apply to many scenarios without clear boundaries.

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 LLM-based extraction and deployment options but lacks critical details: what permissions or authentication are needed, rate limits, whether it's read-only or modifies data, error handling, or output format. For a tool with 7 parameters and 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 concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first defines the tool's function, and the second clarifies deployment options. No wasted words, though it could be more structured with bullet points for clarity.

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 (7 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the extraction process, output format, error cases, or how it differs from siblings. For an LLM-based extraction tool with multiple parameters, more context is needed to guide 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?

The input schema has 100% description coverage, so parameters like 'urls,' 'prompt,' and 'schema' are well-documented in the schema itself. The description adds minimal value beyond this, only implying LLM usage and deployment modes without detailing parameter interactions or constraints. Baseline 3 is appropriate given 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: '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 minimal usage guidance. It mentions support for 'cloud AI and self-hosted LLM extraction,' which hints at deployment options but doesn't specify when to use this tool versus alternatives like firecrawl_scrape (for raw content) or firecrawl_deep_research (for more complex analysis). No explicit when/when-not scenarios or prerequisites are provided.

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?

No annotations are provided, so the description carries the full burden. It mentions discovery methods but lacks critical behavioral details: whether this is a read-only operation, potential rate limits, authentication needs, output format, or error handling. For a tool with 6 parameters and no annotations, this is inadequate.

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) and front-loaded with the core purpose. Every word earns its place, with no redundant or vague language. It efficiently communicates the essential functionality without 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?

Given 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of URLs, structured data), error conditions, or performance characteristics. For a discovery tool with potential complexity, more context is needed to guide 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%, so the schema fully documents all 6 parameters. The description adds no additional parameter semantics beyond implying discovery methods (sitemap.xml and HTML links), which aligns with parameters like ignoreSitemap and sitemapOnly. Baseline 3 is appropriate as 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: 'Discover URLs from a starting point' with specific methods ('sitemap.xml and HTML link discovery'). It uses a clear verb ('Discover') and resource ('URLs'), but doesn't explicitly differentiate from sibling tools like firecrawl_crawl or firecrawl_search, 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 provides no guidance on when to use this tool versus alternatives. It mentions the methods (sitemap.xml and HTML links) but doesn't specify scenarios, prerequisites, or exclusions. Given multiple sibling tools (e.g., firecrawl_crawl, firecrawl_search), this lack of comparative context is a significant gap.

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 explains the dual-mode behavior (SERP vs. full content scraping) and mentions optional scraping, which is helpful. However, it lacks details about rate limits, authentication needs, error handling, or what happens when scrapeOptions are omitted versus provided - important for a web scraping 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 perfectly concise - two sentences that front-load the core functionality and clearly explain the dual output modes. Every word earns its place with zero redundancy or 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 complex tool with 8 parameters, nested objects, and no output schema, the description provides adequate context about what the tool does and its dual modes. However, with no annotations and no output schema, it should ideally mention more about behavioral aspects like rate limits, authentication, or error handling to be fully complete for this level of complexity.

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 8 parameters thoroughly. The description adds some value by explaining the relationship between scrapeOptions and output behavior, but doesn't provide additional semantic context beyond what's in the schema descriptions. This meets the baseline 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 ('search and retrieve content from web pages') and distinguishes it from siblings by specifying it returns SERP results by default or full content with scraping. It explicitly differentiates from tools like firecrawl_scrape and 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 (search with optional scraping) and implies alternatives by mentioning 'SERP results by default' versus 'full page content when scrapeOptions are provided'. However, it doesn't explicitly name when to use this versus siblings like firecrawl_scrape or firecrawl_extract, which would require more specific guidance.

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