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

No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions the tool 'finds comprehensive information' and 'provides insights', it doesn't describe what format the results come in, whether there are rate limits, authentication requirements, cost implications, or how the tool handles ambiguous company names. For a research 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 efficiently structured in two sentences that clearly communicate the tool's purpose and capabilities. The first sentence establishes the core function, and the second elaborates on the types of insights provided. There's no wasted language, though it could be slightly more specific about the tool's scope relative to siblings.

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 research tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what format the results will be in, how comprehensive the information actually is, whether there are limitations on company types or regions, or what happens when no information is found. The description covers basic purpose but leaves too many practical usage questions unanswered.

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 fully documents both parameters ('companyName' and 'numResults'). The description doesn't add any parameter-specific information beyond what's in the schema. It mentions researching companies generally but provides no additional context about parameter usage, formatting requirements, or search behavior.

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: 'Research companies using Exa AI' with specific resources ('businesses, organizations, corporations') and outcomes ('comprehensive information', 'insights into company operations, news, financial information, and industry analysis'). It distinguishes from generic search tools but doesn't explicitly differentiate from sibling tools like 'linkedin_search_exa' or 'deep_researcher_start'.

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 multiple sibling tools including 'linkedin_search_exa', 'web_search_exa', and 'deep_researcher_start', there's no indication of when this company-specific research tool is preferred over other search or research tools. The description only states what it does, not when to use it.

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 what the tool retrieves ('full text content, metadata, and structured information') but lacks critical behavioral details: no information about rate limits, authentication requirements, error handling, or what happens when maxCharacters is exceeded. For a web crawling tool with no annotation coverage, this leaves significant gaps in understanding operational constraints.

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 each serve a purpose: the first defines the core functionality, the second provides usage context. It's front-loaded with the main purpose. While efficient, the second sentence could be slightly more specific about the 'known URLs' distinction compared to sibling tools.

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 2 parameters with 100% schema coverage but no annotations and no output schema, the description provides adequate basic context about what the tool does. However, for a web crawling tool that likely has operational constraints (rate limits, authentication, error cases) and returns complex data, the description should ideally mention more about behavioral aspects and expected output format since there's no output schema to document return values.

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 both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain URL format requirements, character counting methodology, or provide examples. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

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 and crawl content from specific URLs using Exa AI - retrieves full text content, metadata, and structured information from web pages.' It specifies the verb ('extract and crawl'), resource ('content from specific URLs'), and technology ('Exa AI'). However, it doesn't explicitly distinguish this tool from sibling tools like 'web_search_exa' or 'deep_search_exa', which likely have different search/crawling approaches.

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

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

The description provides some implied usage context: 'Ideal for extracting detailed content from known URLs' suggests this tool is for targeted extraction when URLs are already identified. However, it doesn't explicitly state when NOT to use this tool or mention alternatives among the sibling tools (e.g., when to use 'web_search_exa' for broader searches vs. this tool for specific URL extraction).

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 and does so effectively. It discloses key behavioral traits: the built-in 5-second delay before checking, the polling requirement, and the status-based workflow ('running' vs 'completed'). It doesn't cover error handling or rate limits, but provides substantial operational context.

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 efficiently structured with zero wasted sentences. It front-loads the core purpose, then provides essential operational details (delay, polling instructions). Every sentence earns its place by conveying critical information for correct tool invocation.

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 single-parameter tool with no annotations and no output schema, the description provides excellent context about the polling behavior and workflow. It could be more complete by describing the output format or error conditions, but given the tool's relative simplicity, it covers the most critical aspects well.

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 the single parameter (taskId). The description adds context by specifying that the taskId comes from deep_researcher_start, which is helpful but doesn't provide additional semantic meaning beyond what the schema indicates. 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 ('check the status', 'retrieve results') and identifies the resource ('deep research task'). It distinguishes from siblings like deep_researcher_start (which initiates research) by focusing on monitoring and result retrieval rather than task creation.

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 explicit usage instructions: call this tool repeatedly (poll) until status becomes 'completed', and when status is 'running', wait a few seconds and call again with the same task ID. It also implicitly distinguishes from siblings by referencing deep_researcher_start as the source of the task ID.

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 effectively describes the agent's behavior (performs web searches, crawls pages, analyzes information, synthesizes findings) and mentions timing considerations via model options, but lacks details on rate limits, authentication needs, or error handling that would be valuable for an AI agent.

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 and front-loaded with the core purpose, followed by usage guidance and operational instructions. While efficient, the second sentence could be slightly more concise by combining some elements of the agent's actions.

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 (initiating an AI research agent) and lack of annotations/output schema, the description does well by explaining the agent's behavior, model options, and follow-up process. However, it could better address potential limitations or failure modes to be fully complete for agent 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 the schema already fully documents both parameters. The description doesn't add any meaningful parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter understanding.

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 ('start', 'initiates') and resources ('comprehensive AI-powered deep research task', 'intelligent agent'), distinguishing it from siblings like 'deep_search_exa' or 'web_search_exa' by emphasizing complex queries requiring in-depth analysis rather than simple searches.

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 this tool ('for complex research questions that require in-depth analysis rather than simple searches') and provides clear alternatives by naming 'deep_researcher_check' as the follow-up tool for monitoring progress, with implicit guidance against using simpler search tools for such tasks.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, repeatable read operation. The description adds minimal behavioral context beyond this - it mentions the output format ('natural language format') but doesn't disclose rate limits, authentication needs, or other operational constraints that would be helpful for an agent.

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 - a single sentence that states the core functionality. There's no wasted language or unnecessary elaboration. It's front-loaded with the essential information about what the tool does.

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 search tool with good annotations (readOnly, idempotent, non-destructive) and full schema coverage, the description is minimally adequate. However, without an output schema and with multiple sibling tools that likely overlap in functionality, the description should do more to explain what makes this tool unique and what kind of results to expect.

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

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

With 100% schema description coverage, the input schema already fully documents both parameters ('objective' and 'search_queries'). The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how parameters interact or provide usage examples. The baseline of 3 is appropriate when the schema does all the work.

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: 'Searches the web and return results in a natural language format.' It specifies the verb ('searches'), resource ('the web'), and output format ('natural language format'), but doesn't explicitly differentiate it from sibling tools like 'web_search_exa' or 'company_research_exa' 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. With multiple sibling tools like 'web_search_exa', 'company_research_exa', and 'linkedin_search_exa', there's no indication of what makes 'deep_search_exa' distinct or when it should be preferred over other search tools on the server.

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

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by emphasizing 'highest quality and freshest context' and the programming domain focus, but doesn't disclose additional behavioral traits like rate limits, authentication needs, or response format details. No contradiction with annotations exists.

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 front-loaded with the core purpose and usage rule, but includes some redundancy (e.g., repeating 'exa-code' emphasis). Sentences are generally purposeful, though the 'RULE' phrasing could be more integrated. Overall efficient but with minor verbosity.

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

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

Given the tool's moderate complexity (2 parameters, no output schema), annotations cover safety aspects, and the description provides clear purpose and usage rules. However, it lacks details on response structure or error handling, which would enhance completeness for a search 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 100%, providing full documentation for both parameters. The description doesn't add meaningful parameter semantics beyond what's in the schema, such as explaining query formulation strategies or token usage trade-offs. Baseline score of 3 is appropriate given the comprehensive 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: 'Search and get relevant context for any programming task' with specific focus on 'libraries, SDKs, and APIs.' It distinguishes from the sibling tool 'web_search_exa' by specifying programming-related content, though it doesn't explicitly contrast their differences beyond domain focus.

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 explicit usage guidance: 'Use this tool for ANY question or task related to programming' and includes a mandatory rule: 'when the user's query contains exa-code or anything related to code, you MUST use this tool.' This clearly defines when to use it versus alternatives, though it doesn't specify when NOT to use it for non-programming queries.

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 what the tool finds but doesn't describe important behavioral traits like rate limits, authentication requirements, response format, pagination behavior, or whether this is a read-only operation. The description is functional but lacks operational transparency needed for an agent to use it effectively.

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 at two sentences, with the first sentence stating the core functionality and the second providing usage context. It's front-loaded with the essential information and avoids unnecessary elaboration. Every sentence earns its place, though the second sentence could be slightly more specific.

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 no annotations and no output schema, the description is incomplete for a search tool with 3 parameters. It doesn't describe what the return format looks like (structured data, raw HTML, JSON), how results are ranked, error conditions, or any limitations of the LinkedIn search through Exa AI. For a tool with no structured behavioral metadata, the description should provide more operational 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%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it doesn't explain query construction best practices, searchType implications, or numResults limitations. 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 searches LinkedIn profiles and companies using Exa AI, specifying the resource (LinkedIn) and purpose (finding professional profiles, company pages, business content). It distinguishes from some siblings like web_search_exa by mentioning LinkedIn specifically, but doesn't explicitly differentiate from company_research_exa or deep_search_exa which might have overlapping domains.

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 implied usage context ('useful for networking, recruitment, and business research'), suggesting when this tool might be appropriate. However, it doesn't explicitly state when to use this tool versus alternatives like company_research_exa or deep_search_exa, nor does it provide any exclusion criteria or specific prerequisites for use.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it mentions real-time web searches, scraping from specific URLs, configurable result counts, and returning content from relevant websites. This provides useful operational details without contradicting annotations.

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

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

The description is appropriately sized at two sentences, front-loading the core purpose. Every sentence adds value: the first defines the tool's function, and the second elaborates on features and output. There's no wasted text, though it could be slightly more structured for optimal 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 moderate complexity, rich annotations (covering safety and idempotency), and 100% schema coverage, the description is reasonably complete. It explains the tool's function and key features. The lack of an output schema is a minor gap, but the description mentions return content, partially compensating. For a read-only search tool, this provides adequate 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%, so the schema fully documents all 5 parameters. The description adds minimal parameter semantics beyond the schema, mentioning only 'configurable result counts' (referencing numResults) and 'content from the most relevant websites' (hinting at query relevance). Since the schema does the heavy lifting, 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 tool's purpose: 'Search the web using Exa AI - performs real-time web searches and can scrape content from specific URLs.' It specifies the verb (search/scrape) and resource (web/URLs), making the function unambiguous. However, it doesn't explicitly differentiate from its sibling 'get_code_context_exa' beyond mentioning general web search capabilities.

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 web searches and content scraping, but provides no explicit guidance on when to use this tool versus its sibling 'get_code_context_exa' or other alternatives. It mentions configurable result counts and relevance, which suggests some context, but lacks clear when/when-not directives or named alternatives.

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