exa

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

Annotations confirm read-only/idempotent safety, while description adds valuable behavioral context: output format ('clean markdown'), specific return values ('Clean text content and metadata'), and operational pattern ('Batch multiple URLs'). Does not contradict annotations. Could mention rate limits or error handling for a 5.

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

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

Five sentences with zero waste. Front-loaded with core function (sentence 1), followed by sibling relationship, use case specification, operational note (batching), and return value disclosure. The 'Best for:' and 'Returns:' labels create scannable structure that front-loads critical decision-making 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?

Given 100% schema coverage and helpful annotations, the description appropriately compensates for missing output schema by describing returns ('Clean text content and metadata'). Covers sibling relationship, batching behavior, and format expectations. Missing only edge case handling (timeouts, redirects) for a complete picture.

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 coverage is 100% (both parameters fully documented). The description mentions batching behavior but this merely repeats the schema's description for the 'urls' parameter. Does not add syntax details, examples, or semantics beyond what the schema already provides, warranting the baseline score.

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 core action ('Read a webpage's full content') and resource, with specific output format ('as clean markdown'). It explicitly distinguishes from sibling tool web_search_exa by stating when to use this instead ('when highlights are insufficient'), making the differentiation crystal clear.

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

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

Provides explicit temporal and conditional guidance: 'Use after web_search_exa when highlights are insufficient' establishes sequence and fallback criteria. 'Best for: Extracting full content from known URLs' defines ideal use case. Also notes batching capability for efficiency optimization.

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?

The description adds valuable behavioral context beyond what annotations provide. While annotations indicate read-only, idempotent, and non-destructive operations, the description specifies that it returns 'clean text content from top search results, ready for LLM use' and provides query tips about describing ideal pages rather than keywords. It also mentions category-specific searches (people/company) and the relationship with web_fetch_exa for follow-up actions.

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 well-structured and appropriately concise. It uses clear sections (purpose, best for, returns, query tips, category guidance, follow-up action) with bullet-like formatting. Every sentence adds value without redundancy, and key information is front-loaded in the opening statement.

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, comprehensive annotations, and full schema coverage, the description provides complete contextual information. It covers purpose, usage scenarios, behavioral characteristics, parameter guidance, and sibling tool relationships. While there's no output schema, the description adequately explains what the tool returns ('clean text content from top search results').

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 thoroughly documents both parameters. The description adds some semantic context by reinforcing query tips ('describe the ideal page, not keywords') and mentioning category usage, but doesn't provide significant additional parameter meaning beyond what's in the schema. This meets the baseline expectation when schema coverage is high.

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 for any topic and get clean, ready-to-use content.' It specifies the verb ('Search'), resource ('the web'), and distinguishes from its sibling web_fetch_exa by noting this tool returns search results while the sibling fetches specific URLs. The 'Best for' section further clarifies use cases like finding current information, news, facts, etc.

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 guidance on when to use this tool versus alternatives. It states 'Best for: Finding current information, news, facts, people, companies, or answering questions about any topic' and explicitly mentions when to use the sibling tool: 'If highlights are insufficient, follow up with web_fetch_exa on the best URLs.' This gives clear context for tool selection.

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