Extracts clean plain text from a URL using Tavily. Returns readable content for analysis, avoiding binary or structured data.
Plain Tavily extract — returns clean text from a URL.
Prefer `web_fetch_structured` when you need typed key_facts /
numeric_values rather than prose. For PDFs, use `pdf_fetch` instead —
Tavily's Extract often returns "binary / not extractable" for them.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions 'clean text' and PDF limitation but lacks details on error handling, rate limits, or auth needs. Adequate for a simple 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?
Three sentences, front-loaded with purpose, efficient and free of waste.
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 one-parameter tool with no output schema, the description covers purpose, usage guidelines, and a key limitation (PDFs). Sufficient for 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 0%, and the description adds no extra meaning for the single 'url' parameter beyond its name and type. Should provide format or 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 'returns clean text from a URL' with a specific verb and resource, and distinguishes from siblings web_fetch_structured and pdf_fetch.
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?
Explicitly says when to use web_fetch_structured for structured data and pdf_fetch for PDFs, providing clear context for alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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