read_url

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

With no annotations, the description fully carries behavioral disclosure. It explains that it fetches the page, strips navigation/scripts/ads, returns up to 8000 characters, handles retries, anti-bot protection, and Google Cache fallback. This is comprehensive for a read 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 (5 sentences) with front-loaded purpose. Each sentence serves a purpose: action, use case, output specifics, handling mechanisms, and parameter details. No unnecessary words.

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?

The tool has one parameter and an output schema. The description explains the output (title, URL, clean text length) and error handling (retries, cache fallback). It is mostly complete, though it could mention error responses for unreachable pages.

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 0% coverage for 'url', but the description adds an example and the requirement for a full URL (e.g., including protocol). This provides needed context beyond the schema's type definition, though more details on validation could improve.

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 reads a URL and extracts clean text. It specifies the verb 'Read and extract' and resource 'clean text content from a URL'. It also mentions it's useful before or after research(), distinguishing it from sibling tools like gather_sources which likely handle multiple sources.

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 says 'Useful for reading a specific source in detail before or after running research()', providing clear context when to use. It does not explicitly state when not to use, but the context sufficiently guides an agent.

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