read_url

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

The description discloses that the tool strips navigation, ads, and boilerplate, preserves the article body, and returns structured metadata. It also mentions the payment mechanism via x402 protocol. No annotations are provided, so the description carries the full burden, and it does so 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 moderately sized and front-loaded with the core function. Every sentence adds value, including cost and protocol details. Minor room to tighten, but no significant fluff.

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 tool with one parameter and no output schema, the description covers purpose, behavior, cost, and usage scenarios. It lacks details on error handling or rate limits, but overall provides sufficient context for an AI agent to select and invoke the tool correctly.

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 covers the only parameter (url) with a description, achieving 100% coverage. The description adds value by explaining that the URL is fetched and cleaned to produce Markdown, providing context beyond the schema's minimal description.

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 fetches any URL and returns clean Markdown, specifying the resource (URLs), action (fetch and clean), and output (Markdown with metadata). It also lists example use cases (articles, docs, blog posts), making the purpose unambiguous.

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 'Use this whenever you need to read web content' and provides examples. It also mentions the cost model. However, it does not explicitly state when not to use this tool or list alternatives, but given no sibling tools, the context is clear.

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