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Lyrenth

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read_urls

Fetch up to 20 public web pages in a single call and receive each as a clean, structured Markdown document. Handles failures per URL without blocking others, enabling efficient comparison or summarization.

Instructions

Read several public web pages in one batch call, each as a clean AIDocument. Up to 20 URLs, faster than calling read_url repeatedly. Use it to compare or summarize multiple pages at once; a failed URL is reported per-item and does not block the others. Powered by Lyrenth.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes1-20 absolute http(s) URLs to read.
freshNoForce a fresh fetch for all URLs instead of cached versions. Slower; default false.
max_tokensNoCap each returned document to roughly this many tokens.
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses batch size limit (20), speed advantage, and per-item error handling. Lacks details on rate limiting, authentication, or concurrency, but for a read tool these are minor omissions.

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

Conciseness5/5

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

Three clear, front-loaded sentences with no redundancy. Every sentence provides essential information.

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

Completeness4/5

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

Given no output schema, the description mentions 'clean AIDocument' but could specify the exact return format. However, for a 3-parameter tool with good sibling differentiation, it is mostly complete.

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

Parameters3/5

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

Schema coverage is 100% with each parameter described. The description adds context about batch size and caching but does not elaborate on 'fresh' or 'max_tokens' beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the verb 'Read', the resource 'several public web pages', and the output 'clean AIDocument'. It clearly distinguishes from the sibling 'read_url' by highlighting batch capability and speed.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'to compare or summarize multiple pages at once'. Implicitly recommends using 'read_url' for single URLs. Also explains error handling (failed URLs don't block others).

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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