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read_web_page

Read any public URL as clean, LLM-ready markdown (main content + title + links). Paid: $0.01 USDC via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes

TDQS

B3/5.0
Behavior3/5

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

Without annotations, the description must disclose behavior. It reveals that the tool incurs a payment of $0.01 USDC via x402, which is significant. However, it omits details about failure modes, rate limits, or constraints on what constitutes a 'public URL'.

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?

The description is extremely concise (two sentences) and front-loaded with the core purpose. Every word is necessary, and the structure is clear: function first, then cost.

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

Completeness2/5

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

Given the lack of annotations, output schema, and minimal parameter documentation, the description should provide more context about expected inputs, outputs, and constraints. It only covers basic functionality and cost.

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

Parameters2/5

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

The only parameter 'url' has no schema description (0% coverage). The description adds only that the URL must be public, but does not specify format, allowed schemes, or provide examples.

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

Purpose4/5

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

The description clearly states the tool reads a public URL and returns LLM-ready markdown with main content, title, and links. However, it does not differentiate from sibling tools like extract_web_data, which might have overlapping functionality.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The mention of payment is a cost consideration but does not help with tool selection.

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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TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a distinct action: checking AI visibility, extracting structured data, listing agents, making videos, reading web pages, and verifying outputs. No overlapping purposes.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., check_ai_visibility, extract_web_data, list_vevang_agents), which is predictable and clear.

Tool Count5/5

6 tools is a well-scoped set for a general-purpose agent toolkit, covering a range of useful actions without being overwhelming or too sparse.

Completeness4/5

The tool surface covers core operations like checking, extracting, creating, reading, and verifying, but could benefit from a search or management tool for agents, though it is largely complete for its stated purpose.

Resources