Fetch any public web page and return clean, LLM-ready markdown (plus title and final URL). Send { url }. We handle fetching, redirects, and noise-stripping. [x402 paid tool — price $0.003; POST /api/read]
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public http(s) URL to read |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry behavioral weight. Mentions paid pricing, POST method, handling of redirects and noise-stripping, but lacks detail on rate limits, dynamic content handling, or error responses.
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?
Exceptionally concise: two sentences and a cost/endpoint note. No wasted words, front-loads the core purpose and return shape.
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 one-param tool with no output schema, the description covers purpose, input, output shape, and pricing. Minor omission: no hint about error cases or content restrictions.
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 coverage is 100% for the single parameter. Description adds little beyond schema: confirms URL must be public and http(s). Meets baseline but does not compensate for any gaps.
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?
Clearly states fetching any public web page and returning LLM-ready markdown. Differentiates from read_structured implicitly but not explicitly, missing an opportunity to clarify when to use this vs structured extraction.
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?
Implies usage for public web pages needing clean text, but no explicit when-not-to-use or alternatives like read_structured. The pricing info adds context but does not guide selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools are organized by domain prefix (e.g., 'crypto_', 'rh_', 'snipe_'), which helps distinguish between areas. Within each domain, they serve distinct purposes, though some overlap between domains exists (e.g., price data appears in multiple groups). Overall, an agent can navigate effectively.
All tool names follow a consistent snake_case pattern with a domain prefix and a verb_noun combination (e.g., 'compliance_risk', 'rh_stock', 'snipe_honeypot'). This makes the API predictable and easy to explore.
With 159 tools, the server is extremely large. While the broad scope of web3 and utility functions justifies many tools, the count is significantly above the typical range for a coherent toolkit, potentially overwhelming agents and increasing selection error.
The toolkit covers a wide range of web3 operations: crypto, DeFi, compliance, safety, scheduling, memory, etc. There are no obvious major gaps for its intended purpose, though some niche areas might be missing.