pricing_infoFree — list every paid tool in the 'web-intel' bundle with its price, payTo address and network. Call this first if you don't have a wallet ready yet.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful context: the tool is free, requires no wallet, and enumerates the response fields (price, payTo, network). This goes beyond the bare safety annotation profile.
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?
Two short sentences, front-loaded with 'Free — list...' and no filler. Every word earns its place, making it highly scannable for an agent.
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?
Given zero parameters and no output schema, the description adequately states what will be returned (paid tools with price, payTo address, network) and when to invoke it first. There is no unresolved ambiguity for tool selection or invocation.
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 tool has zero parameters and schema coverage is 100% (empty properties object). With no parameters to explain, the baseline of 4 applies, and the description does not need to add parameter-level detail.
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 uses a specific verb ('list') and clearly identifies the resource ('every paid tool in the 'web-intel' bundle') plus the exact details returned (price, payTo address, network). This makes it easy to distinguish from the sibling content-extraction tools.
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?
Provides an explicit trigger: 'Call this first if you don't have a wallet ready yet.' This tells the agent when to use the tool, though it does not name alternative tools or explicitly state when not to use it.
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.
There are multiple overlapping tool pairs: tech-stack-detector vs tech-stack-change-detector (the latter is a superset that includes detection), and url-to-markdown vs structured-extract vs sitemap-to-knowledge (all fetch web pages and convert content, differing only in output format). These boundaries are unclear, and an agent could easily select the wrong one without deep reading of descriptions.
Naming is a mix of hyphenated descriptors (domain-health-checker, tech-stack-detector), snake_case (pricing_info), and verb phrases (structured-extract, url-to-markdown). The pattern is inconsistent: some tools are named after the action (extract, convert), others after the target (shopify-store-intelligence). This makes it hard to predict tool names.
With 10 tools, the count is well within the 3-15 ideal range. Each tool addresses a distinct web intelligence need (domain health, tech stack, e-commerce, content extraction, pricing), and none are purely redundant filler. The scale feels appropriate for the server's stated purpose.
The surface covers core web intelligence workflows well: domain auditing, tech stack detection (with change detection), content extraction, and e-commerce monitoring for Shopify and Zid. Minor gaps exist, such as missing generic e-commerce platform coverage or a dedicated WHOIS lookup, but these are not critical given the existing domain-health-checker.