web_application_verificationScan a website or dApp frontend for vulnerabilities using OWASP checks, before connecting a wallet to it or trusting it with user data. Costs $1.00 per call, paid in USDC over x402.
| Name | Required | Description | Default |
|---|---|---|---|
| target_url | Yes | Target URL | |
| subscription_token | No | Optional Cybercentry subscription token. Uses your plan quota instead of paying per call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses the cost ($1.00 per call, paid in USDC over x402), which is a key behavioral trait beyond the schema. However, it does not mention whether the scan is read-only, what data is sent to the service, or the format of the returned results.
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 two sentences with no filler. The first sentence covers purpose and use case, the second conveys cost and payment method. Every word earns its place.
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?
There is no output schema, so the description should explain what kind of result the agent can expect. It doesn't mention the output format, severity levels, or scope of OWASP checks. While the use case and cost are covered, the missing output information leaves the agent under-informed.
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 input schema has 100% coverage with both parameters described ('target_url' and 'subscription_token'). The description adds context that target_url refers to a website or dApp frontend, but this is already inferable from the tool's purpose. Baseline of 3 is appropriate.
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's purpose with a specific verb ('Scan') and resource ('a website or dApp frontend'), and notes it uses OWASP checks. It distinguishes itself from sibling verification tools by targeting web/dApp frontends rather than tokens, wallets, or media.
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 provides an explicit use case: 'before connecting a wallet to it or trusting it with user data.' This tells when to use the tool, though it doesn't explicitly mention alternatives or 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.
Most tools target distinct domains (token, media, AI agent, wallet, web app, code), but `base_token_verification` and `ethereum_token_verification` are closely related and could be confused despite chain-specific descriptions. The informational tools (`list_services`, `recent_exploits`) are clearly separate.
All tool names are lowercase snake_case, with the majority following a `[domain]_verification` pattern. The exceptions (`cyber_security_consultant`, `list_services`, `recent_exploits`) are still clear but deviate from the dominant suffix convention.
12 tools is a well-scoped size for a multi-domain verification service. Each tool represents a distinct service category, and the count feels appropriately comprehensive without being bloated.
The tool surface covers major verification needs across tokens, code, media, AI agents, wallets, web apps, private data, and quantum-safe encryption. The addition of `cyber_security_consultant`, `list_services`, and `recent_exploits` provides context and support, leaving no obvious dead ends for typical use cases.