private_data_verificationProve a fact about private data without revealing the data: validates text integrity and returns a zero-knowledge proof (ZKP) with a verification ID and URL. Costs $1.00 per call, paid in USDC over x402.
| Name | Required | Description | Default |
|---|---|---|---|
| cpdv_data | Yes | Data (e.g., Test Data) | |
| 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?
With no annotations provided, the description carries the full burden and does well: it discloses the per-call cost ($1.00 USDC via x402), the optional subscription token path to quota, and the output (ZKP with verification ID and URL). It does not mention rate limits or data storage details, but the core behaviors are covered.
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 and front-loads the core purpose. It packs cost, payment method, output artifacts, and privacy guarantee without repetition or fluff.
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?
With only two parameters, no output schema, and no annotations, the description covers the essential return values and cost model. It is complete for basic invocation, though it could briefly mention what the verification ID/URL are for or how it differs from sibling verification tools to be fully self-contained.
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 covers both parameters with descriptions, so the baseline is 3. The description adds meaningful semantics by explaining cpdv_data as 'text integrity' validation and clarifying that the tool proves facts without revealing the data, which elaborates on the schema's terse 'Data (e.g., Test Data)' example.
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?
Description uses a specific verb 'prove' and clearly states the resource: a fact about private data, validated as text integrity, with a zero-knowledge proof output. This distinguishes it from sibling verification tools by focusing on private data rather than tokens, media, or wallets.
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 opening phrase 'Prove a fact about private data without revealing the data' provides clear context for when this tool should be used. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full exclusionary guidance.
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.