verify_vdf

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

With no annotations provided, the description discloses key behavioral traits: it performs efficient verification without redoing squarings and costs ~$0.001 USDC. It does not mention side effects, but the operation is read-only and non-destructive.

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 concise (under 100 words) and well-structured: starts with purpose, then parameter usage, then return value, then cost, then example. Every sentence serves a purpose with no waste.

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 the tool's complexity and the presence of an output schema, the description fully covers purpose, parameters, return format, cost, and provides an example. It is complete for an AI agent to use correctly.

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%, and the description adds significant context: it explains that parameters come from 'compute_vdf', provides an example call, and clarifies the proof object structure. This adds meaning beyond the schema.

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 'Verify a Chronos VDF proof in a single exponentiation' and explains the mathematical verification (y = g^(2^T) mod N). It distinguishes itself from the sibling 'compute_vdf' by focusing on verification rather than computation.

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 explicitly states to pass the parameters returned by 'compute_vdf' or any party claiming a VDF result. It gives clear context for when to use the tool, though does not 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.