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Deconstruct2021

cryptopunks-mcp-server

verify_merkle_proof

Validate Merkle proofs for CryptoPunks ownership verification. Check proof integrity against specified root and punk index using pure computation.

Instructions

Verify that a Merkle proof is valid for a given punk index and Merkle root. Pure computation, no wallet required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
punk_indexYesCryptoPunk index (0–9999)
proofYesArray of proof hashes
rootYesThe Merkle root to verify against

Implementation Reference

  • Handler logic for verify_merkle_proof, which calls the api.verifyMerkleProof function.
    case "verify_merkle_proof": {
      const result = await api.verifyMerkleProof(
        args.punk_index,
        args.proof,
        args.root,
      );
      return ok(result);
    }
  • Definition and input schema for the verify_merkle_proof tool.
    verify_merkle_proof: {
      description:
        "Verify that a Merkle proof is valid for a given punk index and Merkle root. Pure computation, no wallet required.",
      inputSchema: z.object({
        punk_index: punkIndex,
        proof: z.array(z.string()).describe("Array of proof hashes"),
        root: z.string().describe("The Merkle root to verify against"),
      }),
    },
  • API helper function that sends the request to verify the Merkle proof.
    export async function verifyMerkleProof(
      punkIndex: number,
      proof: string[],
      root: string,
    ) {
      return post(BIDS_BASE, "/api/v1/merkle/verify", { punkIndex, proof, root });
    }
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully communicates key behavioral traits: the tool performs verification (implying read-only operation), is 'pure computation' (suggesting deterministic, stateless behavior), and 'no wallet required' (clarifying no authentication or external dependencies). It doesn't mention performance characteristics or error conditions, but covers the essential safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second adds crucial behavioral context about computation requirements. There's zero wasted language or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a verification tool with no annotations and no output schema, the description provides good coverage of the essential context: what it does, what it requires, and its computational nature. It doesn't describe the return value format (true/false, error details), which would be helpful given the lack of output schema, but otherwise addresses the key aspects needed for proper tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter well-documented in the schema itself. The description doesn't add any additional parameter semantics beyond what's already in the schema descriptions. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('verify that a Merkle proof is valid') with the target resource ('for a given punk index and Merkle root'), distinguishing it from sibling tools like generate_merkle_proof or verify_batch_proofs. It explicitly mentions the computational nature and lack of wallet requirement, which adds important context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about when to use this tool ('Pure computation, no wallet required'), indicating it's appropriate for offline verification scenarios. However, it doesn't explicitly state when NOT to use it or mention specific alternatives like verify_batch_proofs for batch operations, which would have earned a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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