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Deconstruct2021

cryptopunks-mcp-server

generate_batch_proofs

Generate Merkle proofs for multiple CryptoPunks simultaneously to verify ownership without wallet access, improving efficiency over single-proof methods.

Instructions

Generate Merkle proofs for multiple target punks within a set in a single request. Pure computation, no wallet required. More efficient than calling generate_merkle_proof repeatedly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
punk_indicesYesThe full set of punk indices in the Merkle tree
target_punk_indicesYesThe specific punks to generate proofs for
expected_rootNo

Implementation Reference

  • The handler function that executes the generate_batch_proofs tool by calling the API.
    case "generate_batch_proofs": {
      const result = await api.generateBatchProofs({
        punkIndices: args.punk_indices,
        targetPunkIndices: args.target_punk_indices,
        expectedRoot: args.expected_root,
      });
      return ok(result);
    }
  • The definition and input schema for the generate_batch_proofs tool.
    generate_batch_proofs: {
      description:
        "Generate Merkle proofs for multiple target punks within a set in a single request. Pure computation, no wallet required. More efficient than calling generate_merkle_proof repeatedly.",
      inputSchema: z.object({
        punk_indices: z.array(punkIndex).min(1).describe("The full set of punk indices in the Merkle tree"),
        target_punk_indices: z
          .array(punkIndex)
          .min(1)
          .describe("The specific punks to generate proofs for"),
        expected_root: z.string().optional(),
      }),
  • The API implementation that performs the backend request for generateBatchProofs.
    export async function generateBatchProofs(params: {
      punkIndices: number[];
      targetPunkIndices: number[];
      expectedRoot?: string;
    }) {
      return post(BIDS_BASE, "/api/v1/merkle/batch-proofs", params);
    }
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 effectively adds context by stating 'Pure computation, no wallet required' (clarifying authentication needs) and 'More efficient than calling generate_merkle_proof repeatedly' (performance characteristic), though it doesn't cover rate limits or error behavior.

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 are front-loaded and zero wasted words. Every phrase adds value: the first sentence states the core purpose, and the second adds important behavioral context.

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 computational tool with no annotations and no output schema, the description provides good context about what it does and its efficiency advantage. However, it doesn't describe the return format or what happens with invalid inputs, leaving some gaps in completeness.

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 67% (two parameters have descriptions, one does not). The description doesn't add any parameter-specific information beyond what the schema provides, so it doesn't compensate for the coverage gap. With moderate schema coverage, the baseline 3 is appropriate.

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 tool's purpose with specific verbs ('Generate Merkle proofs') and resources ('multiple target punks within a set'), and explicitly distinguishes it from its sibling 'generate_merkle_proof' by noting it's 'more efficient than calling generate_merkle_proof repeatedly.'

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 for when to use this tool ('more efficient than calling generate_merkle_proof repeatedly'), but does not explicitly state when not to use it or mention other alternatives beyond the one sibling tool.

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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