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Deconstruct2021

cryptopunks-mcp-server

generate_merkle_root

Generate a Merkle root from CryptoPunk indices to construct collection bids. Pure computation without wallet requirements.

Instructions

Generate a Merkle root for a set of CryptoPunk indices. Pure computation, no wallet required. Used as the first step in constructing a collection bid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
punk_indicesYesPunk indices to include in the Merkle tree
expected_rootNoOptional: verify the result matches this expected root

Implementation Reference

  • The actual API call logic for generating the Merkle root.
    export async function generateMerkleRoot(punkIndices: number[], expectedRoot?: string) {
      return post(BIDS_BASE, "/api/v1/merkle/root", { punkIndices, expectedRoot });
    }
  • The MCP tool handler that invokes the API function.
    case "generate_merkle_root": {
      const result = await api.generateMerkleRoot(
        args.punk_indices,
        args.expected_root,
      );
      return ok(result);
    }
  • src/tools.ts:234-244 (registration)
    The tool definition and schema registration.
    generate_merkle_root: {
      description:
        "Generate a Merkle root for a set of CryptoPunk indices. Pure computation, no wallet required. Used as the first step in constructing a collection bid.",
      inputSchema: z.object({
        punk_indices: z.array(punkIndex).min(1).describe("Punk indices to include in the Merkle tree"),
        expected_root: z
          .string()
          .optional()
          .describe("Optional: verify the result matches this expected root"),
      }),
    },
Behavior3/5

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 adds useful context by stating 'Pure computation, no wallet required,' which clarifies it's a non-destructive, read-like operation without external dependencies. However, it lacks details on computational limits, error handling, or output format, leaving gaps in behavioral understanding for an AI agent.

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 front-loaded and efficiently structured in two sentences: the first defines the core functionality, and the second adds context and usage. Every sentence earns its place by providing essential information without redundancy, making it easy for an AI agent to parse quickly.

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

Completeness3/5

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

Given the tool's computational nature, no annotations, and no output schema, the description is moderately complete but has gaps. It covers purpose and basic usage but lacks details on output format, error cases, or performance constraints, which could hinder an AI agent's ability to handle edge cases effectively in this context.

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%, so the input schema already documents both parameters thoroughly. The description adds minimal value beyond the schema by implying the tool uses 'punk_indices' for the Merkle tree, but it doesn't explain parameter interactions or provide additional semantic context, such as how 'expected_root' affects verification behavior.

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 ('Generate a Merkle root') for a specific resource ('set of CryptoPunk indices'), distinguishing it from siblings like 'generate_merkle_proof' or 'generate_batch_proofs' by focusing on root computation rather than proof generation. It also specifies the application context ('first step in constructing a collection bid'), making the purpose unambiguous.

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 on when to use this tool ('first step in constructing a collection bid') and clarifies that it's 'Pure computation, no wallet required,' which helps differentiate it from wallet-dependent operations. However, it doesn't explicitly state when not to use it or name specific alternatives among siblings, such as when proofs are needed instead.

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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