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TypeScript
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get_nft_metadata

Retrieve NFT metadata including name, image, attributes, and CAIP-19 asset ID for a given token identifier across EVM and Solana networks.

Instructions

Get detailed metadata for a specific NFT (name, image, attributes, CAIP-19 assetId). EVM: contractAddress:tokenId, Solana: mintAddress.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "token_identifier": "<token_identifier>",
      "network": "<network>"
    },
    "name": "get_nft_metadata"
  }
}

Input Schema

NameRequiredDescriptionDefault
token_identifierYesNFT identifier. EVM: {contractAddress}:{tokenId}. Solana: {mintAddress}.
networkYesNetwork identifier (e.g., "ethereum-mainnet", "solana-mainnet" or CAIP-2 "eip155:1").
wallet_idNoTarget wallet ID. Required for multi-wallet sessions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.11.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool 'gets metadata,' implying a read operation, but does not explicitly confirm non-destructiveness, authentication requirements, rate limits, or error handling. The description adds minimal behavioral context beyond the obvious.

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

Conciseness4/5

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

The description is two sentences with no fluff. The first sentence states purpose and output fields, the second provides network-specific examples. It is front-loaded and efficient, though could be slightly improved by noting read-only nature.

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 no output schema, the description lists expected metadata fields but does not detail return structure or behavior on errors (e.g., missing NFT). The schema covers parameters well, but for a cross-network tool, more context about expected responses would enhance 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?

Input schema coverage is 100% with clear parameter descriptions (e.g., token_identifier format). The description adds examples and mentions CAIP-19 assetId, but these are partially redundant with schema descriptions. Baseline 3 is appropriate as schema already explains parameters well.

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 retrieves detailed metadata for a specific NFT, listing the specific fields (name, image, attributes, CAIP-19 assetId) and providing examples for EVM and Solana networks. This distinguishes it from siblings like list_nfts (which lists NFTs) and get_assets (broader asset retrieval).

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

Usage Guidelines3/5

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

The description implies usage for a single NFT metadata retrieval but does not explicitly state when to use this vs alternatives like list_nfts or get_assets. No when-not-to-use or prerequisite conditions are mentioned, leaving the agent to infer context.

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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