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get_token_forensics

Analyze token contract addresses for honeypot detection, rug pull risks, and liquidity analysis to assess safety and security.

Instructions

Get forensics and safety data for a token contract address. Includes honeypot detection, rug pull risk, and liquidity analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesToken contract address (0x...)
chainNoChain to query (default: base)base

Implementation Reference

  • The handler implementation for the `get_token_forensics` tool, which uses the Maiat SDK to fetch token forensics and safety data.
    // ---- Tool: get_token_forensics ----
    server.tool(
      "get_token_forensics",
      "Get forensics and safety data for a token contract address. Includes honeypot detection, rug pull risk, and liquidity analysis.",
      {
        address: z.string().describe("Token contract address (0x...)"),
        chain: z
          .string()
          .default("base")
          .describe("Chain to query (default: base)"),
      },
      async ({ address, chain }) => {
        try {
          const data = await sdk.tokenCheck(address);
          const forensics = await sdk.forensics(address, chain).catch(() => null);
          return {
            content: [
              {
                type: "text" as const,
                text: JSON.stringify({ ...data, forensics }, null, 2),
              },
            ],
          };
        } catch (err) {
          return {
            content: [
              {
                type: "text" as const,
                text: JSON.stringify({
                  error: err instanceof Error ? err.message : String(err),
                  address,
                  chain,
                }),
              },
            ],
          };
        }
      }
    );
Behavior2/5

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 mentions the types of data returned but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or response format. For a tool querying external data with no annotation coverage, this is a significant gap.

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 a single, efficient sentence that immediately states the tool's purpose and key data inclusions. Every word serves a purpose with no redundant information, making it easy to parse and understand 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 moderate complexity (querying token safety data), lack of annotations, and no output schema, the description is minimally adequate. It specifies what data is returned but doesn't address behavioral aspects or usage context. For a tool with no structured safety or output information, more completeness would be beneficial.

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?

The schema description coverage is 100%, so the schema already fully documents both parameters ('address' and 'chain'). The description doesn't add any parameter-specific information beyond what's in the schema, such as format details for the address or valid chain values. This meets the baseline for high schema coverage.

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 ('Get forensics and safety data') and resource ('for a token contract address'), with explicit details on what data is included ('honeypot detection, rug pull risk, and liquidity analysis'). It distinguishes itself from sibling tools like 'get_agent_reputation' or 'get_agent_trust' by focusing on token contracts rather than agents.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools like 'deep_analysis' or 'get_scarab_balance', leaving the agent to infer usage context solely from the tool name and description.

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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