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Label Dataset Statistics

label_stats

Get statistics for the address label dataset: total labeled addresses, categories covered, last updated timestamp. Free endpoint.

Instructions

Get statistics about the address label dataset: total labeled addresses, categories covered, last updated timestamp. Free endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual tool handler for 'label_stats'. Calls apiGet to /api/v1/labels/stats and returns the stats as JSON text content.
    server.registerTool(
      "label_stats",
      {
        title: "Label Dataset Statistics",
        description:
          "Get statistics about the address label dataset: total labeled addresses, " +
          "categories covered, last updated timestamp. Free endpoint.",
        inputSchema: {},
      },
      async () => {
        const res = await apiGet<LabelStatsResponse>("/api/v1/labels/stats");
    
        if (!res.ok) {
          return {
            content: [
              {
                type: "text" as const,
                text: `API error (${res.status}): ${JSON.stringify(res.data)}`,
              },
            ],
            isError: true,
          };
        }
    
        return {
          content: [
            { type: "text" as const, text: JSON.stringify(res.data, null, 2) },
          ],
        };
      },
    );
  • TypeScript interface LabelStatsResponse defining the shape of the API response (dataset, source, update_frequency, stats).
    interface LabelStatsResponse {
      dataset: string;
      source: string;
      update_frequency: string;
      stats: Record<string, unknown>;
    }
  • The inputSchema for 'label_stats' is an empty object (zod validation), meaning no parameters required.
    server.registerTool(
      "label_stats",
      {
        title: "Label Dataset Statistics",
        description:
          "Get statistics about the address label dataset: total labeled addresses, " +
          "categories covered, last updated timestamp. Free endpoint.",
        inputSchema: {},
      },
      async () => {
        const res = await apiGet<LabelStatsResponse>("/api/v1/labels/stats");
    
        if (!res.ok) {
          return {
            content: [
              {
                type: "text" as const,
                text: `API error (${res.status}): ${JSON.stringify(res.data)}`,
              },
            ],
            isError: true,
          };
        }
    
        return {
          content: [
            { type: "text" as const, text: JSON.stringify(res.data, null, 2) },
          ],
        };
      },
    );
  • Registration of 'label_stats' tool via server.registerTool with title, description, empty inputSchema.
    server.registerTool(
      "label_stats",
      {
        title: "Label Dataset Statistics",
        description:
          "Get statistics about the address label dataset: total labeled addresses, " +
          "categories covered, last updated timestamp. Free endpoint.",
        inputSchema: {},
      },
  • src/index.ts:23-23 (registration)
    Import of registerLabelTools from ./tools/labels.js in the main entry point.
    import { registerLabelTools } from "./tools/labels.js";
  • src/index.ts:50-50 (registration)
    Invocation of registerLabelTools(server) which registers all label tools including 'label_stats'.
    registerLabelTools(server);
Behavior3/5

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 mentions the tool is free and returns statistics, but does not disclose potential behaviors like rate limits, response size, or whether the data is cached. This is minimal transparency for a simple read operation.

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, well-structured sentence that concisely conveys the tool's purpose and output. No superfluous information.

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 parameterless stats endpoint, the description covers the key return values. However, it omits any mention of potential limitations (e.g., data staleness, max results) or output format, which could be useful for completeness.

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

Parameters4/5

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

The input schema has no parameters, so schema coverage is 100%. The description adds value by enumerating the specific statistics returned (total labeled addresses, categories, timestamp), which goes beyond the empty schema.

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 gets statistics for the address label dataset and lists specific metrics (total labeled addresses, categories covered, last updated timestamp). This differentiates it from sibling stat tools like contract_stats or dex_stats which operate on different datasets.

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 what the tool does and notes it is a 'free endpoint', giving insight into usage. However, it lacks explicit guidance on when to use this tool versus alternatives, though the specificity of the dataset makes it straightforward.

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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