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LamboPoewert

MadeOnSol — Solana memecoin intelligence

madeonsol_token_batch

Read-onlyIdempotent

Bulk lookup of up to 50 Solana token mints in one request. Uses batched database queries and parallel RPC fan-outs to reduce cost by 10-20x compared to sequential calls.

Instructions

Bulk lookup of up to 50 mints in one request. Returns the same per-mint shape as madeonsol_token_get. DB queries batched with IN(...); dex-stream + RPC fan-outs run in parallel. ~10-20× cheaper than N sequential calls — ideal for sniper pipelines scoring many tokens at once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mintsYes1–50 base58 Solana token mints

Implementation Reference

  • src/index.ts:666-674 (registration)
    Registration of the madeonsol_token_batch tool via server.tool(). It accepts an array of 1-50 Solana mint addresses and calls restQuery('POST', '/token/batch', { mints }) to perform a bulk token lookup.
    server.tool(
      "madeonsol_token_batch",
      "Bulk lookup of up to 50 mints in one request. Returns the same per-mint shape as madeonsol_token_get. DB queries batched with IN(...); dex-stream + RPC fan-outs run in parallel. ~10-20× cheaper than N sequential calls — ideal for sniper pipelines scoring many tokens at once.",
      { mints: z.array(z.string()).min(1).max(50).describe("1–50 base58 Solana token mints") },
      { readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true },
      async ({ mints }) => ({
        content: [{ type: "text" as const, text: await restQuery("POST", "/token/batch", { mints }) }],
      })
    );
  • Handler function for madeonsol_token_batch. It receives the mints array and calls the REST API 'POST /token/batch' with those mints, returning the response as text content.
      async ({ mints }) => ({
        content: [{ type: "text" as const, text: await restQuery("POST", "/token/batch", { mints }) }],
      })
    );
  • The restQuery helper function used by madeonsol_token_batch to make authenticated REST API calls to the MadeOnSol API. It sends a fetch request with the method, path, and optional body to BASE_URL/api/v1/{path}.
    async function restQuery(method: string, path: string, body?: unknown): Promise<string> {
      const headers: Record<string, string> = {
        "Content-Type": "application/json",
        ...apiKeyHeaders(),
      };
      const res = await fetch(`${BASE_URL}/api/v1${path}`, {
        method,
        headers,
        ...(body ? { body: JSON.stringify(body) } : {}),
      });
      if (!res.ok) {
        const text = await res.text().catch(() => "");
        return `Error ${res.status}: ${text}`;
      }
      return JSON.stringify(await res.json(), null, 2);
    }
  • Zod schema for the madeonsol_token_batch tool input. Validates that mints is an array of strings with 1 to 50 items.
    { mints: z.array(z.string()).min(1).max(50).describe("1–50 base58 Solana token mints") },
    { readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true },
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds implementation details: DB queries batched as IN(...), dex-stream + RPC fan-outs run in parallel. This reveals performance characteristics without contradicting annotations.

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?

Two sentences, no filler. First sentence states core purpose and output shape. Second sentence provides implementation insight and use case. Information is front-loaded and every sentence earns its place.

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

Completeness5/5

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

No output schema, but description explains return shape (same as token_get). It covers input limits, parallel execution, cost savings, and use case. All necessary context for an agent to use this batch retrieval tool effectively.

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?

Schema coverage is 100%, so the schema fully documents the parameter. The description adds value by clarifying the return shape matches madeonsol_token_get and restating the input range (up to 50 mints), reinforcing schema constraints.

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?

Description clearly states it performs bulk lookup of up to 50 mints, returning per-mint data identical to madeonsol_token_get. It distinguishes itself from the single-lookup sibling by emphasizing batch capability and efficiency.

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

Usage Guidelines5/5

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

Explicitly states when to use (scoring many tokens at once) and why (10-20x cheaper than sequential calls). It provides a concrete use case (sniper pipelines) and implies the alternative (madeonsol_token_get for single mints).

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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