agent-toolbelt

Instructions

Implementation Reference

• mcp-server/src/index.ts:405-444 (handler)

  The "count_tokens" tool is registered here. It uses the helper function `callToolApi` to forward the request to the `token-counter` endpoint of the Agent Toolbelt API.

  // ----- Tool: Token Counter -----
  server.registerTool(
    "count_tokens",
    {
      title: "Token Counter",
      description:
        "Count tokens for any text across multiple LLM models and get per-model cost estimates. " +
        "Use before sending text to an LLM to check context window usage or compare costs across models. " +
        "Supports GPT-4o, GPT-4, GPT-3.5-turbo, Claude 3.5 Sonnet, Claude 3 Opus, and 10+ more.",
      inputSchema: {
        text: z.string().describe("The text to count tokens for"),
        models: z
          .array(z.string())
          .default(["gpt-4o", "claude-3-5-sonnet"])
          .describe("Models to count tokens for"),
      },
    },
    async ({ text, models }) => {
      const result = await callToolApi("token-counter", { text, models });
      const data = result as any;
      const r = data.result;
  
      const lines = [
        `**Characters:** ${r.characterCount.toLocaleString()}`,
        `**Words:** ${r.wordCount.toLocaleString()}`,
        "",
        "**Token counts:**",
      ];
  
      for (const [model, info] of Object.entries(r.results) as any) {
        const approx = info.approximate ? " (approx)" : "";
        const cost = info.estimatedCost
          ? ` | input ~$${info.estimatedCost.input} / output ~$${info.estimatedCost.output}`
          : "";
        lines.push(`  ${model}: **${info.tokens.toLocaleString()} tokens**${approx}${cost}`);
      }
  
      return { content: [{ type: "text" as const, text: lines.join("\n") }] };
    }
  );