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line_time_forecast

Predict Chipotle line wait times using machine learning algorithms based on day of week, time of day, and payday status.

Instructions

ML-powered burrito congestion prediction. Uses advanced algorithms to estimate Chipotle line wait times.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
day_of_weekNoDay of the weekfriday
time_of_dayNoGeneral time of daylunch
is_paydayNoIs it payday?

Implementation Reference

  • Implementation of the 'line_time_forecast' tool which predicts Chipotle line wait times based on day, time, and payday status using a simulated algorithm.
    server.tool(
      "line_time_forecast",
      "ML-powered burrito congestion prediction. Uses advanced algorithms to estimate Chipotle line wait times.",
      {
        day_of_week: z
          .enum(["monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday"])
          .default("friday")
          .describe("Day of the week"),
        time_of_day: z
          .enum(["breakfast", "lunch", "afternoon", "dinner", "late_night"])
          .default("lunch")
          .describe("General time of day"),
        is_payday: z.boolean().default(false).describe("Is it payday?"),
      },
      async ({ day_of_week, time_of_day, is_payday }) => {
        // Peak Burrito Pressure Algorithm
        const dayMultiplier = {
          monday: 0.7, tuesday: 0.6, wednesday: 0.8, thursday: 0.9,
          friday: 1.4, saturday: 1.3, sunday: 1.1,
        }[day_of_week];
    
        const timeMultiplier = {
          breakfast: 0.3, lunch: 1.5, afternoon: 0.7, dinner: 1.3, late_night: 0.5,
        }[time_of_day];
    
        const paydayBonus = is_payday ? 1.4 : 1.0;
        const randomChaos = 0.8 + Math.random() * 0.4;
    
        const baseLine = 12;
        const estimatedWait = Math.round(baseLine * dayMultiplier * timeMultiplier * paydayBonus * randomChaos);
    
        let pressure, recommendation;
        if (estimatedWait > 25) {
          pressure = "EXTREME";
          recommendation = "mobile_order";
        } else if (estimatedWait > 15) {
          pressure = "HIGH";
          recommendation = "order_ahead";
        } else if (estimatedWait > 8) {
          pressure = "MODERATE";
          recommendation = "walk_in_acceptable";
        } else {
          pressure = "LOW";
          recommendation = "walk_right_in";
        }
    
        const lines = [
          "# Line Time Forecast",
          "",
          "```",
          "  Burrito Congestion Prediction Engine",
          "  =====================================",
          `  Day:                  ${day_of_week}`,
          `  Time:                 ${time_of_day}`,
          `  Payday:               ${is_payday ? "YES (God help us)" : "No"}`,
          `  Estimated Wait:       ~${estimatedWait} minutes`,
          `  Peak Burrito Pressure: ${pressure}`,
          `  Confidence:           ${(60 + Math.random() * 30).toFixed(1)}%`,
          "```",
          "",
          `**Recommendation:** ${recommendation.replace(/_/g, " ")}`,
          "",
          ...(is_payday ? ["> Payday detected. Expect a 40% surge in guacamole orders."] : []),
          ...(pressure === "EXTREME"
            ? ["> WARNING: Line extends past the door. People are questioning their life choices."]
            : []),
          "",
          `> ${getRandomQuip()}`,
        ];
    
        return { content: [{ type: "text", text: lines.join("\n") }] };
      }
    );
Behavior2/5

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

No annotations provided, so description carries full burden. Mentions 'ML-powered' but fails to disclose critical behavioral traits: return format (minutes? confidence intervals?), location scope (generic vs specific store), prediction reliability, or data staleness. No mention of rate limits or calculation latency.

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?

Two efficient sentences with purpose front-loaded. Minor deduct for 'advanced algorithms' which adds no specific value. Structure is appropriate for the tool complexity.

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?

Adequate for a 3-parameter tool with complete schema coverage, but lacks output description critical for a forecasting tool (what units? range?). Missing location context is notable given the domain.

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?

Schema coverage is 100% with clear descriptions for all 3 parameters. Description adds 'burrito congestion' metaphor but provides no additional semantic guidance (e.g., explaining 'is_payday' logic or default rationales) beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb+resource ('estimate... wait times') and distinguishes from siblings (ordering/nutrition tools). 'Burrito congestion' is colloquial but meaning is decipherable. Lacks explicit 'predicts' framing that would clarify it's a forecast vs real-time check.

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?

No explicit guidance on when to use vs alternatives (e.g., should this be called before place_order?). No mention that all parameters are optional with sensible defaults, which affects invocation strategy.

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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