German grocery and drug store real time prices and offers including optimal route planing
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Average 4.5/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possible confusion between tools.
With a single tool, naming consistency is trivially maintained.
One tool for a shopping assistant is minimal; while the tool is comprehensive, it might benefit from separating concerns like store discovery or offer browsing.
The tool covers the core planning task but lacks supporting tools for independent offer searches or list management, leaving minor gaps.
plan_optimal_shopping_routePlan optimal grocery and drug store shopping tripAPlans the optimal shopping trip for a given list and starting location in Germany. Answers 'where should I go to buy this list, and is the trip worth it?' — not 'what's on offer near me'. Matches each item on the list to the best current offer across German supermarkets and drug stores (REWE, Aldi, Lidl, Penny, Netto, Norma, Edeka, DM, Rossmann, Mueller), then computes the cheapest realistic route by weighing product prices against travel distance and shopping time. Returns the chosen store(s), the per-item picks with live prices, the trip's savings and a worth-it verdict, and a shareable interactive grocery list. Supports car, bicycle, and pedestrian travel modes. For corridor trips (A-to-B), supply 'end_location' to route stores along the way.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | The shopping list in natural language (e.g. '3x milk, eggs, bread') | |
| km_cost | No | Travel cost penalty per kilometer (forced to 0.0 for bicycle/pedestrian) | |
| location | Yes | Starting location (ZIP code, city, or address in Germany) | |
| hour_cost | No | Time cost penalty in EUR per hour (defaults to 12.0) | |
| max_stores | No | Maximum number of store stops to allow in the route (default: 100) | |
| travel_mode | No | Travel mode to use | car |
| end_location | No | Optional destination location if not a round trip | |
| max_radius_km | No | Maximum search radius in kilometers (defaults: car=15km, bicycle=5km, pedestrian=2km) | |
| shopping_time_per_store | No | Base shopping minutes spent per store (defaults to 10) |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds valuable behavioral context: it matches items to offers, weighs product prices against travel distance and time, returns a worth-it verdict, and supports multiple travel modes. This goes beyond the structured annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact but information-dense, with four sentences of focused content. It front-loads the primary purpose, then efficiently covers scope, algorithm, outputs, travel modes, and the end_location case—no filler or redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the tool's complexity (9 parameters, multiple outputs, several travel modes), the description covers the algorithm, return values, supported stores, and an important edge case (corridor trips). Having an output schema also reduces the need to document return types in prose, so the level of detail is appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover all 9 parameters, so the baseline is 3. The description adds meaningful semantics by explaining the optimization logic (prices vs. distance/time), which illuminates km_cost, hour_cost, and shopping_time_per_store. It also explicitly explains when to use end_location for corridor trips.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource ('Plans the optimal shopping trip') with a clear geographic scope (Germany) and explicitly distinguishes itself from 'what's on offer near me', making the tool's core purpose unambiguous and well-differentiated from a nearby-offers lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly identifies the intended use case (planning a shopping trip for a given list) and explicitly excludes the 'what's on offer near me' scenario, providing a clear when-to-use boundary. It also gives guidance for corridor trips via end_location, though it doesn't name alternative tools since none exist.
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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