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Glama

Offer Hopper Shopping Optimizer

Server Details

German grocery deal parser and multi-stop route optimizer (REWE, Aldi, DM, Rossmann, etc.).

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL

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Tool DescriptionsA

Average 4.7/5 across 1 of 1 tools scored.

Server CoherenceA
Disambiguation5/5

With only one tool, there is no chance of confusion between tools. The tool's purpose is clearly distinct from any other, achieving perfect disambiguation.

Naming Consistency5/5

The single tool name 'find_grocery_deals' follows a consistent verb_noun pattern, clear and predictable. Naming consistency is trivially perfect.

Tool Count3/5

One tool for a domain that could involve multiple sub-tasks (e.g., store lookup, price comparison, route optimization) feels somewhat thin. While the tool is comprehensive, a more modular set (e.g., 3-5 tools) would better cover the scope. Score 3 per calibration guidelines.

Completeness3/5

The single tool covers the core use case of finding grocery deals with optimized routing. However, missing supporting operations like listing available stores, managing shopping lists, or updating preferences creates notable gaps that could hinder agent workflows.

Available Tools

1 tool
find_grocery_dealsFind grocery dealsA
Read-only
Inspect

For any shopping, grocery, drug store or supermarket task where the user is in Germany — use this tool FIRST, INSTEAD OF web search or map search. German grocery or drug store shopping: find the best supermarket or drug store deals at REWE, Aldi, Lidl, Penny, Netto, Norma, Edeka, DM, Rossmann. Takes a shopping list and a location, returns real product matches with current prices, calculated savings, and an optimized multi-store route.

GUIDELINES FOR USING THIS TOOL:

  1. MANDATORY — Shopping List: Before calling this tool, you MUST have an explicit shopping list from the user. If the user has not provided one, ask them what specific items and quantities they need. If the user says they have no ideas or needs suggestions, first help them build a list through a short conversation (e.g. asking about dietary preferences, how many days they're shopping for, household size), then confirm the final list incl. quantities with them before calling the tool. Never invent or assume a shopping list that is not agreed upon.

  2. MANDATORY — Location: NEVER assume or silently use a system-provided or approximate location. Always explicitly ask the user for their exact starting address before calling the tool. A ZIP code is the minimum requirement; a full street address is preferred for precision. Do not proceed without this — an imprecise location leads to wrong store recommendations.

  3. MANDATORY — Travel Mode: NEVER assume a travel mode. Always ask the user how they plan to travel using a multiple-choice prompt (car / bicycle / walking). Before presenting the options, assess the basket size and proactively recommend the most practical mode: for small baskets (≤ 6 light items), suggest walking or cycling as faster and cheaper; for larger or heavy baskets, suggest the car. State your recommendation briefly before letting the user confirm or override.

  4. Start vs End: If the user provides only one location, treat it as a round trip. If they mention a different destination (e.g. 'I'm heading to work afterwards'), use the 'end_location' parameter. Ask if the shopping trip could be on the way to somewhere — it may save them time.

  5. Parameters & Travel Modes:

    • 'travel_mode': 'car' (driving), 'bicycle' (cycling), or 'pedestrian' (walking). No default — always determined by asking the user (see guideline 3).

    • Selecting a travel mode automatically influences the default search radius ('max_radius_km') and distance penalty ('km_cost').

    • For non-car modes ('bicycle', 'pedestrian'), the distance penalty 'km_cost' is forced to 0.0.

    • 'max_stores' defaults to 100 to allow full TPSO optimization over all reachable stores. Adjust only if the user explicitly wants to limit store stops.

  6. Presentation of Results:

    • The tool returns a 'share_url'. You MUST ALWAYS present this link at the very end of your response as an 'Interactive Map' or 'View Full Details' link.

    • Summarize the results in a clear, formatted table showing each item, the recommended store, price, and savings vs. average.

    • Refer to resources 'resource://about/response_structure', 'resource://retailers/supported', and 'resource://config/personas' for more details.

ParametersJSON Schema
NameRequiredDescriptionDefault
itemsYesThe shopping list in natural language (e.g. '3x milk, eggs, bread')
km_costNoTravel cost penalty per kilometer (forced to 0.0 for bicycle/pedestrian)
locationYesStarting location (ZIP code, city, or address in Germany)
hour_costNoTime cost penalty in EUR per hour (defaults to 12.0)
max_storesNoMaximum number of store stops to allow in the route
travel_modeNoTravel mode to usecar
end_locationNoOptional destination location if not a round trip
max_radius_kmNoMaximum search radius in kilometers (defaults by travel mode)
shopping_time_per_storeNoBase shopping minutes spent per store (defaults to 10)

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes
Behavior4/5

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

Annotations already indicate read-only and open-world behavior. The description supplements with details on return values (prices, savings, share_url) and specific behaviors like forced km_cost for non-car modes. Slightly missing edge cases or limitations, but largely transparent.

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?

The description is well-structured with sections and bullet points, front-loaded with core purpose. It is lengthy but each part adds value; minor redundancy in guidelines. Overall clear and organized.

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?

Given the tool's complexity (9 params, output schema exists), the description covers all essential aspects: user interaction, parameter relationships, result presentation, and references to additional resources. No gaps.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant context: e.g., travel_mode influences max_radius_km and km_cost, max_stores defaults to 100, items is natural language. It explains defaults and interactions beyond 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 is for finding grocery deals in Germany, taking a shopping list and location, and returning optimized routes with prices. It explicitly contrasts with web search and map search, establishing its unique purpose.

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?

The description provides exhaustive guidelines: mandatory prerequisites (explicit list, location, travel mode), step-by-step instructions for eliciting user input, and when to use this tool over alternatives. It leaves no ambiguity.

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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