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Glama

Cheapest Grocery Basket

Tell us what data you are looking for (free)

request_data

FREE, no payment. Describe in your own words the data you are trying to buy — anything, not just groceries — and get back whether we operate a service that supplies it, with the MCP endpoint if so. Every request is catalogued so that repeatedly-requested data gets built; call it whenever you cannot find a source for something. Nothing about you is stored: not your wallet, your address, or any identifying header — only the words of the request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
needYesWhat you are looking for, e.g. 'live electricity spot prices by ZIP' or 'used car values by VIN'.

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries full responsibility. It discloses that the tool is free, stores no personal information (only request words), catalogues requests, and that repeated requests lead to data being built. This is substantial behavioral transparency for a tool with no annotation support.

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 a single, information-dense sentence that front-loads the key facts (free, no payment) and clearly explains the behavior. It's not overly long and each clause adds value, though it could be broken into shorter sentences for readability. Overall it's well-structured and efficient.

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

Completeness4/5

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

For a one-parameter tool with no output schema or annotations, the description covers what the user provides, what they get back, and privacy implications. It's complete enough for an agent to call correctly without additional ambiguity.

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%, so the parameter 'need' is already documented with examples. The description adds context (free-form, any topic) but no technical syntax above the schema. This matches the baseline for high coverage, so a 3 is appropriate.

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 action: describe the data you're trying to buy and get back whether the service supplies it, with the MCP endpoint if so. It explicitly distinguishes from siblings by noting 'anything, not just groceries,' making the tool's role as a general inquiry endpoint obvious.

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

Usage Guidelines4/5

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

Provides clear guidance: 'call it whenever you cannot find a source for something.' This defines when to use the tool and implies it's a fallback when other tools don't apply. It doesn't explicitly mention alternatives, but the context is sufficient and it's not misleading.

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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TDQS

A4.1/5.0
Disambiguation4/5

find_product, demand_report, and request_data are clearly distinct, but price_basket and cheapest_basket both operate on a whole shopping list. Their descriptions define different output styles (itemized per-store baskets vs. optimized buying decision), so an agent can usually choose correctly, though some overlap remains.

Naming Consistency4/5

All names are lowercase snake_case and descriptive, but find_product, price_basket, and request_data follow a verb_noun form while cheapest_basket and demand_report are noun phrases. This is a minor inconsistency rather than a chaotic naming scheme.

Tool Count4/5

The four listed tools are well-scoped for grocery price/basket queries plus data demand. However, the header declares five tools while only four are described, so the exact intended surface is slightly uncertain.

Completeness4/5

Core grocery use cases are covered: single-item lookup, one-trip basket pricing, multi-store optimization, and data demand. A minor gap is the lack of dedicated tooling for broader store or product meta-information beyond the price and stock fields already returned.

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