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Glama

Find Article Nearby

find_article_nearby

Check live toom stock for one or several articles across markets near a location. Returns nearby markets that have all items and marks a complete pickup option.

Instructions

Where in the radius around a place can an article (or several) be picked up right now?

Checks the real stock of every toom market within radius_km (straight-line, nearest first; at most max_markets markets, one request each) and returns them nearest first with the stock per article. Give one article via sku + quantity, or several via items=[{"sku": "...", "quantity": 2}]. near is understood as in markets_nearby. Markets that have everything are marked complete; nearest_complete is the one to drive to. Stock is live and can change, so call the market before a long drive (the phone number is included). Nothing is reserved or put in a basket.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skuNo
nearNo
itemsNo
quantityNo
radius_kmNo
max_marketsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that stock is live and can change, that markets are queried one request each, that results are returned nearest first, and that nothing is reserved or added to a basket. It stops short of covering auth or rate-limit details.

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 definition is front-loaded with the core question before the operational details, and each sentence carries useful content (scope, input formats, completeness flags, live-stock caveat). It is somewhat wordy but well structured and without filler.

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 six-parameter tool with no annotations, the description covers inputs, nearest-first ordering, completeness flags, and live-stock caveats, and an output schema exists so return format need not be explained. Only auth/rate-limit context is missing.

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

Parameters4/5

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

Despite 0% schema description coverage, the description actually documents all six parameters: `sku`+`quantity` or `items` (with an inline example format), `near`, `radius_km`, and `max_markets`. This adds substantial meaning beyond the bare schema, though units/semantics for radius_km rely on the parameter name.

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 opening question states a specific verb (find/pick up) and resource (an article) with clear scope (markets within a radius right now). It is readily distinguishable from siblings like find_markets or search_products, since the purpose is checking live stock of a specific article at nearby markets.

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

Usage Guidelines3/5

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

The description implies usage context (checking where an article can be picked up right now) and notes `near` is understood as in markets_nearby, but it never states when to use this tool versus alternatives like find_markets or search_products. Usage is implied rather than explicitly routed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.