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A2A Retailmedia — the agentic retail-media hub for grocery

by io.github.greencore-solutions
OverviewDiscussionsAdmin

Find Makers

find_makers
Read-onlyIdempotent

Find the brand: makers on the grocery record of one market (node = one of the 20 markets). segment is taken as a lane where it names one (organic, free-from, plant-based, artisan, origin, premium). Read live from the A2A Grocery door.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_makers"
  }
}

Input Schema

NameRequiredDescriptionDefault
nodeNo
limitNo
regionNo
segmentNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • removedOutput schema / additionalProperties
      Removed value: -true
  2. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world, covering the safety profile. The description adds one piece of context beyond that — data is read live from the A2A Grocery door (not cached) — but says nothing about the return shape or any access constraints. Annotations carry most of the burden, so a 3 is appropriate.

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?

Three tight sentences with no padding, and the core action is front-loaded before the node and segment qualifiers. The phrasing is terse to the point of being cryptic ('lane where it names one'), but nothing is wasted.

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

Completeness2/5

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

For a 4-parameter tool with 0% schema coverage, two parameters (limit, region) are explained nowhere, and there is no usage or filtering guidance. The output schema covers return values and annotations cover safety, but the input-side gaps leave the description incomplete for correct invocation.

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 description coverage is 0%, so the description must compensate. It usefully explains node (one of 20 markets) and enumerates segment values (organic, free-from, plant-based, artisan, origin, premium) that the schema does not, but it never mentions limit or region, leaving half the parameters undocumented anywhere.

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?

States a specific verb and resource: finding brand makers on the grocery records of one market. The parenthetical 'node = one of the 20 markets' clarifies scope, and 'makers' implicitly contrasts with the sibling find_buyer. Slightly jargon-heavy wording ('brand: makers', 'grocery record') keeps it from a clean 5.

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 when-to-use or when-not guidance, and no routing to alternatives such as find_buyer or the enrol/resolve siblings. The segment sentence is parameter semantics rather than usage context, so the agent is left to infer when this is the right tool.

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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