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List my lists

list_my_wishlists
Read-onlyIdempotent

Returns the lists on the signed-in shopper's Plendio account: wishlists, shopping lists and favourites they own, and lists other people have shared with them. Each has its list_id, title, kind, the shopper's role on it (owner, editor or viewer), item count, who it is for and the event date when set, and its links. Requires sign-in.

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": "list_my_wishlists"
  }
}

Input Schema

NameRequiredDescriptionDefault
marketNoMarket for the links in the answer: dk, se, de or no. Default: the market of the site you connected to.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavior: an explicit 'Requires sign-in' auth precondition and a full enumeration of returned fields (list_id, title, kind, role, item count, recipient, event date, links), which matters because there is no output schema.

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?

Two sentences, front-loaded with the scope statement, and every clause earns its place — the second sentence substitutes for a missing output schema. It is dense and slightly run-on in the return-field enumeration, but there is no 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 no-output-schema read tool it covers the essentials: what is returned, per-list attributes, and the sign-in requirement. Gaps are minor — no mention of ordering, pagination or size limits on the result set, which an agent may want before calling on a large account.

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?

The single `market` parameter has 100% schema description coverage including its enum and default, so the schema carries the meaning. The description does not mention `market` at all and adds nothing about how it affects the returned links; per the high-coverage baseline, 3 is the ceiling.

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?

States a specific verb and resource ('Returns the lists on the signed-in shopper's Plendio account') and then disambiguates the resource by enumerating the three kinds (wishlists, shopping lists, favourites) plus the owned-vs-shared distinction. That scope definition lets an agent separate it from singular siblings like get_wishlist or get_my_wishlist without opening a schema.

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?

Usage is implied by the scope: call this when you need an overview of every list the shopper can see. It never names an alternative (get_wishlist, get_my_wishlist, list_shares) or states when not to use it, so routing between the several list-related siblings is left to inference.

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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