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delete_listing

DestructiveIdempotent

Delete (soft-delete) one of the USER'S OWN listings by listing_id. Only the owner can delete. No credit is refunded — the listing was already published.

Input Schema

NameRequiredDescriptionDefault
listing_idYesListing id, taken from the `id` field of search_listings, semantic_search or list_my_listings results. Example: "adzuna-us-5947308680".

Output Schema

NameRequiredDescriptionDefault
successYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. Changed1 schema field changed
    • addedInput schema / properties / listing_id / description
      Added value: +"Listing id, taken from the `id` field of search_listings, semantic_search or list_my_listings results. Example: \"adzuna-us-5947308680\"."
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "http://json-schema.org/draft-07/schema#",
      +  "additionalProperties": {},
      +  "properties": {
      +    "success": {
      +      "type": "boolean"
      +    }
      +  },
      +  "required": [
      +    "success"
      +  ],
      +  "type": "object"
      +}
  4. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and idempotentHint=true, so the safety profile is known. The description adds genuinely new context beyond annotations: that deletion is a soft-delete and that no credit is refunded because the listing was already published — both material to a mutation decision.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with the action and ownership constraint, followed by the cost implication. Zero 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?

An output schema exists and annotations cover safety, so the description doesn't need to explain returns. It covers ownership, soft-delete semantics, and credit behavior; only the downstream effect on related conversations/messages is unstated, a minor gap.

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 100%, and the listing_id description even explains where to source the value with an example. The description adds only the phrase 'by listing_id', so baseline 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?

States a specific verb (soft-delete), resource (listing), and scope (USER'S OWN listings, by listing_id). This distinguishes it clearly from create_listing/update_listing/renew_listing siblings without needing to open any schema.

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?

Explicitly states the precondition that only the owner can delete and that no credit is refunded, which tells the agent when this call is valid and what it costs. It stops short of naming alternatives (e.g. set_listing_status for deactivation vs. true removal), leaving that routing 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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