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retail-order-sync-hub

retail-order-sync-hub

by JulioPradenas
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

_drain_dlq

List or bulk-reset dead letter queue entries to manage failed order synchronization records.

Instructions

List (dry_run=True) or bulk-reset (dry_run=False) all DLQ outbox entries. Scope: dlq.admin. Audited.

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": "_drain_dlq"
  }
}

Input Schema

NameRequiredDescriptionDefault
dry_runNo
api_tokenNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that actions are audited and requires admin scope. However, it does not specify the irreversibility of the bulk-reset action, error behavior, or idempotency, which are important for a potentially destructive operation.

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 concise and front-loads the key behavior. It packs useful information into a single sentence. Could be slightly more structured, but it efficiently conveys purpose and mode.

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

Completeness3/5

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

Given the presence of an output schema, return values are covered. The description includes scope and auditing. However, it lacks details on pagination, failure handling, or consequences of the bulk-reset action, making it somewhat incomplete for the tool's complexity.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain parameters. It explains the dry_run parameter's function but completely omits the api_token parameter. Only half of the parameters receive semantic context.

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 dual purpose: listing (dry_run=True) or bulk-resetting (dry_run=False) all DLQ outbox entries. It identifies the specific resource and verb, distinguishing it from siblings like _find_failed_orders and _get_dlq_depth.

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?

The description provides clear guidance on when to use each mode via the dry_run parameter. It mentions the scope (dlq.admin) indicating access requirements. However, it does not explicitly state when not to use this tool compared to alternatives.

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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