Easyparser — Amazon Product & Seller Data

get_bulk_webhook_logs

Read-only

Check webhook delivery logs for bulk jobs: whether the completion notification was delivered to the user's callback_url, delivery status, and timestamps. Use this when a job shows as completed but the user's system never received the webhook — the classic "job finished but my integration didn't fire" debugging case.

Scope to one job with group_id (from list_bulk_jobs), or omit it to scan recent deliveries across all jobs. This tool is free of per-call credits.

Input Schema

Name	Required	Description	Default
`page`	No	Page number. Default 1.
`limit`	No	Items per page (max 1000). Default 100.
`status`	No	Filter by delivery status (e.g. 'success', 'failed').
`date_to`	No	ISO date upper bound.
`group_id`	No	Scope logs to one job's group_id. Omit to see recent webhook deliveries across all jobs.
`date_from`	No	ISO date lower bound.
`request_id`	No	Filter by an individual request ID.
`sort_direction`	No	Sort by creation time. 'desc' (newest first) is default.	desc
`bulk_request_id`	No	Filter by the job's bulk_request_id.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 11, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "description": "JSON response from the Easyparser Data Service or Account API. Non-object payloads are wrapped as { result }.",
+  "properties": {},
+  "type": "object"
+}

First observedSep 6, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful context not in the annotations: it is a read-only diagnostic over delivery records and is free of per-call credits, which matters for a log-scanning tool.

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?

Front-loaded with purpose, then the trigger case, then scoping and cost. Two tight paragraphs with no filler; the colloquial 'job finished but my integration didn't fire' phrasing is slightly colorful but aids recognition of the debugging scenario.

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 so return values need no explanation, and annotations carry the safety profile. Trigger, scoping, data source, and cost are all covered, leaving only minor gaps like pagination expectations for a 9-parameter list tool.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds cross-tool provenance the schema lacks — that group_id comes from list_bulk_jobs and that omitting it broadens the scan to all jobs. That provenance/routing detail is real value beyond the schema's per-parameter text.

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 ('Check webhook delivery logs for bulk jobs') and enumerates the payload the agent will get back (delivery status, timestamps, whether the callback fired). The diagnostic framing plus the group_id/bulk_request_id filters distinguish it from siblings like get_error_logs and list_bulk_jobs.

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

Usage Guidelines5/5

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

Gives an explicit trigger scenario ('when a job shows as completed but the user's system never received the webhook') and concrete scoping guidance: use group_id for one job (sourced from list_bulk_jobs) or omit it to scan all jobs. This routes the agent without inference, though it stops short of naming an explicit when-not case.

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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