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Easyparser — Amazon Product & Seller Data

get_error_logs

Read-only

List the user's API request error logs — the same data shown on the Errors page of the Easyparser web app. Covers both real-time API errors and bulk job errors: each row has the platform, operation, domain, error code, channel (BULK/REALTIME), the request parameters that caused it, and a timestamp.

Use this tool when the user asks about failures: "why are my requests failing?", "show me recent errors", "any webhook errors today?", "what went wrong with my DETAIL calls?". Filter by error_channel (BULK or REALTIME), error_code, operation, or domain to narrow down. Each row includes a request_id and the query_params that triggered the error, so you can trace exactly which input failed.

This tool is free of per-call credits (monitoring endpoints do not consume credits).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number. Default 1.
limitNoItems per page (max 1000). Default 100.
domainNoFilter by marketplace domain.
date_toNoISO date upper bound.
order_byNoSort field. Default 'create_date'.create_date
platformNoFilter by platform (e.g. 'AMZ').
date_fromNoISO date lower bound, e.g. 2026-08-01.
operationNoFilter by operation type, e.g. DETAIL or SEARCH.
error_codeNoFilter by a specific error code, e.g. 'WEBHOOK_ERROR_404' or 'SOMETHING_WENT_WRONG'.
order_typeNoSort direction. 'desc' (newest first) is default.desc
error_channelNoFilter by channel: BULK (bulk job errors) or REALTIME (real-time API errors). Omit for both.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already mark it read-only and non-destructive. The description adds extra operational context: it's a monitoring endpoint that does not consume credits, it covers both BULK and REALTIME channels, and each row exposes requst_id/query_params to trace failures. No contradiction with annotations.

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 front-loaded paragraphs: purpose, usage triggers, filter guidance, and a credit-cost note. It is information-dense and mostly efficient, though there is minor redundancy between 'request parameters that caused it' and later 'request_id and query_params'.

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?

With no output schema, the description compensates by enumerating the row fields, covering both error channels, naming the comparable UI page, and noting monitor-no-credit behavior. It doesn't need to restate page/limit defaults since the schema already covers them. A few additional examples of returned structure could nudge it to 5, but it is well within complete range.

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%, so the 11 parameters are already well documented. The description reinforces relevant filters (error_channel, error_code, operation, domain) and explains why they are useful, but it doesn't add semantic details beyond what the schema provides. 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?

The description opens with a specific verb and resource ('List the user's API request error logs') and immediately scopes what it covers: real-time API errors and bulk job errors, with a precise list of row fields. This clearly distinguishes it from sibling tools that fetch product data, job items, or webhook logs.

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?

It explicitly lists user intent phrases that should lead to this tool, such as 'why are my requests failing?' and 'show me recent errors'. It doesn't name alternatives or state when not to use it, but the context is clear enough for an agent to route correctly.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct resource or action, and overlapping data is carefully disambiguated in the descriptions—e.g., get_product_detail includes BSR and dimensions, but get_bestseller_rank and get_package_dimensions are explicitly positioned as narrower alternatives. The bulk-job tools also form a clear pipeline with no realistic confusion between listing jobs, inspecting items, fetching item data, and checking webhook logs.

Naming Consistency4/5

The dominant get_* pattern is consistent for data retrieval, and list_* is used for collection-style endpoints. Minor deviations like check_credits, lookup_product, and search_products are understandable but break the strict verb_noun consistency enough to prevent a perfect score.

Tool Count4/5

At 17 tools, the server is slightly above the ideal 3-15 range, but the count is justified by the breadth of the domain: product details, offers, sales history, seller intelligence, bulk job monitoring, account credits, and error logs. Each tool earns its place, and the heavier count does not feel bloated.

Completeness4/5

Real-time product and seller data coverage is strong, including search, barcode lookup, product detail, offers, BSR, dimensions, sales history, seller profile, seller products, and seller feedback. The main gap is that bulk jobs can be listed and inspected but there is no tool to create or submit a new bulk job from the MCP server, leaving that workflow incomplete.