Skip to main content
Glama

Easyparser — Amazon Product & Seller Data

by com.easyparser

get_bulk_item_result

Read-only

Fetch the parsed result data of a single bulk job item — the actual Amazon data that item produced (product detail, search results, seller profile, etc., depending on the job's operation). This is the same data shown in the item result modal on the Bulk Requests page of the web app.

Use this tool after get_bulk_job_items, when the user wants to see the DATA behind a specific item, not just its status. Typical flow: list_bulk_jobs → get_bulk_job_items → get_bulk_item_result.

IMPORTANT: bulk results are retained for 24 HOURS after the job completes, then they expire. If the item's data has expired, the tool returns a clear message — suggest re-running that item through the matching real-time tool (e.g. get_product_detail for a DETAIL item) to regenerate the data. This tool is free of per-call credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
item_idYesThe item_id of a bulk job item — get it from get_bulk_job_items (each item row has one). This is the same ID the Data Service calls a query ID.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive, and the description adds valuable behavior beyond that: a 24-hour retention window, expiration behavior with a clear fallback message, and zero per-call credits. It also ties the output to the web app's item result modal, which is useful context. There is 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.

Conciseness5/5

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

The description is front-loaded with the core action, then adds usage flow, expiration caveat, and cost information in a logical order. Every sentence carries distinct information with no filler or redundancy.

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

Completeness5/5

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

For a one-parameter read tool with a fully documented schema, the description covers the output shape (depends on operation), usage sequence, lifecycle/expiration behavior, error fallback, and cost. Nothing essential is missing for an agent to select and invoke it correctly.

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 input schema has 100% coverage for the single item_id parameter, including where to obtain it and its query-ID alias. The description reinforces the idea of a specific item but adds no new parameter-level semantics beyond the schema, so it stays at the baseline.

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?

Opens with a specific verb and resource: 'Fetch the parsed result data of a single bulk job item' and clarifies that the returned data depends on the job's operation. It explicitly distinguishes from get_bulk_job_items by contrasting status with the data behind the item. This leaves no ambiguity about what the tool produces.

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?

Explicitly states the sequencing: use after get_bulk_job_items, and only when the user wants the actual data rather than status. It provides a typical flow (list_bulk_jobs → get_bulk_job_items → get_bulk_item_result) and names real-time fallback tools for expired results. This is strong when/alternative guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Easyparser — Amazon Product & Seller Data toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources