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Glama

Easyparser — Amazon Product & Seller Data

by com.easyparser

list_bulk_jobs

Read-only

List the user's bulk extraction jobs with their progress: operation, domain, status, total/completed/failed/invalid item counts, and timestamps. This reads the SAME data as the Bulk Requests page in the Easyparser web app — so it can see and track jobs the user started from the app UI, not just jobs submitted through this MCP server.

Use this tool when the user asks about their bulk jobs: "is my job done?", "what bulk jobs are running?", "did the DETAIL batch finish?". Each row includes a group_id — pass it to get_bulk_job_items to drill into individual items, or to get_bulk_webhook_logs to check webhook deliveries.

Statuses: pending (queued), processing (running), completed (all items succeeded), partial_success (some items failed or were dropped — check failed_items/invalid_items counts), 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.
statusNoFilter by job status: pending, processing, completed, partial_success (some items failed), failed.
operationNoFilter by operation type, e.g. DETAIL or SEARCH.
sort_directionNoSort by creation time. 'desc' (newest first) is default.desc
bulk_request_idNoLook up a specific job by its bulk_request_id (returned when the job was submitted).

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 readOnlyHint=true and destructiveHint=false, and the description consistently presents a read-only listing operation. It adds meaningful context beyond annotations: visibility into jobs created from the web UI, free credit usage, and detailed meaning of statuses (e.g. partial_success means some items failed or were dropped).

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 purpose, and each subsequent sentence adds distinct value: usage triggers, sibling routing, status semantics, and cost behavior. There is no redundancy or filler.

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 monitoring tool with no output schema, it describes the return row contents, available filters, status meanings, and next-step tool routing. Combined with a fully-documented schema and safety annotations, nothing essential is missing for an agent to select and call it.

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 with explicit descriptions for all seven parameters, so the heavy lifting is done by the schema. The description adds little to parameter semantics, though it explains status enum values more fully and references group_id in the output rows.

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 opening sentence states the exact verb and resource: 'List the user's bulk extraction jobs with their progress' and enumerates the fields returned. It differentiates from siblings by noting it reads the same data as the Bulk Requests web app and by pointing to get_bulk_job_items / get_bulk_webhook_logs for drilling down.

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 explicit user-intent triggers: 'is my job done?', 'what bulk jobs are running?', 'did the DETAIL batch finish?'. It also tells the agent when the tool is not the final step by directing group_id to sibling tools, and adds a cost consideration ('free of per-call credits').

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.

Resources