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Glama

Easyparser — Amazon Product & Seller Data

by com.easyparser

list_operations

Read-only

List all Easyparser operations available in this server, with their parameters, credit costs, and example use cases. Works WITHOUT an API key — call this first to understand what the server can do, or when you are unsure which tool fits the user's question. Also returns the list of supported Amazon marketplace domains.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
operationNoOptional. Get the usage guide for one operation instead of the whole catalog.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond those annotations: it emphasizes that the tool 'Works WITHOUT an API key' and explains what it returns, including marketplace domains. This meaningfully helps an agent understand the tool's behavior and requirements.

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?

Two sentences deliver all essential information with no filler. The primary function is front-loaded, followed by usage guidance and return value highlights. Every clause earns its place.

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 low-complexity discovery tool with one optional parameter and strong read-only annotations, the description is fully sufficient. It explains what is listed, the auth requirement, when to call it, and additional return content. No critical gaps remain even without an output schema.

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 coverage is 100% for the single optional parameter, and the schema already provides a clear description and enum values. The tool description does not add parameter-specific detail, but it does not need to because the schema is sufficient. Baseline 3 applies.

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 identifies the tool as a catalog/discovery operation: 'List all Easyparser operations available in this server, with their parameters, credit costs, and example use cases.' It is unambiguous and distinct from the sibling tools, which are the actual operations being listed.

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 gives explicit usage context: 'call this first to understand what the server can do, or when you are unsure which tool fits the user's question.' It could be stronger by explicitly stating when not to use it or naming alternatives, but the intended entry-point role is clear.

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