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ilyautov

marketplaces-mcp-ru

ym_fetch_all

Read-only

Auto-paginate any read endpoint and collect every record in one JSON response. Handles offset, last_id, cursor, and page parameter styles to bypass manual pagination.

Instructions

Auto-paginate a read endpoint and return every row in one response.

Handles offset, last_id, cursor (Ozon v4/v5), page and WB lastChangeDate styles. The array path is taken from the catalog automatically.

Args: operation_id: a read endpoint from the catalog. query / body / path_values: base parameters (cursor fields are managed). items_path: override the array path (default: the endpoint's own). limit: page size to request. max_items: hard cap to protect context (default 10000). Returns JSON: {"ok", "items", "total_fetched", "pages_fetched", "truncated"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNo
limitNo
queryNo
max_itemsNo
items_pathNo
path_valuesNo
operation_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.2

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, and the description adds substantial behavioral detail: it handles multiple pagination styles, manages cursor fields, applies a max_items cap to protect context, and returns a structured summary with truncation status. This goes well beyond what annotations reveal.

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?

Every sentence earns its place: the main behavior is front-loaded, pagination styles are summarized compactly, and the Args section is a clean scannable list. No filler or redundant restatement.

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 7-parameter tool with no enums and an output schema present, the description is complete: what it does, how pagination is handled, how parameters interact, the safety cap, and the return shape are all covered. Nothing an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates by explaining every parameter: operation_id is a read endpoint from the catalog, query/body/path_values are base parameters with cursor fields managed, items_path overrides the array path, limit controls page size, and max_items is a hard cap. This adds real meaning beyond the bare schema.

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: 'Auto-paginate a read endpoint and return every row in one response.' It clearly distinguishes this from single-call tools like ym_call_method by emphasizing pagination and bulk return.

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 clearly states the use case: any read endpoint from the catalog where all rows are needed, with cursor fields automatically managed. It does not name alternatives or exclusions explicitly, but the context is strong enough that an agent can infer when to choose it.

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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