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crpt_fetch_all

Read-only

Fetch every row from a Chestny ZNAK read endpoint by automatically paginating offset, cursor, page, or lastChangeDate responses into one JSON result.

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. First observedv0.2.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already cover readOnly and openWorld. The description adds genuine beyond-annotation detail: it auto-handles multiple pagination styles (offset, last_id, cursor, page, WB lastChangeDate), manages cursor fields, and applies a context-protecting max_items cap. That said, it omits auth prerequisites and rate-limit behavior for a potentially heavy paging loop.

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?

Front-loaded purpose sentence, then a compact helper paragraph and a structured Args/Returns block. Every section earns its place, though the Args list partially restates schema-defined parameter names.

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 an output schema present, the description appropriately names the return fields rather than describing them fully. Given openWorld/readOnly annotations and a non-trivial 7-param tool, the description is largely complete, missing only auth/rate context.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must carry the load, and it does describe each parameter's role (operation_id, query/body/path_values, items_path override, limit page size, max_items cap). A few params (limit, max_items) still lack format/constraint detail and the override semantics of items_path could be clearer.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: auto-paginate a read endpoint and return every row. Distinguishes from siblings like crpt_call_method (single call) implicitly by the pagination-aggregation behavior. However, it never explicitly names an alternative tool or contrast, so sibling differentiation is only implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (fetching all rows when you need the full dataset across pages), but gives no explicit when-to-use vs crpt_call_method or crpt_call_raw, and no when-not-to-use guidance. An agent can infer the context, but must do the inference itself.

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