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ztemerbekov

A1 Yandex KIT MCP

by ztemerbekov
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JavaScript
Remote

List products

list_products
Read-only

Retrieve paginated product records for raw catalog-structure dumps. Use when you need explicit product-group data; check coverage envelope and report partial results.

Instructions

List product records (paginated); for export requests, use this for explicit raw product-group or catalog-structure dumps. Ordinary catalog exports use list_variants: a product carries grouping and category data, not a sellable name, SKU, price or stock. format:"csv" defaults to top-level scalar fields (currently id and group_id) and does not flatten variant data. The response carries a machine-readable coverage envelope (coverage, received, total_count, pages_read); coverage:"partial" MUST be reflected in the user-facing answer and forbids claiming the listing is complete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
allNoFetch pages via auto-pagination, up to 500 items; inspect coverage and continue with explicit page reads if coverage is partial; ignores page/per_page.
pageNoPage number, starting at 1 (default 1).
fieldsNoCSV columns; only valid together with format:"csv". Field names are validated against the item schema of the operation's response — an unknown name fails with the list of allowed fields. Default: every top-level scalar field of the item.
formatNoOutput format: "csv" renders the items as RFC 4180 CSV (a leading "# coverage:" comment line, then the header row) instead of JSON — cheaper for wide exports. Default: JSON.
per_pageNoItems per page, 1-100 (default 25). Values outside the range are clamped.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.6.2
    • changedInput schema / properties / all / description
      Previous value: -"Fetch all pages via auto-pagination, up to 500 items; ignores page/per_page."New value: +"Fetch pages via auto-pagination, up to 500 items; inspect coverage and continue with explicit page reads if coverage is partial; ignores page/per_page."
  2. Changed2 schema fields changedv1.6.0
    • addedInput schema / properties / fields
      Added value: +{
+  "description": "CSV columns; only valid together with format:\"csv\". Field names are validated against the item schema of the operation's response — an unknown name fails with the list of allowed fields. Default: every top-level scalar field of the item.",
+  "items": {
+    "type": "string"
+  },
+  "minItems": 1,
+  "type": "array"
+}
    • addedInput schema / properties / format
      Added value: +{
+  "description": "Output format: \"csv\" renders the items as RFC 4180 CSV (a leading \"# coverage:\" comment line, then the header row) instead of JSON — cheaper for wide exports. Default: JSON.",
+  "enum": [
+    "csv"
+  ],
+  "type": "string"
+}
  3. First observedv1.3.0

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint annotation by disclosing the coverage envelope (coverage, received, total_count, pages_read), the mandatory rule that coverage:'partial' must be reflected in the user-facing answer and forbids claiming completeness, and the CSV behavior of defaulting to top-level scalar fields only (id and group_id) without flattening variant data. These are non-obvious behavioral constraints that materially affect agent output.

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?

Four sentences with zero filler; the core purpose and routing guidance are front-loaded in the first two sentences, and the remaining two add only high-value behavioral details. Each sentence earns its place and the key disambiguation from list_variants is placed early.

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 no output schema present, the description compensates by documenting the response envelope fields and the partial-coverage contract, and it names the concrete CSV default fields (id and group_id). It stops short of fully describing the JSON item shape or pagination semantics, but for a straightforward list tool whose parameters are fully documented in the schema, the coverage is strong.

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 100%, so the baseline is 3; the description adds genuine value on top by specifying that format:'csv' defaults to id and group_id specifically, does not flatten variant data, and by tying the coverage envelope to pagination behavior. The schema already covers the mechanics of each parameter, so the description's contribution is additive but modest.

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 plus resource ('List product records (paginated)') and then differentiates from the sibling list_variants by articulating the data-model difference: a product carries grouping and category data, not a sellable name, SKU, price or stock. This makes the tool's scope unambiguous and separates it from its closest sibling.

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 when to use it ('explicit raw product-group or catalog-structure dumps') and names the alternative for the other case ('Ordinary catalog exports use list_variants'). No inference is required — the selection condition and the rejected alternative are both spelled out.

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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