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neuratechcompany-ops

Kettu Marketplace Intelligence

Megamarket Search

megamarket_search
Read-onlyIdempotent

Find products on Megamarket by search query and get prices, availability, ratings, and seller details. Returns structured results with status and metadata for price comparison.

Instructions

Search the Megamarket catalog via the mobile API, inside the operator's Chrome.

Return Format

MegamarketSearchResponse: {status, query, tier_used, count, total_count, items[], meta}. price_rub is None when absent — never 0.

Error Format

ToolError: TransportDownError on ServicePipe refusals (with the fix inline); ParserDriftError when a reached-200 body no longer parses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch text, e.g. 'стиральная машина'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaNoValidation metadata.
countNoNumber of items returned.
itemsNoSearch result items.
queryNoSearch query text.
statusNoResponse status: success or error.success
tier_usedNoFetch tier used (cdp).
total_countNoTotal matches reported.

Schema Changelog

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

  1. First observedv1.2.2

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description goes beyond them by documenting return-format quirks ('price_rub is None when absent — never 0') and specific failure modes (TransportDownError, ParserDriftError), which is genuinely useful behavioral context.

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 compact and well-structured: a one-sentence purpose, followed by clearly labeled Return Format and Error Format sections. Every line earns its place, and the most important information is front-loaded.

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 single-parameter search tool with an output schema and rich annotations, the description is complete enough. It explains the response shape, critical edge cases, and failure modes without unnecessary elaboration.

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 schema covers the only parameter, query, with a description and an example. The tool description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.

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: 'Search the Megamarket catalog via the mobile API.' It also gives environmental context ('inside the operator's Chrome') and the tool name clearly distinguishes it from sibling card and selfcheck tools like megamarket_card and megamarket_selfcheck.

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 usage is implied by the verb 'Search' and the catalog target, but there is no explicit when-to-use guidance, exclusions, or mention of alternative search tools such as yandex_search or wb_search. It meets the minimum viability bar but leaves selection reasoning to the agent.

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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