mcp-server

preview_products

Read-only

Get a page of the project's imported products with their real source-attribute values, so you can inspect the data and advise on feed mapping (e.g. see that brand holds "Nike" or color holds hex codes). Returns {products:[{id, type, attributes:{:}}], returned, requested, total, offset, hasMore, nextOffset}. attributes keys are source attribute codes (as in list_source_attributes); only catalogued, non-sensitive attributes are included — cost/margin and internal fields are never returned, and empty values are omitted. sample_size is the page window (default 5, max 100); page through the whole catalog with offset — when hasMore is true, re-call with offset=nextOffset until hasMore is false. project_id is OPTIONAL (inferred for a single-project customer). Data must be imported first (see get_import_status). This tool returns real product data values only after you set acknowledge_sensitive:true. The first call (flag absent/false) returns {sensitiveGate:{confirmationRequired:true, itemCount, fields, kind}} with an EMPTY products array — present that gate to the USER, get their approval, then re-call with acknowledge_sensitive:true to receive the actual values. To review the full generated feed output for every product (not just source attributes), use export_feed instead of paging this preview — that is the intended bulk path. Next: pick a template (list_feed_templates) and map attributes (map_feed_attribute).

Input Schema

TableJSON Schema

Name	Required	Description
`offset`	No	Number of products to skip (paging); pass the response's nextOffset to get the next page.
`project_id`	No
`sample_size`	No	Page window: how many products to return per call (default 5, max 100).
`acknowledge_sensitive`	No	Set true only AFTER the user approves seeing real data. When absent/false the tool returns a sensitiveGate with an empty products array; with true it returns the actual attribute values.

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive, and the description adds substantial behavioral detail: the sensitive-data gate and acknowledge_sensitive flow, exclusion of cost/margin/internal fields, omission of empty values, and the paging contract via hasMore/nextOffset. No contradiction exists.

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?

The description is dense and front-loaded with its main purpose, and every sentence adds operational value. However, it is a single long paragraph that intermixes response shape, security gating, paging, prerequisites, and sibling routing; tighter structuring would improve scannability.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so completely: the products array shape, sensitiveGate behavior, paging loop, excluded fields, prerequisite import status, and routing to export_feed and mapping tools. An agent can invoke this tool correctly without needing external context.

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?

Although the schema already documents 3 of 4 parameters, the description goes beyond it by explaining project_id optionality/inference, sample_size default and max, offset/nextOffset paging behavior, and the exact semantics of acknowledge_sensitive with the sensitiveGate response flow. This fully compensates for the undocumented project_id parameter.

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 states a specific verb, resource, and purpose: 'Get a page of the project's imported products with their real source-attribute values' for inspecting data and advising on feed mapping. It also differentiates itself from export_feed by noting that export_feed is the intended bulk path for reviewing full generated feed output.

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?

The description explicitly says when to use this tool vs alternatives: use this for inspecting source attributes, use export_feed for full generated feed output. It also states prerequisites and next steps: data must be imported first (see get_import_status), then pick a template and map attributes.

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

A4.2/5.0

Disambiguation4/5

Tools are organized around distinct resources (ads, marketplaces, feeds, orders, rules, sources) with clear action verbs, and descriptions explicitly disambiguate near-pairs like get_feed_status vs ad_status or set_feed_filter vs set_feed_attribute_filter. A few similarly named status/action pairs (e.g. ad_status vs get_ad, run_ad_item_action vs run_ad_operation) require careful reading, but overall the purposes are separable.

Naming Consistency4/5

The overwhelming majority follow a consistent verb_noun snake_case pattern (list_*, get_*, create_*, set_*, run_*, test_*). Minor deviations like ad_status and marketplace_status (noun-based status tools) and koongo_knowledge break the pattern slightly, but the convention is clearly recognizable and predictable.

Tool Count1/5

At 105 tools, the surface is extreme and far beyond the 50+ threshold, even for a complex e-commerce integration domain. Much of the bloat comes from systematic triplication across ads, marketplaces, and feeds (e.g. three nearly identical map_*_attribute tools, three list_*_items, three get_*_report) that a generic resource parameter could have consolidated.

Completeness4/5

The toolset covers the full lifecycle of feeds, ads, marketplaces, order connections, rules, and imports, including create/read/update/delete, status monitoring, item-level actions, validation, repair, and restore. Minor gaps exist, such as no delete for standalone order connections and limited update capabilities for some entities, but these are workable and do not create dead ends for the core workflows.

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