mcp-server

get_import_status

Read-only

Check whether a project's store data is connected, imported and fresh before advising on feeds/mapping. Returns {projectId, code, apiStatus, dataStatus, lastImportAt, progressPercent, productsCount, categoriesCount, lastRun:{...}, recentRuns:[{status, type, startedAt, endedAt, productTotal, productsAvailable, errorMessage, importTimeSeconds, running}]}. apiStatus is GRANTED|REVOKED|OVERLIMIT; dataStatus is not_imported|pending|processing|ok|error|suspend|stopped (the project-wide state — map only when 'ok'). recentRuns lists the latest runs newest-first, each tagged by type (normal|partial|api_source|additional_source|upgrade) and running (true while pending/processing); lastRun is the newest of them. Use recentRuns to verify a specific enrichment import: after set_api_source/set_ai_source with apply:true (or run_import), poll here until the newest run of that type (api_source for set_api_source, additional_source for set_ai_source) has running:false and status 'ok' — THEN the new attribute is available in list_source_attributes / list_api_sources and you can map it or continue the integration. A run with status 'error' → read its errorMessage and run_import to retry. project_id is OPTIONAL (inferred for a single-project customer; project_id_required otherwise — then call list_projects). When dataStatus='ok', proceed to preview_products then list_feed_templates.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`project_id`	No

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral detail: status enums, polling semantics (running true while pending/processing), the meaning of dataStatus='ok' as the only state appropriate for mapping, and that recentRuns is newest-first. It also explains how to verify a specific import by type without contradicting the annotations.

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?

Although lengthy, every sentence carries essential operational information: statuses, output fields, polling workflow, error handling, and project_id inference. The description is front-loaded with the core purpose and then systematically details the response shape and usage sequence, with no filler or repetition.

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 tool with one optional parameter and no output schema, the description is unusually complete. It documents the full response shape, all status values, execution semantics, related next-step tools, and a concrete polling procedure. Nothing an agent needs to invoke it correctly or interpret its results 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%, but the description fully compensates: it explains that project_id is optional, inferred for single-project customers, required otherwise, and instructs to call list_projects in the latter case. This adds meaning far beyond the bare integer schema definition.

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?

States a specific verb and resource: 'Check whether a project's store data is connected, imported and fresh' before advising on feeds/mapping. This clearly distinguishes it from sibling tools like get_feed_status or list_feeds by establishing its role as a readiness/import verification step.

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?

Provides explicit when-to-use guidance: before advising on feeds/mapping, after set_api_source/set_ai_source with apply:true, and when dataStatus is 'ok' then proceed to preview_products and list_feed_templates. It also explains the error path (read errorMessage, run_import to retry) and when to call list_projects, leaving no ambiguity about alternatives.

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.

Resources

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