mcp-server

get_import_status

Read-onlyIdempotent

Check whether a project's store data is connected, imported and fresh before advising on feeds/mapping. Use this to confirm the store data is ready before mapping or creating feeds. 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 run_api_source_import / run_ai_source_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 call the same import tool again 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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_import_status"
  }
}

Input Schema

Name	Required	Description	Default
`project_id`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 1, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint/idempotentHint, so the safety profile is covered; the description goes well beyond by disclosing the full status state machine, the polling protocol (poll until running:false and status 'ok'), the error→retry behavior, and the fact that a run being 'running' means pending/processing. That is exactly the operational context an agent needs and could not infer from annotations.

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 gating condition and purpose are front-loaded and every paragraph maps to a real agent decision (readiness, polling, retry, next step). It is dense and long, with some restatement (lastRun described as newest of recentRuns twice), which keeps it from a 5.

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?

There is no output schema, so the description takes on that burden entirely and does so: it enumerates the returned fields, the allowed values of apiStatus and dataStatus with their meanings, and the structure of recentRuns entries including the type tags. Nothing an agent needs to interpret a response or decide its next call is missing.

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 only parameter, and it does: project_id is OPTIONAL, inferred for single-project customers, and a project_id_required signal means calling list_projects. It stops just short of naming the exact error shape or accepted value range, so not quite a 5.

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 opening sentence names a specific verb and resource ('Check whether a project's store data is connected, imported and fresh') and scopes it to the pre-mapping/pre-feed phase. This clearly separates it from siblings like get_feed_status, ad_status or marketplace_status, which cover different resources.

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?

It states both when to use it (before advising on feeds/mapping, before creating feeds, after run_api_source_import / run_ai_source_import) and a hard gate ('map only when ok'). It also names the next tools to call on success (preview_products then list_feed_templates), the retry path on error, and the list_projects fallback when project_id_required.

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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