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seodata_generate_article

Queue background AI article generation for a storefront from a topic and keywords, returning a job ID for status checks; completed articles become storefront drafts.

Instructions

Запуск AI-генерации статьи в фоне (очередь комбайна). Возвращает jobId — статус смотри через seodata_get_job; готовая статья появится черновиком витрины. Scope: generate:run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoТон (по умолчанию expert)
topicYesТема статьи, 3–2000 символов
lengthNoОбъём (по умолчанию medium)
keywordsNoКлючевые слова через запятую (до 1000 символов)
languageNoЯзык (по умолчанию ru)
keywordIdNoId главного ключа из панорамы — статья пишется строго под него
storefrontIdYesId витрины (число из seodata_list_storefronts)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does reasonably well: it discloses that generation is asynchronous (background/очередь), returns a jobId, that the finished article lands as a draft, and that it requires the generate:run scope. It omits error/failure behavior and any rate or quota limits.

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?

Compact, front-loaded sentence covering action, async nature, return value, and scope with no filler. Slightly dense due to parenthetical queue note and trailing scope, but efficient overall.

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?

No output schema exists, so the description appropriately explains the return (jobId) and the async follow-up path via seodata_get_job, plus the final draft destination. It is complete enough for a 7-param generation tool, though permission/error details remain thin.

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?

Schema description coverage is 100%, so the schema already documents every parameter (tone, length, keywords, keywordId, etc.) with defaults and constraints. The description adds no parameter-level detail beyond what the schema provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (запуск AI-генерации) and resource (статья) and notes it runs in the background queue. It also clarifies the outcome (article appears as a storefront draft), which distinguishes it from read-oriented siblings like seodata_list_articles or seodata_get_article.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It directs the agent to seodata_get_job for status tracking, establishing the async workflow, and implies the tool is the entry point for article creation. However, it does not explicitly state when NOT to use it or contrast it against other generation paths.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.