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Generate a GEO/AEO article

generate_article

Starts generating a new SEO/GEO-optimized article via AI for a given topic and site. Counts against the account's monthly article quota, same as generating from the dashboard. Does NOT publish it — call publish_article separately once you're happy with it.

ASYNC: this returns a jobId immediately, it does NOT wait for the article. Generation typically takes 1-6 minutes (reasoning model, non-streaming) — poll get_generation_status with the returned jobId every ~15-20s until status is "ready" or "error". (Older integrations that awaited this call directly would hit their own client's default request timeout, typically 60s, long before generation finished — this is why it's async now.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesThe article topic or working title
siteIdNoTarget site id (from list_sites) — determines language, tone and SEO tags used

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only cover the boolean safety profile; the description adds substantial context beyond that — it consumes the monthly article quota, it is non-publishing, it returns a jobId immediately rather than the article, generation takes 1-6 minutes, and polling every ~15-20s is required. Even the timeout rationale for the async redesign is disclosed.

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?

Front-loaded with purpose, constraint, and async contract in tight sentences. The trailing parenthetical about older integrations hitting a 60s client timeout is historical color that an agent does not need in order to invoke the tool correctly, so one sentence is not fully earning its place.

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 return-value burden and does so: it names the jobId, the status values 'ready'/'error', and the expected latency. For a 2-param async generation tool, nothing an agent needs to call and follow up correctly is missing.

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 both topic and siteId (including the list_sites provenance and its effect on language/tone/SEO tags) are already fully documented in the schema. The description restates 'a given topic and site' without adding syntax or format detail, so the baseline 3 applies.

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+resource ('starts generating a new SEO/GEO-optimized article via AI') scoped to a topic and site, and explicitly contrasts itself with the sibling publish_article. An agent can distinguish this from refresh_article or get_generation_status without opening any schema.

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?

Explicitly routes the agent: 'Does NOT publish it — call publish_article separately once you're happy with it,' and names get_generation_status plus the polling cadence and terminal states. When-to-use, what-to-do-next, and the alternative are all spelled out.

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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