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Write an article

generateArticle

Research and draft a new SEO article on a topic. The result is a DRAFT in Kooga - nothing reaches the site until it is published.

A change inside Kooga: it runs at once, as the person.

It uses one article from the workspace's plan.

Input Schema

NameRequiredDescriptionDefault
topicYesConcrete, specific article topic - not a category
formatNoThe article's shape. listicle for "best X for Y" questions (the shape AI answers quote for them), comparison for "X vs Y", alternatives for "alternatives to X"; leave it out for a guide
languageNoLeave this out: the draft is written in the store's own content language. Name one only to choose between the languages the store publishes in
workspaceIdNoThe workspace, as listWorkspaces gives it (its id or slug). Leave it out when this connection reaches one workspace.

Output Schema

NameRequiredDescriptionDefault
statusYes
pieceIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Beyond annotations, the description adds important context: execution is immediate and runs 'as the person', the output is a non-public draft, and it consumes one article from the workspace's plan. It does not cover error behavior or rate limits, but adds substantial behavioral detail.

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 short and front-loaded with the core purpose. The later sentences are slightly fragmented but each adds relevant behavioral detail without excess verbosity.

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?

Given the presence of an output schema and fully described parameters, the description covers the key behavioral aspects an agent needs: draft state, immediate execution, and plan usage. It could mention failure modes or rate limits, but is largely complete.

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 coverage is 100% and parameter descriptions are rich, so the schema carries the semantic load. The description adds little beyond the schema, only reaffirming that a topic is required and that the tool uses a plan article.

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?

The description states a specific verb (research and draft) and resource (new SEO article) and includes the scope that the result is a draft. It implicitly distinguishes from publishArticle and refreshArticle but does not explicitly name or contrast with siblings.

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

Usage Guidelines3/5

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

It implies usage by explaining the result is a draft and that publishing is a separate step, but does not specify when to use this tool versus alternatives like researchKeywords or refreshArticle. No explicit when-not guidance is provided.

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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