Generate a blog post

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals three key behaviors: asynchronous execution (returns a Job ID immediately), credit cost (~400 credits per post), and the side effect of fanning out translations at 5 credits per language. This is substantial context for an AI agent to anticipate the tool's operational characteristics and plan accordingly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loaded with the core action, and follows a logical progression: what it does, how to track it, and key cost/translation options. No filler or redundant content; every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (11 parameters, asynchronous pipeline, no output schema), the description covers the essential workflow: it specifies that a Job ID is returned and how to poll for results, mentions the cost implications, and explains the translation fan-out behavior. This is sufficient for an agent to use the tool correctly without needing to inspect the schema for workflow details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides 100% description coverage for all 11 parameters, so the baseline is 3. The description adds minimal parameter semantics beyond the schema; it mentions translate_to_all and its cost, but this is more behavioral than semantic. The schema already explains each parameter's purpose and defaults, so the description does not need to compensate.

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

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

The description clearly states the tool's function: 'Kick off the AI blog generation pipeline' with an explicit series of stages (research → draft → image → SEO). This distinguishes it from sibling tools like get_blog_post or generate_image by focusing on the end-to-end generation workflow rather than retrieving or editing an existing post.

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

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

The description provides practical guidance for post-invocation behavior: polling with get_job until completion. It also gives an explicit alternative for translation control ('For per-language control use translate_blog_post on the resulting post id'), helping the agent choose between this tool's fan-out feature and a more granular tool. However, it does not explicitly state when to use this tool versus other blog-related siblings (e.g., update_blog_post), though the purpose is largely implied by the name and pipeline description.

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