create_image_post

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

No annotations are provided, so the description carries the full burden. It discloses the API source ('Official API') and prerequisites, but lacks details on behavioral traits such as rate limits, authentication needs beyond the product requirement, error handling, or whether the operation is idempotent. It mentions the return format, which is helpful but not comprehensive for a mutation tool.

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 well-structured with clear sections (overview, prerequisites, args, returns) and uses bullet points for readability. It is appropriately sized, but could be slightly more concise by integrating the prerequisite into the main flow. Every sentence adds value, with no redundant information.

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 complexity of a mutation tool with no annotations, 4 parameters, and an output schema (which covers return values), the description is mostly complete. It explains prerequisites, parameters in detail, and hints at returns. However, it lacks full behavioral context (e.g., side effects, error cases), leaving a minor gap in completeness.

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 description coverage is 0%, so the description must compensate fully. It provides detailed semantics for all parameters: text (max length, content type), image_path (multiple source options with examples), alt_text (purpose and recommendation), and visibility (options and default). This adds significant value beyond the bare schema, effectively documenting all parameters.

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 specific action ('Create a LinkedIn post with an image') and resource ('using the Official API'), distinguishing it from sibling tools like create_post, create_document_post, and create_video_post by specifying the image media type. It goes beyond just restating the name by detailing the platform and method.

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 explicit prerequisites ('Requires "Share on LinkedIn" product enabled in your LinkedIn Developer app'), which gives clear context for when this tool can be used. However, it does not explicitly state when to use this versus alternatives like create_post or create_video_post, nor does it provide exclusions or comparative guidance with siblings.

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