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

With no annotations, the description carries the full burden of behavioral disclosure. It explicitly states the pay-per-call cost, the payment method, the requirement for a payment-signature header, and the error behavior when payment is missing. This goes well beyond a generic generation tool description, though it does not mention rate limits or any additional authentication requirements.

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 purposeful sentences: the first states purpose and outputs, the second states cost and payment rail, and the third discloses the failure mode. It is front-loaded and every sentence contributes critical information without redundancy.

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?

The description covers the core purpose, output format, payment requirement, and error behavior, which is strong given the absence of an output schema and annotations. Minor gaps remain, such as how to construct a valid payment-signature header or whether context is required for quality, but these do not prevent a competent agent from invoking it.

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 input schema already provides 100% coverage with descriptions for both 'query' and 'context', including an example. The description adds only the high-level notion of 'topic' and the output contents, but does not significantly elaborate on how the parameters should be used or combined.

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 writes a LinkedIn post, names the specific deliverables (post text, hashtags, engagement hook), and is distinguishable from sibling generation tools like generate-cold-email or generate-show-notes by the explicit 'LinkedIn post' resource.

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 intended use case is implied by the tool name and the phrase 'Write a compelling LinkedIn post about a topic,' but the description provides no explicit when-to-use guidance, exclusions, or comparisons to sibling tools. It does not help an agent choose between this and other generate-* tools beyond the resource name.

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