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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it returns full text and is deterministic (from title), but does not disclose any additional behavioral traits like authentication needs, rate limits, or response size. With rich annotations, this is acceptable but not exceptional.

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 a single, front-loaded sentence that leads with the core action and then enumerates usage scenarios. It is efficient and every clause adds value, though it is slightly long and could be broken into bullet points for readability. Still, it is well-structured and not verbose.

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 simplicity (one enum parameter, no output schema), the description covers document selection thoroughly. It states the return type is the full text, which is sufficient. It does not describe error handling or response format details, but annotations cover safety and the schema constrains input, so the description is largely complete.

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?

Schema coverage is 100%, but the description goes far beyond the schema's simple 'Which document to return.' It semantically maps each key enum value (quickstart, openai, anthropic, attribution, verification, troubleshooting, onboarding) to concrete integration scenarios, providing critical decision-making support for the agent. This is exactly what parameter semantics should add.

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 it returns the full text of a Spendline agent document, with a verb and resource. It enumerates the specific documents available, which adds clarity. However, it does not explicitly differentiate this tool from sibling spendline_get_onboarding_instructions, which might cover the 'onboarding' document, so it is slightly less decisive than the ideal.

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 gives explicit when-to-use guidance for each document: quickstart for fastest integration, openai/anthropic for SDK specifics, attribution for header contract, verification for proving integration, troubleshooting for failures, and onboarding for no account. However, it does not mention when to prefer this tool over alternative sibling tools, so tool-level guidance is absent.

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