get_brand_topic

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

No annotations are provided, so the description carries the burden. It discloses the output format (markdown) and the scope (full spec for one topic), which is useful. However, it doesn't disclose whether the tool is read-only (likely safe), whether it can fail for invalid topics, or any rate limits. The description is adequate but not rich in 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.

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

The description is a single, well-structured sentence that front-loads the core action ('Get the full ApoSuite brand spec for one topic as markdown') and then provides clarifying examples. No wasted words.

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?

For a simple single-parameter tool with a clear enum, the description is nearly complete. It tells the agent what it returns (markdown spec) and how to pick a topic (from list_brand_topics). The only missing piece is explicit mention of error behavior for invalid topics, but the enum in the schema mitigates that. No output schema exists, but the description's mention of markdown format partially covers return expectations.

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 description coverage is 100%, so the schema already documents the 'topic' parameter well, including an enum of valid values. The description adds context by explaining that the topic is one returned by list_brand_topics, which is helpful. But it doesn't add much beyond the schema, so baseline 3 is appropriate.

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: retrieving the full ApoSuite brand spec for a single topic as markdown. It provides concrete examples of what the content includes (button states, field focus colors, color palette), which makes the purpose immediately understandable. It also distinguishes itself from siblings by focusing on 'full brand spec for one topic' rather than listing topics or searching.

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 implies usage context: use this when you need the complete brand specification for a specific topic. The parameter description references list_brand_topics, which tells the agent to first get the list of topics. However, it doesn't explicitly state when to use this vs. get_color or search_brand, though the 'full spec' wording helps differentiate.

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