list_topic_lives

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

With no annotations provided, the description carries the full disclosure burden. It successfully reveals filtering behavior (only completed and AI-processed lives) and authorization requirements, but omits safety classification (read-only), pagination limits, or error behaviors.

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?

Extremely concise with two sentences that earn their place: the first defines the operation and filters, the second states prerequisites. Information is front-loaded with no redundant language.

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 list operation with 100% schema coverage and no output schema, the description provides sufficient context by explaining what subset of data is returned (completed/AI-processed) and the auth requirement. It appropriately delegates parameter details to the schema.

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%, establishing a baseline of 3. The description implicitly links the 'knowledge base' mentioned in the first sentence to the `topic_id` parameter, but the schema already explicitly defines `topic_id` as '知识库 ID', so no additional semantic value is added beyond the structured definition.

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 uses a specific verb (获取/get) and resource (直播列表/live stream list), and clearly defines scope (知识库中/in knowledge base). It distinguishes from siblings like `get_live_detail` by specifying this returns a filtered list (completed and AI-processed), though it doesn't explicitly name alternatives.

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 states the required scope (topic.live.read), providing prerequisite context. However, it lacks explicit guidance on when to choose this over `list_topic_notes` or `get_live_detail`, though the filtering criteria (completed + AI-processed) imply usage context.

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