Fetches completed, AI-processed live sessions for a given knowledge base using its topic ID and optional page number, enabling review of processed livestream content.
获取知识库中已完成且 AI 已处理的直播列表。需要 topic.live.read scope。
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | 页码，从 1 开始，默认 1 | |
| topic_id | Yes | 知识库 ID（来自 list_topics 的 topic_id 字段） |
Changes observed during successful MCP inspections.
v1.10.1Input schema / properties / topic_id / descriptionPrevious value: -"知识库 ID（alias id）"New value: +"知识库 ID（来自 list_topics 的 topic_id 字段）"v1.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It does add useful auth context by stating the topic.live.read scope requirement, and 'list/获取' implies a read operation. However, it says nothing about pagination behavior, ordering, or what the response contains, leaving notable gaps for an unannotated 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?
Two short sentences with zero waste; the filter condition is front-loaded before the scope requirement. Every clause earns its place.
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 paginated read with only two parameters at 100% schema coverage and no output schema, the description gives enough to call it correctly, including the required scope. Minor gaps remain around ordering and return shape, but nothing essential is missing.
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 both parameters (page, topic_id) are already fully documented in the schema. The description adds no syntax or format detail beyond that, making the baseline 3 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?
States a specific verb (获取/list) and resource (直播/livestreams) scoped to a knowledge base, plus a meaningful filter (completed and AI-processed). It is clear enough to distinguish from generic note tools, though it never names the closest siblings like get_live_detail or follow_topic_live.
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 'completed and AI-processed' qualifier tells the agent what this list contains, implying when it applies. But there is no explicit when-to-use vs. get_live_detail or the follow_topic_live tools, and no exclusion guidance, so usage is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.