Confluence Node MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions filtering capabilities (Space and date range) and notes that KMS is an internal alias, but fails to describe critical behaviors such as pagination (implied by 'limit' parameter but not explained), authentication requirements, rate limits, error handling, or what the output format looks like (e.g., list of comments with metadata). For a search tool with no annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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 concise and front-loaded, stating the core purpose in the first sentence and adding filtering details and context in subsequent sentences. It avoids redundancy and wastes no words, though it could be slightly more structured (e.g., separating functional description from contextual notes). Overall, it's efficient and easy to parse.

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 complexity (search with filtering), lack of annotations, and absence of an output schema, the description is incomplete. It covers the basic purpose and filtering options but omits essential context such as return value format, pagination behavior (beyond the 'limit' parameter), authentication needs, error cases, and how results are ordered or truncated. For a tool with no structured output documentation, this leaves the agent with insufficient information to handle responses effectively.

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 has 100% description coverage, with clear documentation for all 5 parameters (e.g., username, space, startDate, endDate, limit). The description adds minimal value beyond the schema by mentioning Space and date range filtering, but doesn't provide additional semantics like format details beyond what's in the schema (e.g., date format is already specified). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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 purpose: '搜索指定用户在 Confluence (KMS) 中发表的所有评论' (search for all comments by a specified user in Confluence/KMS). It specifies the resource (comments) and verb (search), and distinguishes from siblings like 'confluence_get_page_comments' by focusing on user-based rather than page-based comment retrieval. However, it doesn't explicitly differentiate from other search tools like 'confluence_search_pages' beyond mentioning '评论' (comments).

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 by stating '可按 Space 和日期范围筛选' (can filter by Space and date range), which suggests when to apply optional parameters. It doesn't provide explicit guidance on when to use this tool versus alternatives like 'confluence_get_page_comments' or 'confluence_search_pages', nor does it mention prerequisites or exclusions. The context is clear but lacks comparative or conditional directives.

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