Coffee Company MCP Server

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

With no annotations provided, the description carries the full burden. It discloses the HTTP method (POST) and status code meanings (0-3), which adds valuable behavioral context beyond basic querying. However, it lacks details on error handling, rate limits, authentication needs, or response format, leaving gaps for a tool with mutation potential (POST).

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 appropriately sized and front-loaded with the core purpose in the first sentence. The status explanations and HTTP API note are relevant additions. However, the structure could be improved by separating usage guidelines or parameter details more clearly, and some redundancy exists in stating the tool name in the Args section.

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 moderate complexity (1 parameter, no annotations, but with output schema), the description is reasonably complete. It covers the purpose, parameter semantics, and behavioral aspects like status codes. The presence of an output schema reduces the need to explain return values. Minor gaps include lack of error handling or sibling tool differentiation.

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 0%, so the description must compensate. It adds meaning by specifying 'member_id' as 'Coffee Company会员ID' (Coffee Company member ID), clarifying the parameter's context and format. Since there's only one parameter, this is sufficient to elevate the score above baseline, though it doesn't detail validation rules or examples.

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: '查询会员 8 项权益状态' (query member's 8 benefit statuses) with specific examples like '新人礼' (newcomer gift) and '生日奖励' (birthday reward). It distinguishes from siblings like 'member_benefit_list' by focusing on status query rather than listing. However, it doesn't explicitly contrast with 'equity_detail' or 'equity_query', leaving some ambiguity.

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?

No explicit guidance on when to use this tool versus alternatives like 'member_benefit_list', 'equity_detail', or 'equity_query'. The description mentions the HTTP API endpoint but doesn't provide context about prerequisites, typical use cases, or comparisons with sibling tools. Usage is implied through the query focus but not clearly defined.

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