baton_approve
방장이 입장 대기자를 승인한다(require_approval 방).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | 방장 계정 키 | |
| room_id | Yes | ||
| member_id | Yes | 승인할 참가자 |
방장이 입장 대기자를 승인한다(require_approval 방).
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | Yes | 방장 계정 키 | |
| room_id | Yes | ||
| member_id | Yes | 승인할 참가자 |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool is an approval action (mutation) but lacks details on permissions, idempotency, rate limits, or side effects. For a mutation tool, this is minimal.
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 concise sentence that efficiently conveys the core action. However, it lacks structure (e.g., bullet points or sections) that could improve readability. 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?
Given the tool has 3 required parameters, no output schema, and no annotations, the description is adequate for a simple approval action but misses details like return value, idempotency, and error scenarios. It does not fully inform an AI agent about all relevant behavior.
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 schema provides descriptions for 2 of 3 parameters (api_key, member_id), covering 67%. The description adds no additional parameter semantics beyond the action context. With partial coverage, a baseline score of 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 verb 'approves' (승인) and the resource 'entry waiting persons in a require-approval room', which distinguishes it from siblings like baton_kick (expel) or baton_join (join). The purpose is specific and unambiguous.
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 (room with require_approval) but does not explicitly state when to use versus alternatives, such as baton_pass or baton_join. No exclusion criteria or usage conditions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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