LMS MCP 기능 제안

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

The description discloses meaningful behavioral details beyond the annotations: it sends submitted content to a remote address if configured, requires explicit user consent before calling, avoids collecting personal and LMS academic content, and stores locally in a retry-pending state on failure. No contradiction with annotations exists.

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 well-structured, consisting of two focused sentences that convey purpose, consent requirement, privacy scope, and failure behavior without unnecessary detail.

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 rich schema and the absence of an output schema, the description is complete enough for an agent to understand when and how to invoke the tool, what side effects may occur, and what safeguards are required.

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 JSON schema already provides 100% coverage of all 8 parameters with clear descriptions and constraints. The tool description adds overall context about privacy and consent but does not significantly enrich per-parameter semantics beyond what the schema already states.

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: receiving new feature or UX improvement suggestions. It uses a specific verb ('접수합니다') and a specific resource ('새 기능이나 UX 개선 의견'), which distinguishes it from problem-reporting siblings like report_lms_problem.

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 when to use the tool (for feature/UX suggestions) and includes important procedural guidance about showing content and obtaining explicit consent. However, it does not explicitly contrast this tool with alternatives such as report_lms_problem or explain when not to use it.

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