get_learning_history

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

With no annotations provided, the description carries the full burden of behavioral disclosure. The word '조회합니다' indicates a read/query operation and the description states what data is returned, which is helpful. However, it does not mention whether results are paginated, sorted, or limited beyond the schema's 'limit' parameter, nor does it clarify any access considerations.

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 sentence that immediately states the resource and action, making it easy to scan. The only waste is the '[MCP Tool]' prefix, which is redundant metadata, but overall the structure is tight and front-loaded.

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 relatively simple read tool with two parameters and no output schema, the description covers the core purpose and the nature of the returned data. However, it omits parameter semantics and any usage context, so an agent has only partial information needed to invoke it confidently.

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 by clarifying parameter meaning. It does not describe 'project_slug' or 'limit' at all. The names are somewhat self-explanatory, but the description adds no semantic value beyond the schema itself.

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 uses a specific verb ('조회합니다' / retrieves) and clearly identifies the resource: the project's recent learning review records and top recurring weaknesses list. It distinguishes the tool from siblings like get_project_context or get_evidence by naming a distinct data domain, though it does not explicitly contrast itself with any sibling.

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 guidance is given about when to use this tool versus alternatives such as get_project_context or get_evidence. There are no stated conditions, exclusions, or hints about whether this should be called before or after other learning/review operations.

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