Korean Law MCP

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it performs a 'Deep Search' returning a 'comprehensive markdown document' with main article and connected texts, implying a structured output. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a tool with no annotations.

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 front-loaded with the core purpose, followed by usage context and examples, and ends with return details. Every sentence adds value without redundancy, such as listing what the search includes and providing concrete query examples, making it efficient and well-structured for quick understanding.

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 (deep legal search) and no annotations, the description is mostly complete: it explains purpose, usage, and output format. However, with an output schema present, it doesn't need to detail return values, but it could benefit from mentioning potential limitations or dependencies. Sibling tools provide context, but the description adequately covers the core functionality.

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 0% description coverage, so the description must compensate. It adds meaning by specifying the parameter 'query' should be a law provision reference (e.g., 'Higher Education Act Article 20'), including examples in both English and Korean. This clarifies the expected format beyond the schema's generic 'string' type, though it doesn't detail constraints like exact syntax or validation rules.

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 specific action ('Perform a Deep Search') and resource ('Legal Graph'), explaining it retrieves a law provision with its full context including references and regulations. It distinguishes from siblings like 'search_law_articles' or 'read_legal_resource' by emphasizing comprehensive, interconnected results rather than basic searches or single articles.

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?

Explicitly states 'Use this when you want to understand the full context of a law provision', providing clear when-to-use guidance. It includes examples like 'Higher Education Act Article 20' to illustrate appropriate queries, helping differentiate from tools that might return partial or historical data (e.g., 'get_article_history' for version changes).

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