e-Gov Law MCP Server

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'efficiently' and 'performance stats' in returns, hinting at optimization, but lacks critical details: whether this is read-only or mutating, authentication requirements, rate limits, error handling, or what 'performance stats' entail. For a batch tool with zero annotation coverage, this leaves significant gaps in understanding operational behavior.

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 well-structured with a purpose statement, Args section, and Returns section, all in three sentences. It's front-loaded with the core function and avoids unnecessary fluff. However, the 'ctx' parameter in Args is not defined in the input schema, creating minor confusion that slightly reduces efficiency.

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 no annotations, no output schema, and low schema coverage, the description is moderately complete. It covers the main purpose and parameter format but misses behavioral details like safety, performance implications, and output specifics beyond 'Dict with batch results and performance stats'. For a batch operation tool, this leaves room for improvement in contextual understanding.

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 provides a clear example of the JSON format for 'law_article_pairs', explaining the structure with law-article pairs. This adds meaningful semantics beyond the bare schema, though it doesn't detail all possible values or constraints. With only one parameter, this is sufficient for baseline understanding.

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: 'Batch find multiple law articles efficiently' specifies the verb ('find'), resource ('law articles'), and scope ('batch', 'multiple'). It distinguishes from siblings like 'find_law_article' (singular) and 'search_laws' (different operation). However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.

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 for batch operations versus single lookups (contrasting with 'find_law_article'), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'search_laws' or 'search_laws_by_keyword'. No when-not-to-use instructions or prerequisites are mentioned, leaving usage context partially implied.

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