legal_research

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

Annotations already carry the full safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false, openWorldHint=true), lowering the burden. The description does add genuinely useful orchestration context — parallel API chaining, automatic scenario/domain detection when unspecified, and the document_review text requirement. But it doesn't disclose richer runtime behavior such as latency, number of sub-calls, or rate-limit implications of the parallel execution. Moderate value over a rich annotation set.

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 long but every section earns its place: purpose is front-loaded in the first clause, followed by a compact task table and scenario routing. For a 12-parameter, 8-task, 9-scenario tool this density is justified, not padding. Minor deduction for the encyclopedic listing style, which sacrifices scannability slightly.

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 high complexity (12 params, 3 enums, 8 tasks, 9 scenarios) and the absence of an output schema, the description covers a lot: task semantics, scenario compatibility, auto-detection behavior, and fallback routing. It does not describe the response/return shape, but with no output schema and a research-category tool this is a tolerable gap. Comprehensive for its complexity tier.

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 coverage is 100%, with every one of the 12 parameters already described in the schema including enums with examples, patterns (YYYYMMDD), and conditional requirements (document_review requires text). The description complements this by cross-referencing task-to-scenario compatibility and default values, but most semantic weight is already in the schema. Baseline 3 is appropriate since the description adds only marginal contextual value over the structured fields.

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?

States a specific verb+resource: it is a Korean-law MCP multi-stage research tool that chains multiple APIs in parallel for complex questions. It enumerates 8 distinct task types with concrete examples, and explicitly differentiates from siblings by telling agents to use search_law/get_law_text for single-query answers. An agent can clearly tell this apart from the sibling list.

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?

Provides explicit routing guidance: use this only for composite/complex questions ('복합 질문 전용'), and states the exclusion condition — if a single search suffices, use search_law/get_law_text instead. Each task type carries a usage example, and the incompatible/when-to-avoid signals are explicit rather than implied.

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