Generate improvement action candidates and 3, 6, 12, and 24-month checkpoints from hypothesis results; flag pre-validation tasks as conditional on expert review.
가설 판정 결과로 개선과제 후보(검증·개선·구축·모니터링·유지 등)와 3·6·12·24개월 점검 시점을 만듭니다. 검증 전 실행 과제는 '전문가 검토로 원인 확정 시' 조건부로 표시됩니다.
| Name | Required | Description | Default |
|---|---|---|---|
| strengths | No | ||
| next_evidence | No | ||
| hypothesis_results | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, and it does disclose one meaningful trait: pre-verification execution tasks are conditionally labeled 'contingent on expert review confirming the cause.' However, it says nothing about whether this persists state, requires auth, or how it relates to record_review/record_outcome, leaving large behavioral gaps.
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?
Two sentences with no filler; the primary action (create candidates + check points) is front-loaded and the conditional-labeling caveat follows. Efficient and well-ordered.
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?
A recommendation-generating tool with no annotations, no output schema, and 3 undocumented parameters needs the description to fill more gaps. It omits what the returned candidates look like, how strengths/next_evidence feed in, and any prerequisite state, so it is under-specified for the agent's needs.
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% across 3 parameters, so the description must compensate. It only obliquely explains hypothesis_results ('가설 판정 결과') and gives no meaning at all for strengths or next_evidence, leaving two of three inputs undocumented in both places.
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 and resource: generating improvement-task candidates (verification/improvement/build/monitoring/maintenance) plus 3/6/12/24-month check points from hypothesis assessment results. An agent understands the output shape, though it never explicitly contrasts itself with siblings like assess_hypotheses or next_best_evidence.
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 phrase 'based on hypothesis assessment results' implies this runs downstream of hypothesis assessment, giving weak sequencing guidance. There is no explicit when-to-use, when-not-to-use, or named alternative among the many siblings, so the agent must infer its place in the pipeline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.