AI 인사이트
get_ai_insights매월 3일 발행하는 AI 인사이트(변화 신호 해설)를 돌려줍니다. month 를 비우면 발행된 달 목록입니다. 맛보기 한도를 1회 씁니다.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| agent | No | 이 도구를 부르는 AI 에이전트 · 서비스 이름(예: Claude, ChatGPT). 밝히시면 하루 한도가 늘어납니다 | |
| month | No | YYYY-MM |
get_ai_insights매월 3일 발행하는 AI 인사이트(변화 신호 해설)를 돌려줍니다. month 를 비우면 발행된 달 목록입니다. 맛보기 한도를 1회 씁니다.
| Name | Required | Description | Default |
|---|---|---|---|
| agent | No | 이 도구를 부르는 AI 에이전트 · 서비스 이름(예: Claude, ChatGPT). 밝히시면 하루 한도가 늘어납니다 | |
| month | No | YYYY-MM |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds information annotations cannot carry: the fixed monthly publication schedule and that each call consumes one unit of a trial quota, which materially affects invocation decisions.
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?
Three short sentences with the core action and cadence front-loaded, then the empty-month behavior, then the quota cost. Nothing wasted, though the quota sentence is slightly cryptic about what a 'trial quota' is.
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?
There is no output schema, so the description is the only source of return-shape information; it characterizes the content only loosely as 'change-signal commentary' and doesn't say how many issues or what fields come back. Adequate for calling the tool, but thin on what the response looks like.
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%, so baseline would be 3. The description goes beyond the bare 'YYYY-MM' schema by defining the special semantics of an empty month (returns the publication list), which is the only non-obvious parameter behavior; the agent param's benefit is already stated in the schema.
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 concrete verb and resource (returns the monthly AI insight / change-signal commentary) and pins the publication cadence to the 3rd of each month. It does not name a sibling (e.g. get_industry_brief) to contrast with, which keeps it below 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?
It does imply two modes — passing a month fetches that issue, omitting month returns the list of published months — which is genuine usage guidance. However, it never says when to pick this over get_industry_brief or get_data_status, so alternatives are left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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