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StatPan

AssemblyMCP

by StatPan

analyze_legislative_issue

Generate a comprehensive legislative analysis report on any issue, combining related bills, key details, committee minutes, and lead lawmakers in one integrated overview.

Instructions

특정 주제(이슈)에 대한 종합적인 입법 현황 분석 리포트를 생성합니다. 이 도구는 관련 법안 검색, 주요 법안의 상세 내용, 관련 위원회 회의록, 그리고 해당 주제를 주도하는 주요 국회의원 정보를 한 번에 통합하여 제공합니다.

Args: topic: 분석할 입법 주제 또는 키워드 (예: "인공지능", "저출산"). limit: 검색할 관련 법안 수 (기본 5).

Returns: 종합 분석 리포트 데이터.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
topicYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.6.6

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden of explaining behavior. It discloses that the tool aggregates several data sources into a single report and describes the return value. It does not mention side effects or limitations, but for a read-oriented report generator this is a reasonable level of transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the main purpose, and uses a clear Args/Returns structure. Every sentence adds useful information without unnecessary filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, integration behavior, both parameters, and the existence of a return value; the output schema handles the detailed return structure. It is slightly incomplete in that it does not address sibling-tool tradeoffs or potential error/edge cases, but it is sufficient for a moderately complex analysis tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema provides no parameter descriptions (0% coverage), but the description fully compensates: topic is explained as a legislative issue/keyword with examples, and limit is defined as the number of related bills to search with a default value. This adds meaning well beyond the bare input schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's specific action: generating a comprehensive legislative analysis report on a given topic. It also enumerates the integrated components (bill search, bill details, committee minutes, key lawmakers), which distinguishes it from sibling tools like analyze_voting_trends or search_bills.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes the intended use clear: providing a one-stop comprehensive analysis of a legislative issue. However, it does not explicitly mention alternatives or state when not to use this tool, so it stops short of full routing guidance.

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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