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StatPan

AssemblyMCP

by StatPan

analyze_voting_trends

Analyze voting trends for bills related to a specific topic, summarizing passage outcomes and yea/nay statistics to reveal legislative patterns.

Instructions

특정 주제(키워드)와 관련된 법안들의 본회의 투표 경향을 분석합니다. 최근 관련 법안들의 가결 여부와 찬반 통계를 요약하여 제공합니다.

Args: topic: 분석할 주제 또는 키워드 (예: "종합부동산세", "간호법").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv0.6.6

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It discloses that it provides summaries of passage and vote stats, but does not disclose data source scope, time range, aggregation method, or whether results are limited to certain plenary sessions. It also does not mention what the output summary includes beyond vague 'recent bills'.

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

Conciseness4/5

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

The description is compact and front-loaded, with a clear definition and example. The Args section is a slight structural redundancy since it's already in the schema, but overall it earns its place by giving the example.

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

Completeness3/5

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

The output schema exists, so return values are covered. The tool is fairly simple (one param), but the description could be more complete regarding what 'recent' means, which bills are included, and how the trend analysis is computed. A 3 reflects the adequate but not thorough guidance.

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

Parameters4/5

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

The schema has 0% description coverage for the topic parameter, and the description compensates well by defining topic as a subject/keyword and giving two concrete examples. This is enough for an agent to understand what to pass, though it doesn't specify format constraints (e.g., exact bill title vs keyword).

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

Purpose4/5

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

The description clearly states it analyzes plenary voting trends for bills related to a keyword, summarizing passage status and yes/no statistics. It distinguishes itself from sibling tools like get_bill_voting_results (which likely returns raw results) and analyze_legislative_issue (broader issue analysis), though it doesn't name the closest sibling explicitly.

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

Usage Guidelines3/5

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

The description implies use when one wants voting trend analysis by topic, and the sibling list shows alternatives like get_bill_voting_results and get_member_voting_history. However, it does not explicitly state when to choose this over those alternatives, nor does it specify limitations like recency or scope of bills covered.

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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