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AiAgentKarl

political-finance-mcp-server

get_top_donors_by_industry

Retrieve top political donors by industry for a specified election cycle. View total contributions from major sectors like technology, finance, healthcare, and energy.

Instructions

Zeigt Top-Spender nach Industrie/Branche (via FEC).

Args: industry: Branche (z.B. "technology", "finance", "healthcare", "energy") election_cycle: Wahlzyklus-Jahr (Standard: 2024) limit: Anzahl Ergebnisse (Standard: 10)

Returns: Top-Spender der Branche mit Summen

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
industryYes
election_cycleNo
limitNo
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the data source ('via FEC') and return format ('mit Summen'), but does not disclose behavioral traits like rate limits, data freshness, or authorization needs. There is no contradiction with annotations.

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

Conciseness3/5

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

The description is organized with Args and Returns sections, but uses German which may hinder English agents. It is relatively concise but could be more streamlined and include English translations for broader accessibility.

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 description explains parameters and return value (top donors with sums), but lacks details on error handling, pagination, or limitations. Given no output schema, more context would be beneficial for a data tool.

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 input schema has 0% description coverage, but the description adds meaning by listing parameters with examples ('technology', 'finance'), defaults (election_cycle=2024, limit=10), and purpose. This compensates for the lack of schema descriptions.

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 the tool shows top donors by industry, using 'Top-Spender nach Industrie/Branche (via FEC).' The verb 'zeigen' (show) and resource 'top donors by industry' are specific. While it distinguishes from siblings like get_campaign_finance_summary, it does not explicitly differentiate in the description.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The description lacks context on preferred scenarios, exclusions, or comparisons to sibling tools such as search_individual_donations or get_pac_spending.

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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