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Hug0x0

mcp-reunion

reunion_get_legislative_2024_round1

Retrieve results of the 2024 French legislative election first round for La Réunion, including candidate details and vote statistics per circonscription.

Instructions

Results of the 2024 anticipated legislative elections (élections législatives anticipées, 1st round on June 30, 2024 — called by President Macron after dissolving the Assemblée nationale post-European elections) for La Réunion, aggregated per circonscription (Réunion has 7 circonscriptions). Returns base stats (registered, voters, abstentions, blank, null, expressed votes + percentages) and an array of candidates with panel number, political nuance, last/first name, sex, vote count, % of registered/expressed, elected flag (R1 elections happen rarely). Source: Ministère de l'Intérieur via data.gouv.fr (queried through tabular-api). For round 2 use reunion_get_legislative_2024_round2.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
circumscriptionNoRéunion circonscription number, integer 1 to 7. Omit to return all 7 circonscriptions
Behavior4/5

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

No annotations provided; description compensates well by detailing the return structure: base stats and candidate array with fields. It also notes that R1 elections happen rarely, which is useful behavioral context.

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?

Description is detailed but front-loaded with essential information. Includes background context that may be helpful, but is still relatively concise for the amount of information conveyed.

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

Completeness5/5

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

No output schema, but description explains return fields (base stats and candidate array) and includes source attribution. It provides sufficient context for an agent to understand what the tool returns.

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

Parameters3/5

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

Schema coverage is 100% with a clear parameter description. The tool description does not add significant new semantic information beyond the schema's existing description.

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 it returns results of the 2024 anticipated legislative elections for La Réunion, aggregated per circonscription. It distinguishes from siblings like reunion_get_legislative_2024_round2 and other election tools.

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 mentions the specific election context and explicitly directs users to reunion_get_legislative_2024_round2 for round 2. It provides clear context for when to use this tool, though it does not explicitly list exclusions from other sibling tools.

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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