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André — Analyse électorale française

by vote.andre

Carte électorale

generate_map
Idempotent

Génère une carte électorale (PNG) pour une élection.

    Paramètres :
    - election_id : identifiant élection (ex: '2024_legi_t1', '2020_muni_t2')
    - metric : 'family' (famille politique en tête) ou 'nuance' (score d'une nuance)
    - geography : 'bv' (bureau de vote, défaut) ou 'commune' (carte nationale)
    - department : code département (ex: '13'). Requis pour geography='bv' sauf si commune fourni.
    - commune : code commune (ex: '13055' pour Marseille). Centre la carte sur cette commune.
    - nuance : code nuance quand metric='nuance' (ex: 'RN', 'LFI', 'ENS')
    - region : code région pour geography='commune' (ex: '93' pour PACA)

    Exemples :
    - Carte par famille, Bouches-du-Rhône : election_id='2024_legi_t1', department='13'
    - Carte score RN, Marseille : election_id='2024_legi_t1', commune='13055', metric='nuance', nuance='RN'
    - Carte nationale : election_id='2024_euro_t1', geography='commune'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cantonNo
metricNofamily
nuanceNo
regionNo
communeNo
geographyNobv
departmentNo
election_idYes
circonscriptionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate idempotentHint=true, which aligns with generating a deterministic map image. The description adds that output is a PNG, enhancing transparency about the result. No contradictions with annotations exist. It could mention rate limits or data freshness, but overall good.

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 well-organized: a one-line summary, then a bullet list of parameters with clear explanations, followed by illustrative examples. Every sentence serves a purpose, and the structure is easily scannable for an AI agent.

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?

With 9 parameters and an output schema present, the description covers all parameters and their relationships. It lacks explicit mention of error conditions or data freshness, but the examples and constraints provide a solid understanding. It is nearly complete for a map generation 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 description coverage is 0%, but the description compensates fully by explaining every parameter with examples and constraints. It clarifies interdependencies (e.g., department vs commune) and provides realistic usage scenarios, adding immense value beyond the bare 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 begins with a clear, specific statement: 'Génère une carte électorale (PNG) pour une élection.' This precisely identifies the verb (generate), resource (electoral map), and output format (PNG). It is distinct from sibling tools like query_elections or create_data_table, leaving no ambiguity.

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 provides detailed parameter constraints and examples, implicitly guiding when to use each parameter combination (e.g., 'geography='bv' requires department unless commune provided'). It does not explicitly compare to alternatives, but the sibling list lacks another map tool, so context is sufficient.

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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TDQS

A3.7/5.0
Disambiguation4/5

The query_* tools are mostly cleanly separated by domain (elections, admin, DLP, élus, sociodem), and the output-oriented tools (map, table, visualization, export) are distinct. The main potential confusion is query_sql versus query_elections, since both can access election results, but the descriptions mitigate this by explicitly recommending query_elections for standard analysis.

Naming Consistency5/5

All tool names follow a consistent lowercase snake_case verb-first convention: query_* for data retrieval, and create_data_table, export_data, generate_map, simulate_fusions, and visualize for actions. There is no mixing of camelCase, inconsistent verb styles, or vague duplicate-like naming.

Tool Count5/5

With 11 tools, the server is well within the ideal scope for a specialized electoral analysis toolset. Each query tool covers a coherent data domain, while mapping, visualization, export, table formatting, and simulation cover distinct workflow needs without redundancy.

Completeness5/5

The tool surface covers the full electoral analysis workflow: querying results and administrative divisions, socio-demographic profiles, elected officials, municipal candidate data, mapping, custom visualizations, tabular formatting, export, and fusion simulation. The advanced panel actions in query_elections plus the read-only SQL fallback fill most conceivable gaps.

Resources