simulate_fusionsSimule les scénarios de fusion de listes municipales entre T1 et T2.
Actions : 'status' (classer les listes après T1), 'simulate' (projections T2).
Uniquement pour les élections municipales.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| election_id | Yes | ||
| report_rate | No | ||
| commune_code | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds domain-specific context (municipal elections, action types) but does not disclose additional behavioral traits beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose. The listing of actions is efficient, though the formatting with a colon and newline is slightly informal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of fusion simulation and 4 parameters, the description is adequate but not complete. It explains actions and domain restriction but omits details on parameters like 'report_rate'. An output schema exists, so return values are not required, but parameter guidance is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by explaining the 'action' parameter and its two values ('status', 'simulate'). However, it fails to describe 'election_id' and 'report_rate', leaving gaps for the agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool simulates fusion scenarios of municipal lists between T1 and T2, listing specific actions ('status' and 'simulate'). It explicitly limits usage to municipal elections, making the purpose distinct from sibling tools like query_elections or generate_map.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is only for municipal elections, providing a clear context for when to use. However, it does not exclude alternative tools or explicitly state when not to use, leaving the agent to infer from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.