query_sociodemRequête les données socio-démographiques au niveau bureau de vote.
Données INSEE RP 2022 + Filosofi 2021, interpolées IRIS→BDV.
Actions : list_variables, profile, indices, rank, compare.
| Name | Required | Description | Default |
|---|---|---|---|
| order | No | ||
| action | Yes | ||
| variable | No | ||
| commune_code | No | ||
| commune_codes | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read operations. The description adds value by specifying data sources (INSEE, Filosofi), interpolation method (IRIS→BDV), and available actions, which enriches behavioral understanding beyond annotations.
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 brief and uses a bullet list for actions, making it scannable. Every sentence adds information without redundancy. A slightly more structured format could improve readability.
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?
The description covers the core purpose, data sources, and actions, which is adequate for a read-only query tool with an output schema. However, the lack of parameter documentation and usage guidance reduces completeness for the tool's complexity (5 params, multiple actions).
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?
Schema description coverage is 0%, and the description only mentions the 'action' parameter explicitly. The other four parameters (order, variable, commune_code, commune_codes) are not explained, leaving their purpose and format unclear. This is a significant gap given the number of parameters.
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 the tool queries socio-demographic data at polling station level, specifies data sources (INSEE RP 2022, Filosofi 2021), and lists available actions. It distinguishes from sibling tools by domain (socio-demographic vs. admin, elections, etc.).
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?
While the description mentions the tool's purpose and actions, it provides no explicit guidance on when to use this tool versus alternatives like query_admin or query_elections. Usage context is implied by the domain but not clarified.
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.