create_data_tableCrée un tableau de données formaté à partir de données structurées.
Utile pour présenter des résultats calculés ou combinés. Retourne un tableau texte formaté.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | ||
| title | Yes | ||
| columns | Yes | ||
| description | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide idempotentHint=true and destructiveHint=false. The description adds that it returns a formatted text table, which is useful. No contradictions, but little extra 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences, no fluff, and front-loads the main action. Every sentence adds value.
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 output schema exists, the description still lacks details on parameter format (e.g., how columns and rows should be structured) and output specifics. It is too minimal for a tool with 4 parameters and zero schema descriptions.
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%, so the description must compensate. It only hints at structured data without explaining the structure of 'columns' and 'rows' or the role of each parameter. This is insufficient for correct invocation.
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 creates a formatted data table from structured data using the verb 'Crée' and resource 'tableau de données'. It is specific but does not differentiate from sibling tools like 'export_data' or 'visualize'.
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 useful for presenting calculated or combined results, providing implied usage context. However, it lacks explicit when-to-use, when-not-to-use, or alternative guidance.
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.