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odoo_dashboard_create

Create or replace Odoo dashboards. Define charts with JSON, get real-time recalculated graphs, and validate before writing, even in read-only mode.

Instructions

Creer (ou remplacer via dashboard_id) un tableau de bord. Graphiques recalcules en direct par Odoo.

graphiques, liste JSON : {"titre":"CA par mois","model":"sale.order","groupby":["date_order:month"], "mesure":"amount_untaxed","type":"line","domaine":[["state","=","sale"]], "pleine_largeur":true} type=bar|line|pie ; mesure "__count" pour compter ; date : champ:month (day/week/quarter/year).

Valide avant ecriture, y compris en lecture seule. rubrique creee si absente.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nomYes
rubriqueNo
graphiquesYes
sous_titreNo
dashboard_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the burden and does a good job: it discloses mutation/replacement behavior, live recalculation of charts, validation before writing (including read-only mode), and auto-creation of `rubrique`. It does not mention permissions or failure modes, but the disclosed behaviors are substantial and accurate.

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 compact and front-loaded with the purpose, then quickly gives an illustrative JSON payload and key behavioral constraints. Every sentence adds value, and the structure is easy to scan.

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?

For a tool with a complex JSON parameter and an output schema, the description covers the essential format, replacement semantics, and validation behavior. Minor gaps (e.g., `sous_titre` semantics, exact return value) are partially offset by the output schema and the detailed example.

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

Parameters4/5

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

Schema description coverage is 0%, so the description compensates well. It provides a detailed JSON schema for `graphiques`, explains value domains for `type`, `mesure`, and date grouping, and clarifies `dashboard_id` (replacement) and `rubrique` (auto-created). `sous_titre` is not explained, but the main complex parameters are covered.

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 uses a specific verb+resource ('Creer ... un tableau de bord') and clarifies that it can also replace an existing dashboard via `dashboard_id`. It clearly distinguishes this tool from dashboard listing/inspection siblings and generic Odoo create 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 gives clear operational context: create or replace a dashboard, with validation happening before write even in read-only mode. It does not explicitly name alternatives or exclusions, but the replacement and validation notes provide strong usage guidance.

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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