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odoo_execute

Call any Odoo model method for workflow actions or custom operations not available through standard CRUD tools.

Instructions

Execute any model method via execute_kw. Use for workflow actions (action_confirm, action_done), custom methods, or anything not covered by CRUD tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
connectionNodefault
modelYes
methodYesMethod name (e.g. 'action_confirm', 'button_validate')
argsNoPositional arguments
kwargsNoKeyword arguments
Behavior3/5

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

No annotations are provided, so the description must convey behavioral traits. It discloses that it can call any model method, but does not explain side effects (e.g., data modification), error handling, or return values. While the potential for mutation is implied, more clarity on safety would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. It front-loads the core action and adds usage context efficiently. Could be slightly more structured with separate sections, but it is very concise and readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters, no output schema, and no annotations, the description is somewhat incomplete. It does not explain the return value format, the purpose of the 'connection' parameter, or how arguments should be structured beyond the schema. It covers usage but lacks operational details needed for safe invocation.

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

Parameters3/5

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

The schema covers 60% of parameters with descriptions (method, args, kwargs), leaving 'connection' and 'model' undocumented. The description adds context by explaining the tool's purpose and giving method examples, but does not elaborate on parameter details like the role of 'connection'. It provides marginal added value beyond the 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 clearly states it executes any model method via execute_kw, with specific examples like workflow actions. It explicitly distinguishes from CRUD tools by stating 'anything not covered by CRUD tools', making it easy for an AI to understand its unique role.

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 guidance on when to use the tool ('workflow actions, custom methods, or anything not covered by CRUD tools'), implicitly excluding standard CRUD operations. However, it lacks explicit 'when not to use' or alternative tool names, and no prerequisites are mentioned.

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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