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dokploy_ai_create

dokploy_ai_create

Configure AI model integration for the Dokploy MCP Server by setting up API connections, specifying models, and enabling AI capabilities for self-hosted PaaS resources.

Instructions

[ai] ai.create (POST)

Parameters:

  • name (string, required)

  • apiUrl (string, required)

  • apiKey (string, required)

  • model (string, required)

  • isEnabled (boolean, required)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
apiUrlYes
apiKeyYes
modelYes
isEnabledYes
Behavior3/5

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

Annotations indicate this is a write operation (readOnlyHint=false), non-destructive, non-idempotent, and open-world. The description doesn't add behavioral context beyond what annotations provide (e.g., no mention of authentication requirements, rate limits, or what happens on duplicate creation). However, it doesn't contradict annotations, so it meets the baseline for having annotations.

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

Conciseness3/5

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

The description is brief but inefficiently structured. It starts with redundant tool naming, then lists parameters without context. While concise, it lacks front-loaded purpose and wastes space on basic parameter types that the schema already defines.

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

Completeness2/5

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

For a creation tool with 5 required parameters, 0% schema coverage, no output schema, and no annotations explaining behavior, the description is inadequate. It doesn't explain what resource is created, what the parameters mean, or what to expect upon success/failure, leaving significant gaps for an agent to use it correctly.

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

Parameters2/5

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 lists parameter names and types but provides no semantic meaning (e.g., what 'apiUrl' should point to, what 'model' refers to, or what 'isEnabled' controls). This adds minimal value beyond the schema's structural information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool name '[ai] ai.create (POST)' but doesn't explain what it actually creates. It mentions parameters but not the resource being created (e.g., an AI configuration, model deployment, or integration). While 'create' implies a creation action, the specific purpose remains vague without stating what is being created in the Dokploy context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. With sibling tools like dokploy_ai_get, dokploy_ai_update, and dokploy_ai_delete, the description doesn't differentiate this create operation from other AI-related operations or explain prerequisites (e.g., whether an existing AI configuration is needed).

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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