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external_models_create

Create an external model by specifying provider, task, and provider-specific configuration for serving LLMs.

Instructions

Create an external model (POST /api/2.0/external-models).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesExternal model name
providerYesProvider, e.g. 'openai', 'anthropic', 'cohere', 'databricks'
taskYesllm/v1/chat | llm/v1/completions | llm/v1/embeddings | ...
configYesProvider-specific config. Typical fields: ``api_key_secret``, ``api_base``, ``model_name`` (and provider-specific options).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

Annotations already indicate write operation (readOnlyHint: false). The description adds no further behavioral context such as idempotency, error handling, or required permissions, falling short of providing value beyond the annotation.

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 a single sentence that includes the HTTP method and endpoint, which is efficient and front-loaded. However, it could be slightly expanded to provide minimal context without becoming verbose.

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?

Given the complexity of creating an external model with provider-specific configuration, the description is insufficient. It lacks explanation of typical config fields, required secrets, or when to use external models. The output schema might help, but the description itself is incomplete.

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?

Schema coverage is 100% with clear descriptions for all 4 parameters. The tool description does not add any additional meaning beyond what is already in the schema, meeting the baseline expectation.

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 the verb 'Create' and the resource 'external model', and includes the HTTP endpoint, which distinguishes it from sibling tools like external_models_delete, external_models_get, etc.

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, nor any precondition or context. The description is too brief to help an agent decide.

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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