Omnara

Create model provider config

model_providers_create

Create a model provider config, validate the configured credential where the provider exposes a validation endpoint, then probe the provider's /models endpoint. The result is returned as model_catalog; a failed probe does not fail creation.

Input Schema

Name	Required	Description
`name`	Yes	Human-readable name. Spaces and punctuation are allowed; leading or trailing whitespace and invisible or control characters are not.
`orgID`	Yes	Organization ID (org_...).
`preset`	No	Built-in provider settings: openai, openrouter, or anthropic. A preset fills in api_format, api_variant, base_url, endpoint_path, auth_kind, and auth_options, so omit those when using one.
`headers`	No	Extra HTTP headers sent with every request to this provider. Values are stored and returned as plain text; use secret_headers for keys and tokens.
`base_url`	No	Provider endpoint base URL. Normal API mode requires a public HTTPS endpoint; localhost and loopback endpoints are accepted only when the API is running in insecure dev mode.
`auth_kind`	No	How the credential is sent: bearer_token, api_key_header, or sigv4 (AWS).
`api_format`	No	Wire protocol the model endpoint speaks: openai-responses, openai-chat-completions, or anthropic-messages.
`api_variant`	No	Provider-specific flavor of the API format: default, openrouter, or bedrock.
`auth_options`	No	Non-secret authentication settings. Use an empty object with bearer_token. With api_key_header, set {"header_name":"..."}; Anthropic Messages defaults to {"header_name":"x-api-key"}. With sigv4, set the AWS signing service and region.
`endpoint_path`	No	URL path under base_url. Defaults to /responses for OpenAI Responses, /chat/completions for Chat Completions, and /messages for Anthropic Messages.
`secret_headers`	No	Extra HTTP headers whose values come from org-owned generic secrets, mapping header names to secret references.
`idle_timeout_ms`	No	Maximum wait for response headers or the next response bytes, in milliseconds. Heartbeats count as activity. Defaults to 300000 (5 minutes). Local response processing does not consume this idle budget.
`request_timeout_ms`	No	Total deadline for one provider attempt, including reasoning and response streaming, in milliseconds. Defaults to 3600000 (60 minutes). Retries have separate deadlines.
`credential_secret_id`	Yes	Secret ID (sec_...).

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedOct 17, 2026

addedInput schema / $defs / SecretID / description
```
Added value: +"Secret ID (sec_...)."
```

addedInput schema / properties / api_format / description

Added value: +"Wire protocol the model endpoint speaks: openai-responses, openai-chat-completions, or anthropic-messages."

addedInput schema / properties / api_variant / description

Added value: +"Provider-specific flavor of the API format: default, openrouter, or bedrock."

addedInput schema / properties / auth_kind / description

Added value: +"How the credential is sent: bearer_token, api_key_header, or sigv4 (AWS)."

addedInput schema / properties / credential_secret_id / description
```
Added value: +"Secret ID (sec_...)."
```
addedInput schema / properties / orgID / description
```
Added value: +"Organization ID (org_...)."
```

addedInput schema / properties / preset / description

Added value: +"Built-in provider settings: openai, openrouter, or anthropic. A preset fills in api_format, api_variant, base_url, endpoint_path, auth_kind, and auth_options, so omit those when using one."

AddedOct 13, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations only declare the mutation profile (readOnly=false, destructive=false, idempotent=false); the description adds real behavioral detail beyond that: credential validation against the provider's validation endpoint, a probe of /models, and the important guarantee that a failed probe does not fail creation. It stops short of covering auth/permission requirements or what happens on partial failure.

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?

Two tightly packed sentences with zero filler; the creation act is front-loaded and the failure-tolerance caveat is placed last, where it matters most.

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 14-parameter, nested-object mutation with no output schema, the description usefully names the return artifact (model_catalog) and discloses the validation/probe side effects. It omits credential/permission prerequisites and any pointer to the update path, but the core call-time behavior is covered.

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 description coverage is 100%, so every one of the 14 parameters is already documented, including the preset's role in filling api_format/base_url/auth_kind. The description adds no parameter-level meaning beyond the schema, so the baseline 3 applies.

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 states a specific verb and resource ('Create a model provider config') and distinguishes itself from sibling mutations by describing the creation-time validation and probing sequence. An agent can tell this apart from model_providers_update or model_providers_catalog without opening any schema.

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

Usage Guidelines3/5

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

It implies when creation is appropriate by enumerating what creation entails, but never names alternatives (e.g. model_providers_update for existing configs) or states preconditions such as needing an existing credential secret and orgID. Usage is inferable but not spelled out.

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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Create model provider config

Input Schema

Schema Changelog

TDQS

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