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Create model provider config

model_providers_create

Create a model provider config, validate its credential when supported, then probe the /models endpoint to return a model catalog; failed probes do not block creation.

Instructions

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

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable name. Spaces and punctuation are allowed; leading or trailing whitespace and invisible or control characters are not.
orgIDYes
presetNo
headersNoExtra 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_urlNoProvider 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_kindNo
api_formatNo
api_variantNo
auth_optionsNoNon-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_pathNoURL path under base_url. Defaults to /responses for OpenAI Responses, /chat/completions for Chat Completions, and /messages for Anthropic Messages.
secret_headersNoExtra HTTP headers whose values come from org-owned generic secrets, mapping header names to secret references.
idle_timeout_msNoMaximum 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_msNoTotal deadline for one provider attempt, including reasoning and response streaming, in milliseconds. Defaults to 3600000 (60 minutes). Retries have separate deadlines.
credential_secret_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare this is a non-readonly, non-idempotent, non-destructive create. The description adds real behavioral value beyond them: it discloses that the credential is validated when the provider exposes a validation endpoint, that /models is probed, that the catalog comes back as model_catalog, and that a failed probe does not fail creation. It stops short of stating permission requirements, duplicate-name failure behavior, or reversibility.

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 tight sentences, front-loaded with the primary action and then the side effects and their failure semantics. No filler or restatement of the name.

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?

For a 14-parameter tool with nested objects (auth_options, secret_headers, headers) and no output schema, the description covers the creation flow and return shape at a high level but leaves the parameter surface entirely to the schema. It is adequate for orientation, not for confident invocation of a complex configuration object.

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?

Fourteen parameters with only 57% schema description coverage, and the description mentions none of them. Parameters like preset, api_format, api_variant, auth_kind, orgID, and credential_secret_id are left to the schema alone, and the description does nothing to compensate for the uncovered ones or to explain how base_url/auth_kind/auth_options interact.

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?

States a specific verb and resource ('Create a model provider config') and goes further by naming the two post-creation behaviors it triggers (credential validation, /models probe). An agent can distinguish this from model_providers_update or model_providers_get 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?

Usage is implied by the name and the create semantics, but the description never says when to reach for this versus model_providers_update, when a `preset` should be used instead of explicit base_url/auth fields, or what the prerequisites (e.g. an existing secret) are. No exclusions or alternatives are offered.

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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