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create_integration

Create an org-level provider integration for AI services like OpenAI, Anthropic, or Azure. Specify provider ID and name to generate a new integration that downstream providers and workspaces can use.

Instructions

Create an org-level provider integration. Some backends need provider-specific fields, and the new integration becomes the source for downstream providers and workspace access. Returns the new integration id and slug.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesHuman-readable name for the integration
ai_provider_idYesID of the AI provider (e.g., 'openai', 'anthropic', 'azure-openai', 'aws-bedrock', 'vertex-ai')
slugNoURL-friendly identifier (auto-generated from name if not provided)
keyNoAPI key for the provider (if required)
descriptionNoOptional description of the integration
workspace_idNoWorkspace ID for workspace-scoped integrations
api_versionNoAPI version (for Azure OpenAI)
resource_nameNoResource name (for Azure OpenAI)
deployment_nameNoDeployment name (for Azure OpenAI)
aws_regionNoAWS region (for AWS Bedrock)
aws_access_key_idNoAWS access key ID (for AWS Bedrock)
aws_secret_access_keyNoAWS secret access key (for AWS Bedrock)
vertex_project_idNoGCP project ID (for Vertex AI)
vertex_regionNoGCP region (for Vertex AI)
custom_hostNoCustom base URL for the provider

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYesWhether the tool call succeeded and returned structured data
dataNoStructured success payload when ok is true
errorNoStructured error payload when ok is false
Behavior4/5

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

Annotations indicate non-read-only, non-destructive, non-idempotent, and open-world. Description adds that the integration becomes source for downstream providers and workspace access, providing useful behavior beyond annotations.

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?

Two sentences, each serves a purpose (purpose and context/returns). No unnecessary text, though could be slightly more structured.

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?

Covers creation, side effects, and returns, but lacks information on prerequisites (e.g., permissions) and handling of workspace_id for scoping, given the output schema exists.

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 descriptions cover 100% of parameters, so baseline is 3. Description adds little to parameter meaning beyond noting provider-specific fields exist, but does not specify which fields for which providers.

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?

Clearly states it creates an org-level provider integration, distinguishes from sibling update/delete tools, and specifies return values (id and slug).

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?

Mentions org-level and that some backends need provider-specific fields, but lacks explicit guidance on when to use this tool vs alternatives like create_mcp_integration or workspace-scoped integrations.

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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