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create_integration

Creates an organization-level provider integration, returning a unique ID and slug for downstream use. Supports provider-specific fields for Azure, AWS, Google Vertex AI, and custom hosts.

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
Behavior3/5

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

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=true, which align with the creation behavior. The description adds that the integration 'becomes the source for downstream providers and workspace access,' providing useful context beyond annotations. However, it does not detail side effects, authentication requirements, or rate limits, leaving some gaps.

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?

The description is three sentences, front-loading the core action and including key context about downstream usage and return values. No extraneous detail—every sentence adds value.

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?

Given the complexity of 15 parameters and full schema coverage, the description covers the main purpose, side effects, and return values. The output schema exists and mentions returning id and slug. It is sufficiently complete for an experienced user, though it could briefly note that many parameters are optional.

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?

Input schema has 100% coverage with descriptions for all 15 parameters. The description only adds 'some backends need provider-specific fields' without enriching specific parameter meanings. This meets the baseline of 3 as schema already handles semantics.

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

Purpose4/5

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

The description clearly states it creates an org-level provider integration, specifying that it becomes the source for downstream providers and workspace access. It distinguishes from siblings like create_provider and create_mcp_integration by mentioning org-level scope and downstream usage, though not explicitly naming alternatives.

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?

The description implies usage by stating 'org-level' and mentioning provider-specific fields, but lacks explicit guidance on when to use this tool versus alternatives like create_provider or create_mcp_integration. No explicit when-not-to-use or alternative recommendations.

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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