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Deploy Project to Production

openl_deploy_project
Idempotent

Deploys a project to production by publishing rules to a specified deployment repository for runtime execution. Use the production repository's display name.

Instructions

Deploy a project to production environment. Publishes rules to a deployment repository for runtime execution. Use production repository name (not ID) - e.g., 'Production Deployment' instead of 'production-deploy'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentNoDeployment reason comment (e.g., 'Deploy version 1.2.0', 'Production release')
projectIdYesProject ID to deploy. Use the exact 'projectId' value from openl_list_projects() response.
deploymentNameYesName for the deployment (e.g., 'InsuranceRules', 'AutoPremium'). This will be the deployment identifier.
response_formatNoResponse format: 'json' for structured data, 'markdown' for human-readable (default), 'markdown_concise' for brief summary (1-2 paragraphs), 'markdown_detailed' for full details with contextmarkdown
productionRepositoryIdYesTarget production repository name (display name, not ID). Use the 'name' field from openl_list_deploy_repositories() response (e.g., if list_deploy_repositories returns {id: 'production-deploy', name: 'Production Deployment'}, use 'Production Deployment' here, NOT 'production-deploy'). Must be configured in OpenL Studio.
Behavior2/5

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

Annotations already indicate idempotent and open world. Description does not add behavioral context such as whether previous deployments are overwritten, permission requirements, or what happens on success/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 sentences, front-loaded with purpose, followed by a key usage note. No wasted words.

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?

Does not describe return value, deployment lifecycle, or how to verify success. For a production deployment tool, more context (e.g., 'Returns deployment ID') is expected.

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 covers all parameters with descriptions (100% coverage). Description reinforces one point about repository naming but adds no substantial new meaning beyond what schema provides.

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?

Clearly states the verb 'Deploy', resource 'a project', and target 'production environment'. However, it does not differentiate from sibling tool 'openl_redeploy_project', which is a closely related action.

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?

Provides specific guidance on using repository name instead of ID, but does not explain when to use this tool over alternatives like openl_redeploy_project, or mention prerequisites like project being open.

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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