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openl-tablets

OpenL MCP Server

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Deploy Project to Production

openl_deploy_project

Deploy a project to production by publishing its rules to a configured deployment repository for runtime execution.

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, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with contextjson
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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv1.2.0
    • changedInput schema / properties / response_format / default
      Previous value: -"markdown"New value: +"json"
    • changedInput schema / properties / response_format / description
      Previous value: -"Response format: 'json' for structured data, 'markdown' for human-readable (default), 'markdown_concise' for brief summary (1-2 paragraphs), 'markdown_detailed' for full details with context"New value: +"Response format: 'json' for structured, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with context"
  2. First observedv0.0.0

TDQS

A3.7/5.0
Behavior4/5

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

The openWorldHint annotation is present, and the description adds meaningful context beyond it by disclosing the real-world consequence: rules are published to a deployment repository for runtime execution, meaning the deployment affects production behavior. This is exactly the kind of side-effect disclosure that helps an agent. It stops short of 5 because it doesn't address whether an existing deployment is overwritten or whether this action is reversible.

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?

Three short sentences, each earning its place: the core action, the behavioral consequence, and the critical name-vs-ID gotcha with a concrete example. No filler, no repetition of schema-level defaults.

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 production-deployment tool with openWorldHint and no output schema, the description covers the core semantics well but leaves gaps: no mention of what the response contains, no guidance on when to choose openl_redeploy_project over this tool, and no indication of whether deploying over an existing deployment is allowed. The params are well-documented in the schema, which carries some weight here, but the deploy-vs-redeploy decision is an important missing piece.

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 the baseline of 3 applies — every parameter already carries a rich description, including the exact source of projectId and the name-vs-ID rule for productionRepositoryId. The description's repository-name warning repeats what the schema already documents, adding no net new meaning for parameters.

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 states a specific verb ('Deploy'), resource ('a project'), and target ('production environment'), and adds what the operation actually accomplishes ('Publishes rules to a deployment repository for runtime execution'). It is clearly distinguishable from most siblings, but it does not explicitly differentiate itself from the closely related openl_redeploy_project, so it misses the top score.

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 core usage context is implied clearly: use this when deploying a project to production and publishing its rules for runtime execution. However, no alternatives or exclusions are named — notably there is no 'use openl_redeploy_project for existing deployments' guidance, which an agent would need given that sibling exists. The practical 'use repository name, not ID' advice is helpful but is a parameter-format rule, not a tool-selection rule.

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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