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deployment_trigger

Initiate new deployments for Railway services to apply code changes, configuration updates, or roll back to previous states using specific commit SHAs.

Instructions

[API] Trigger a new deployment for a service

⚡️ Best for: ✓ Deploying code changes ✓ Applying configuration updates ✓ Rolling back to previous states

⚠️ Not for: × Restarting services (use service_restart) × Updating service config (use service_update) × Database changes

→ Prerequisites: service_list

→ Alternatives: service_restart

→ Next steps: deployment_logs, deployment_status

→ Related: variable_set, service_update

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesID of the project
serviceIdYesID of the service
environmentIdYesID of the environment
commitShaYesSpecific commit SHA from the Git repository

Implementation Reference

  • Handler function for the deployment_trigger tool, which delegates to deploymentService.triggerDeployment
    async ({ projectId, serviceId, environmentId, commitSha }) => {
      return deploymentService.triggerDeployment(projectId, serviceId, environmentId, commitSha);
    }
  • Zod schema defining input parameters for the deployment_trigger tool
    {
      projectId: z.string().describe("ID of the project"),
      serviceId: z.string().describe("ID of the service"),
      environmentId: z.string().describe("ID of the environment"),
      commitSha: z.string().describe("Specific commit SHA from the Git repository")
    },
  • createTool call that registers the deployment_trigger tool, including description, schema, and handler
    createTool(
      "deployment_trigger",
      formatToolDescription({
        type: 'API',
        description: "Trigger a new deployment for a service",
        bestFor: [
          "Deploying code changes",
          "Applying configuration updates",
          "Rolling back to previous states"
        ],
        notFor: [
          "Restarting services (use service_restart)",
          "Updating service config (use service_update)",
          "Database changes"
        ],
        relations: {
          prerequisites: ["service_list"],
          nextSteps: ["deployment_logs", "deployment_status"],
          alternatives: ["service_restart"],
          related: ["variable_set", "service_update"]
        }
      }),
      {
        projectId: z.string().describe("ID of the project"),
        serviceId: z.string().describe("ID of the service"),
        environmentId: z.string().describe("ID of the environment"),
        commitSha: z.string().describe("Specific commit SHA from the Git repository")
      },
      async ({ projectId, serviceId, environmentId, commitSha }) => {
        return deploymentService.triggerDeployment(projectId, serviceId, environmentId, commitSha);
      }
    ),
  • Registers all tools with the MCP server, including the deploymentTools array which contains deployment_trigger
    export function registerAllTools(server: McpServer) {
      // Collect all tools
      const allTools = [
        ...databaseTools,
        ...deploymentTools,
        ...domainTools,
        ...projectTools,
        ...serviceTools,
        ...tcpProxyTools,
        ...variableTools,
        ...configTools,
        ...volumeTools,
        ...templateTools,
      ] as Tool[];
    
      // Register each tool with the server
      allTools.forEach((tool) => {
        server.tool(
          ...tool
        );
      });
    } 
  • DeploymentService.triggerDeployment method called by the tool handler, wraps the repo call and formats response
    async triggerDeployment(projectId: string, serviceId: string, environmentId: string, commitSha?: string) {
      try {
        // Wait for 5 seconds before triggering deployment
        // Seems like the LLMs like to call this function multiple times in combination
        // with the health check function and the list deployments function
        // so we need to wait a bit to avoid rate limiting
        await new Promise(resolve => setTimeout(resolve, 5000));
        const deploymentId = await this.client.deployments.triggerDeployment({
          serviceId,
          environmentId,
          commitSha
        });
    
        return createSuccessResponse({
          text: `Triggered new deployment (ID: ${deploymentId})`,
          data: { deploymentId }
        });
      } catch (error) {
        return createErrorResponse(`Error triggering deployment: ${formatError(error)}`);
      }
    }
  • TypeScript interface used in repo for DeploymentTriggerInput
    export interface DeploymentTriggerInput {
      commitSha?: string;
      environmentId: string;
      serviceId: string;
    }
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is a deployment-triggering action (implying a write/mutation operation) and provides context about what it's designed for (deploying code changes, applying config updates, rolling back). However, it doesn't explicitly mention potential side effects like service downtime, authentication requirements, or rate limits.

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 well-structured and efficiently organized with clear sections (purpose, best for, not for, prerequisites, alternatives, next steps, related). Every sentence earns its place by providing valuable guidance without redundancy. The information is front-loaded with the core purpose immediately stated.

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?

For a mutation tool with no annotations and no output schema, the description provides substantial context about usage scenarios, exclusions, prerequisites, and related tools. However, it doesn't describe what happens after triggering (e.g., deployment process initiation, return values, or error conditions), leaving some behavioral aspects unspecified.

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 schema already documents all four parameters (projectId, serviceId, environmentId, commitSha) with clear descriptions. The description doesn't add any additional parameter semantics beyond what's in the schema, making the baseline score of 3 appropriate when the schema does the heavy lifting.

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?

The description clearly states the tool's purpose with a specific verb ('Trigger') and resource ('new deployment for a service'), distinguishing it from siblings like service_restart and service_update. The title line '[API] Trigger a new deployment for a service' provides immediate clarity about the core function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance with 'Best for' and 'Not for' sections, naming specific alternatives (service_restart, service_update) and exclusions (restarting services, updating service config, database changes). It also includes prerequisites (service_list) and related tools, offering comprehensive usage context.

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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