Skip to main content
Glama

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;
    }

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions 'API' but does not disclose behavioral traits like idempotency, rate limits, or whether the deployment is synchronous. It does note prerequisites, but lacks depth on what happens on trigger.

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?

Description is extremely concise and well-structured using bullet points, emojis, and clear section headers. Every sentence adds value, making it easy for an AI agent to parse quickly.

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 no output schema and no annotations, the description provides substantial context: purpose, usage, alternatives, prerequisites, next steps. However, it lacks details on return values or potential side effects, leaving minor gaps in completeness for a mutation tool.

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 parameters. The description does not add additional meaning beyond the schema's parameter descriptions. Baseline 3 is appropriate.

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?

Description clearly states the action 'Trigger a new deployment for a service' and lists specific use cases (deploying code, config updates, rollbacks). It distinguishes from siblings by explicitly stating what it is not for, e.g., 'Restarting services (use service_restart)'.

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?

Provides explicit 'Best for' and 'Not for' sections with alternative tool names. Also includes prerequisites ('service_list') and next steps ('deployment_logs, deployment_status'), giving clear guidance on when and how to use the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.