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deployment_logs

Retrieve logs for a specific deployment to debug issues, monitor progress, and check build output in Railway.app infrastructure.

Instructions

[API] Get logs for a specific deployment

⚡️ Best for: ✓ Debugging deployment issues ✓ Monitoring deployment progress ✓ Checking build output

⚠️ Not for: × Service runtime logs × Database logs

→ Prerequisites: deployment_list

→ Next steps: deployment_status

→ Related: service_info, deployment_trigger

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deploymentIdYesID of the deployment to get logs for
limitNoMaximum number of log entries to fetch

Implementation Reference

  • Handler function that executes the deployment_logs tool logic by calling the deployment service.
    async ({ deploymentId, limit = 100 }) => {
      return deploymentService.getDeploymentLogs(deploymentId, limit);
    }
  • Input schema using Zod for validating parameters of the deployment_logs tool.
    {
      deploymentId: z.string().describe("ID of the deployment to get logs for"),
      limit: z.number().optional().describe("Maximum number of log entries to fetch")
    },
  • Creation of the deployment_logs tool object including name, description, schema, and handler using createTool.
      "deployment_logs",
      formatToolDescription({
        type: 'API',
        description: "Get logs for a specific deployment",
        bestFor: [
          "Debugging deployment issues",
          "Monitoring deployment progress",
          "Checking build output"
        ],
        notFor: [
          "Service runtime logs",
          "Database logs"
        ],
        relations: {
          prerequisites: ["deployment_list"],
          nextSteps: ["deployment_status"],
          related: ["service_info", "deployment_trigger"]
        }
      }),
      {
        deploymentId: z.string().describe("ID of the deployment to get logs for"),
        limit: z.number().optional().describe("Maximum number of log entries to fetch")
      },
      async ({ deploymentId, limit = 100 }) => {
        return deploymentService.getDeploymentLogs(deploymentId, limit);
      }
    ),
  • Registration function that registers all tools, including deploymentTools containing deployment_logs, to the MCP server.
    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
        );
      });
    } 
  • Supporting service method that fetches build and deployment logs, formats them, and returns a response. Called by the tool handler.
    async getDeploymentLogs(deploymentId: string, limit: number = 100) {
      try {
        // Wait for 5 seconds before fetching logs
        // Seems like the LLMs like to call this function multiple times in combination
        // with the health check function, so we need to wait a bit to avoid rate limiting
        await new Promise(resolve => setTimeout(resolve, 5000));
        const buildLogs = await this.client.deployments.getBuildLogs(deploymentId, limit);
        const deploymentLogs = await this.client.deployments.getDeploymentLogs(deploymentId, limit);
    
        const logs: DeploymentLog[] = [...buildLogs.map(log => ({ ...log, type: 'build' as const })), ...deploymentLogs.map(log => ({ ...log, type: 'deployment' as const })) ];
    
        if (logs.length === 0) {
          return createSuccessResponse({
            text: `No logs found for deployment ${deploymentId}`,
            data: []
          });
        }
    
        const formattedLogs = logs.map(log => {
          const timestamp = new Date(log.timestamp).toLocaleString();
          const severity = log.severity.toLowerCase();
          const emoji = severity === 'error' ? '❌' : severity === 'warn' ? '⚠️' : '📝';
          return `[${log.type}] [${timestamp}] ${emoji} ${log.message}`;
        }).join('\n');
    
        return createSuccessResponse({
          text: formattedLogs,
          data: logs
        });
      } catch (error) {
        return createErrorResponse(`Error fetching logs: ${formatError(error)}`);
      }
    }
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It implies a read-only operation ('Get logs') but doesn't explicitly state permissions, rate limits, or pagination behavior. It adds some context about log types (not for service/database logs) but lacks details on output format or error handling.

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 with bullet points and icons, front-loading the core purpose. Every sentence adds value: the main action, usage scenarios, exclusions, and related tools. No wasted words or redundancy.

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 annotations and no output schema, the description does well by covering purpose, usage, and exclusions. However, it lacks details on behavioral aspects like permissions or output format, which would be helpful for a tool with no structured output. It's mostly complete but has minor gaps.

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 both parameters (deploymentId and limit). The description doesn't add any parameter-specific details beyond what's in the schema, such as format examples or constraints. Baseline 3 is appropriate when schema coverage is high.

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 verb ('Get logs') and resource ('for a specific deployment'), distinguishing it from siblings like deployment_status (status vs. logs) and service_info (service vs. deployment). It specifies the exact scope of logs (deployment-related) rather than being generic.

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?

It provides explicit guidance with 'Best for' (debugging, monitoring, checking build output) and 'Not for' (service runtime logs, database logs), plus prerequisites (deployment_list), next steps (deployment_status), and related tools (service_info, deployment_trigger). This clearly defines when to use this tool versus alternatives.

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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