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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)}`);
      }
    }

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 provided, so description carries the burden. It does not disclose behavioral traits like rate limits, pagination, or authentication requirements. For a simple log retrieval, basic transparency is sufficient but could be improved by noting that logs are for deployment builds, not runtime.

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?

Well-structured with bullet points for best uses, not-for, prerequisites, next steps, and related tools. Every sentence adds value, and it is very concise.

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, the description could explain the content or format of logs, but it does not. However, it provides context about usage and related tools, and the tool is simple. Almost complete for its complexity.

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 coverage is 100% with descriptions for both parameters (deploymentId and limit). The description does not add further meaning beyond the schema, so 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?

The description clearly states 'Get logs for a specific deployment' with a specific verb and resource. It distinguishes from siblings like service_info (runtime logs) and deployment_trigger (triggering deployment).

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?

Explicitly lists use cases (debugging, monitoring, checking build output) and non-use cases (service runtime logs, database logs). Also mentions prerequisite (deployment_list) and next step (deployment_status), providing clear context for when to use this tool.

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