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VPS_getProjectLogsV1

Retrieve recent log entries from all services in a Docker Compose project. Use for debugging, monitoring application behavior, and troubleshooting issues across your project stack.

Instructions

Retrieves aggregated log entries from all services within a Docker Compose project.

This endpoint returns recent log output from each container, organized by service name with timestamps. The response contains the last 300 log entries across all services.

Use this for debugging, monitoring application behavior, and troubleshooting issues across your entire project stack.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNameYesDocker Compose project name using alphanumeric characters, dashes, and underscores only
virtualMachineIdYesVirtual Machine ID

Implementation Reference

  • Schema and documentation for the VPS_getProjectLogsV1 tool, defining input parameters (virtualMachineId and projectName) and response type. This is the type definition used for input/output validation in the MCP tool implementation.
      /**
       * Retrieves aggregated log entries from all services within a Docker Compose project. 
    
    This endpoint returns recent log output from each container, organized by service name with timestamps. 
    The response contains the last 300 log entries across all services. 
    
    Use this for debugging, monitoring application behavior, and troubleshooting issues across your entire project stack.
       */
      "VPS_getProjectLogsV1": {
        params: {
          /**
           * Virtual Machine ID
           */
          virtualMachineId: number;
          /**
           * Docker Compose project name using alphanumeric characters, dashes, and underscores only
           */
          projectName: string;
        };
        response: any; // Response structure will depend on the API
      };
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 describes key behavioral traits: it returns recent log output organized by service name with timestamps, includes the last 300 log entries across all services, and is read-only (implied by 'retrieves'). It does not mention rate limits, authentication needs, or error handling, but covers the core functionality well for a read operation.

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 concise, with three sentences that each add value: the first states the purpose, the second details the response format and limit, and the third provides usage guidelines. There is no wasted text, and key information is front-loaded.

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 the tool's moderate complexity (read operation with two parameters), no annotations, and no output schema, the description is mostly complete. It covers purpose, response format, log entry limit, and usage context. However, it lacks details on output structure (e.g., JSON format, field names) and error cases, which would be helpful for an agent to handle the response.

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%, with both parameters (projectName and virtualMachineId) fully described in the schema. The description does not add any parameter-specific information beyond what the schema provides, such as explaining why both parameters are needed or how they relate to log retrieval. Baseline 3 is 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 retrieves aggregated log entries from all services within a Docker Compose project, specifying the verb (retrieves), resource (log entries), and scope (all services within a project). It distinguishes from sibling tools like VPS_getProjectContainersV1 or VPS_getProjectContentsV1 by focusing specifically on logs rather than containers or files.

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

Usage Guidelines4/5

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

The description explicitly states when to use this tool: 'for debugging, monitoring application behavior, and troubleshooting issues across your entire project stack.' However, it does not mention when not to use it or name specific alternatives among the many sibling tools, such as VPS_getMetricsV1 for performance data or VPS_getScanMetricsV1 for security metrics.

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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