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Windows Developer MCP Server

Windows Developer MCP Server

by peeyushcodes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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docker_compose_logs

Get logs from Docker Compose services. Set the compose file, service, and line count to inspect output.

Instructions

Return logs from Docker Compose services.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "docker_compose_logs"
  }
}

Input Schema

NameRequiredDescriptionDefault
fileNoPath to the Compose file.docker-compose.yml
tailNoNumber of lines to show per service. Default: 100.
serviceNoSpecific service name. Leave empty for all services.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full responsibility for behavioral disclosure. It only states that it 'Return[s] logs' without mentioning whether logs are streamed or historical, how many lines are returned, whether it requires the compose file to be present, or whether it performs any side effects. The schema covers parameter details, but the description adds no behavioral context beyond the core action.

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 a single, focused sentence with zero redundancy or fluff. It efficiently captures the essential purpose without wasted words, matching the standard exemplified by high-scoring examples.

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 simple read-only nature of the tool, the full parameter documentation in the schema, and the existence of an output schema (which eliminates the need to describe return values), the description is adequately complete. It states the core function and leaves parameter details to the schema, making it sufficient for basic usage.

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 each parameter (file, tail, service) having a clear description. The tool description itself adds no additional parameter semantics beyond the schema, which is the baseline for good schema coverage.

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 function with a specific verb ('Return') and resource ('logs from Docker Compose services'), effectively distinguishing it from the sibling `docker_logs` tool, which likely targets raw Docker container logs rather than Compose-defined services.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like `docker_logs` or `docker_compose_up`/`down`. It neither states prerequisites (e.g., needing a running Compose project) nor explains scenarios where another tool would be more appropriate.

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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