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PlugLayer MCP Server

PlugLayer MCP Server

Official
by pluglayer
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Python
Hybrid

deploy_compose

Decompose docker-compose configs into deployable units, provision databases via Data Layer, deploy services as separate apps, and provide archives for local image builds.

Instructions

Analyze docker-compose.yml, split it into separate deploy units, provision known databases through Data Layer, and deploy the remaining services as separate apps. If any service uses a local Docker build, provide local_image_archives keyed by service name after building and exporting those images.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>",
      "compose_yaml": "<compose_yaml>"
    },
    "name": "deploy_compose"
  }
}

Input Schema

NameRequiredDescriptionDefault
app_nameNo
project_idYes
route_slugNo
compose_yamlYes
compute_placementNopersonal
redeploy_strategyNorecreate
local_image_archivesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.5

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and provides good transparency: it explains the multi-step process, mentions the Data Layer for databases, and specifies the local build behavior with local_image_archives. It doesn't cover idempotency or permissions, but the key side effects and conditional behavior are disclosed.

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 two sentences with every clause serving a purpose: the first sentence encloses the entire workflow, and the second adds a conditional instruction. No fluff or redundancy exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description offers a good high-level overview and the local build nuance, but for a complex tool with 7 parameters, no annotations, and many sibling tools, it lacks context about prerequisites, idempotency, and relationship to planning/analyzing tools. The presence of an output schema mitigates return-value explanations but not these gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description only elaborates on local_image_archives and indirectly compose_yaml. Parameters like project_id, app_name, route_slug, compute_placement, and redeploy_strategy are not explained beyond their schema titles, leaving a significant gap for a tool with 7 parameters.

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: analyze docker-compose.yml, split into deploy units, provision databases, and deploy the remaining services as apps. This distinguishes it from sibling tools like deploy_image which handles single images, making the purpose unambiguous.

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

Usage Guidelines3/5

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

The description implies use for docker-compose files by mentioning 'docker-compose.yml' and the deployment flow, but it does not explicitly mention alternatives or when not to use this tool. Sibling tools like analyze_compose_deploy_plan are not referenced, so guidance is only implied.

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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