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docker-mcp-server

docker-mcp-server

by L337-org
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Python
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swarm_join_tokens

Read-only

Retrieve the current worker and manager join tokens for a Docker swarm. These secret credentials are required to authorize a new node to join the swarm via swarm_join.

Instructions

Return the swarm's worker and manager join tokens.

These are the tokens a new node passes to swarm_join - without one, swarm_join cannot be called, so this closes the init -> join loop. The tokens are secret bearer credentials (anyone holding the manager token can join as a manager); treat the result as sensitive and avoid logging it. Reads swarm.attrs["JoinTokens"] after a reload, so it always reflects the current tokens.

Returns: dict: {"Worker": , "Manager": }

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": "swarm_join_tokens"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.0

TDQS

A4.7/5.0
Behavior5/5

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

Even with readOnlyHint=true and destructiveHint=false already provided, the description adds valuable behavioral context: the tokens are secret bearer credentials, should be treated as sensitive and not logged, and the tool reads swarm.attrs after a reload to always reflect current tokens. This goes well beyond the annotations.

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 compact and front-loaded with the core purpose, then supplies essential security and implementation details without fluff. Every sentence adds meaningful information for the agent.

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

Completeness5/5

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

With zero parametersholistically, no output schema, and annotations covering safety, the description is complete: it states what is returned, why the tokens matter, how to treat them, and that the value is freshly reloaded. An agent has everything needed to call and interpret this tool correctly.

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

Parameters4/5

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

The tool has zero parameters)Skip, so the schema already covers everything. The description does not need to explain parameters; it instead documents the return format, which is helpful. Baseline 4 is appropriate for a zero-parameter tool that adds no conflicting or extraneous parameter information.

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?

Description opens with a clear, specific action-resource pairing: 'Return the swarm's worker and manager join tokens'. This distinguishes it from sibling tools like swarm_inspect and swarm_unlock_key by naming exactly which data it returns.

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 clearly contextualizes when to use the tool: it closes the init -> join loop and explains that swarm_join cannot be called without one of these tokens. It does not explicitly list exclusions or alternative sibling tools, but the usage context is strong enough for an agent to select it appropriately.

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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