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docker_top

Read-only

List processes running inside a Docker container from the host without executing commands or opening a TTY, so you can inspect container activity safely.

Instructions

List the processes running inside a Docker container, read from the host. Nothing runs in the container: no exec, no TTY. Each process's command is the kernel's name for it (ps comm, at most 15 characters), never read from its command line, because a token passed on a command line would otherwise be in the result

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": {
      "name": "<name>"
    },
    "name": "docker_top"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesContainer name to inspect
serverNoRemote server name from config (optional, runs locally if omitted)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.22.1

TDQS

A3.7/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/destructiveHint annotations: it discloses that no exec/TTY is used, that commands are the kernel's truncated (<=15 char) 'comm' rather than the command line, and even the security rationale (avoiding leaking tokens passed on a command line). This is exactly the kind of behavioral context annotations cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the core action, then layers constraints and the truncation/security rationale. Every clause carries signal, though the final 'because...' rationale is slightly verbose for the space it occupies.

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?

For a read-only, no-output-schema listing tool, the description covers the important behaviors (host-read, no exec, truncated comm) that an agent would otherwise get wrong. It doesn't describe the other returned fields (pid, user, etc.), a minor gap given no output schema exists.

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 both 'name' and 'server' clearly documented in the schema. The description adds no parameter-level meaning (e.g., name format, server config behavior) beyond what the schema already provides, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('List the processes running inside a Docker container') plus the critical scoping detail that it reads from the host. An agent can distinguish it from host-level 'processes' and from 'docker_stats'/'docker_logs' by the resource being container processes. It stops short of naming those siblings explicitly.

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 explains *how* it operates (read from host, nothing runs in the container) but never says *when* to choose it over 'processes', 'docker_stats', 'docker_inspect', or 'docker_logs'. No exclusions or alternative-selection guidance is provided, leaving routing to inference from the name.

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