pnetlab-mcp-server

get_lab

Fetch the open lab's topology and node status. Use compact mode to view only essential details for large topologies.

Instructions

Get the open lab's info, full topology (nodes/networks/connections) and node status. Use this to see what's running and how nodes are wired.

Pass compact=True to drop cosmetic noise (empty style dicts, second-console fields, empty qemu_options) and thin each node to id/name/type/template/image/ ram/status/console plus an ethernet map of {if -> {name, network_id, suspend}}. Compact mode is recommended for large topologies -- the full payload can be 80-100KB.

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": "get_lab"
  }
}

Input Schema

Name	Required	Description	Default
`compact`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Jul 31, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the effect of compact=True (removing cosmetic noise, thinning node data) and warns about large payloads (80-100KB). It does not explicitly state read-only semantics, but 'get' implies safe read behavior.

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: a concise purpose statement followed by a focused explanation of the compact mode. Every sentence earns its place with no redundant text.

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?

The tool has one optional parameter and an output schema (not shown). The description fully covers the parameter's behavior and note about payload size, making it complete for selection and invocation without needing additional context.

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

Parameters5/5

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

Schema coverage is 0%, but the description thoroughly explains the sole parameter, compact, detailing exactly what is removed and the output structure. This adds substantial meaning beyond the bare boolean schema.

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 explicitly states the tool retrieves the open lab's info, full topology, and node status. It clearly distinguishes itself from siblings like get_node_status by focusing on the entire lab view.

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 provides clear context: 'Use this to see what's running and how nodes are wired.' It does not explicitly mention alternatives or when not to use it, but the use case is well-defined.

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