Get up and running
in minutes.

Prerequisites

Installation

git clone https://github.com/Mats2208/MCP-Packet-Tracer.git
cd MCP-Packet-Tracer

pip install -e .

python -m pytest tests/ -v

Start the Server

$ python -m src.packet_tracer_mcp
INFO  Packet Tracer MCP v0.4.0
INFO  Transport: streamable-http
INFO  Listening on http://127.0.0.1:39000/mcp

The server starts on port 39000 using streamable HTTP transport. For legacy stdio mode, use --stdio flag.

# Alternative: stdio mode for debugging
python -m src.packet_tracer_mcp --stdio

Connect a Client

{
  "servers": {
    "packet-tracer": {
      "url": "http://127.0.0.1:39000/mcp"
    }
  }
}

{
  "mcpServers": {
    "packet-tracer": {
      "url": "http://127.0.0.1:39000/mcp"
    }
  }
}

Your First Topology

Once connected, ask your AI agent to build a topology. You can also call tools directly:

Quick Build (one-shot)

pt_full_build(
  routers=2,
  pcs_per_lan=2,
  dhcp=True,
  routing="ospf",
  has_wan=True
)

# Returns: plan + script + configs + explanation

Step by Step

# 1. Generate plan
plan = pt_plan_topology(routers=2, pcs_per_lan=2, routing="static")

# 2. Validate
pt_validate_plan(plan)

# 3. Auto-fix if needed
pt_fix_plan(plan)

# 4. Generate artifacts
pt_generate_script(plan)    # PTBuilder JavaScript
pt_generate_configs(plan)   # IOS CLI configs

# 5. Get explanation
pt_explain_plan(plan)       # Natural language summary

Export to Files

# Save everything to a directory
pt_export(plan, path="./my_lab/")

Live Deploy Setup

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# Check bridge connection
pt_bridge_status()

# Deploy a plan in real-time
pt_live_deploy(plan)

Project Management

Save and load topologies for later reuse.

# List saved projects
pt_list_projects()

# Load a previously saved project
pt_load_project("my_lab")

# Export creates a project automatically
pt_export(plan, path="./my_lab/")

Troubleshooting