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<h1>Bridging AI and Network Infrastructure: The Junos MCP Server</h1>
<h2>Introduction</h2>
<p>In the rapidly evolving landscape of network automation, the convergence of artificial intelligence and network infrastructure management has opened new possibilities for operational efficiency. The Junos Model Context Protocol (MCP) server represents a significant advancement in this space, enabling Large Language Models (LLMs) like Github Copilot, Cursor or Anthropic's Claude to interact directly with Juniper network devices through a standardized interface.</p>
<p>This article explores how the Junos MCP server transforms network operations by providing a bridge between AI assistants and Juniper's Junos-powered network infrastructure, making complex network management tasks accessible through natural language interactions.</p>
<h2>Architecture Overview</h2>
<div class="architecture-diagram"><pre>
┌─────────────────────────────────────────────────────────────────────────────────┐
│ User's Development Environment │
│ │
│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────────┐ │
│ │ GitHub Copilot │ │ Claude Desktop │ │ Cursor │ │
│ │ (MCP Client) │ │ (MCP Client) │ │ (MCP Client) │ │
│ └────────┬────────┘ └────────┬────────┘ └────────┬─────────┘ │
│ │ │ │ │
│ └───────────────────────────┴───────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────┐ │
│ │ LLM Layer │ │
│ │ ┌────────┐ ┌──────────┐ │ │
│ │ │ GPT-4 │ │ Claude │ │ │
│ │ │ │ │ Sonnet │ │ │
│ │ └────┬───┘ └────┬─────┘ │ │
│ │ └─────┬─────┘ │ │
│ └─────────────┼────────────────┘ │
│ │ │
│ MCP Protocol Layer │
│ (JSON-RPC over stdio/HTTP) │
└───────────────────────────────────────┼─────────────────────────────────────────┘
│
┌──────────────────┴──────────────────┐
│ Junos MCP Server (jmcp.py) │
│ ┌────────────────────────────────┐ │
│ │ FastMCP Framework │ │
│ │ ┌────────────────────────┐ │ │
│ │ │ Tool Registration │ │ │
│ │ │ - execute_junos_cmd │ │ │
│ │ │ - get_junos_config │ │ │
│ │ │ - junos_config_diff │ │ │
│ │ │ - gather_device_facts │ │ │
│ │ │ - get_router_list │ │ │
│ │ │ - load_and_commit │ │ │
│ │ └────────────────────────┘ │ │
│ └────────────────────────────────┘ │
│ │
│ ┌────────────────────────────────┐ │
│ │ Connection Manager │ │
│ │ - prepare_connection_params │ │
│ │ - _run_junos_cli_command │ │
│ └────────────┬───────────────────┘ │
└───────────────┼─────────────────────┘
│
┌───────────────┴───────────────┐
│ Juniper PyEZ Library │
│ (NETCONF/SSH Communication) │
└───────────────┬───────────────┘
│
┌────────────────────┴────────────────────┐
│ Network Layer │
│ (SSH Port 22 / NETCONF 830) │
└────────────────────┬────────────────────┘
│
┌────────────────────────────┴───────────────────────────┐
│ │
┌────┴──────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌────┴──────┐
│ Junos │ │ Junos │ │ Junos │ │ Junos │ │ Junos │
│ Router │ │ Switch │ │Firewall │ │ QFX │ │ MX │
│ (edge-01) │ │ (core-sw)│ │ (fw-01) │ │(spine-01)│ │ (pe-01) │
└───────────┘ └──────────┘ └──────────┘ └──────────┘ └───────────┘
</pre></div>
<h2>What is the Model Context Protocol?</h2>
<p>The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables seamless integration between AI assistants and external tools and data sources. Think of MCP as a universal translator that allows AI models to understand and interact with various systems, databases, and APIs in a standardized way.</p>
<h2>How the Architecture Works: A Technical Deep Dive</h2>
<h3>1. MCP Client Layer</h3>
<p>MCP clients like GitHub Copilot, Claude Desktop, Cursor or VS Code extensions interact with users and LLMs (GPT-4, Claude Sonnet, etc.). When a user asks a network-related question, the flow begins:</p>
<pre><code>// Example: User asks "Show BGP status on edge-01"
// The LLM generates an MCP tool call:
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "execute_junos_command",
"arguments": {
"router_name": "edge-01",
"command": "show bgp summary"
}
},
"id": "call_123"
}</code></pre>
<h3>2. MCP Protocol Communication</h3>
<p>The MCP protocol uses JSON-RPC 2.0 for communication. Depending on the transport:</p>
<ul>
<li><strong>stdio</strong>: Direct process communication via stdin/stdout</li>
<li><strong>streamable-http</strong>: HTTP-based communication with Server-Sent Events (SSE) for streaming</li>
</ul>
<p>The server implements handlers for standard MCP methods:</p>
<pre><code>@mcp.list_tools()
async def list_tools():
"""Return available tools to the MCP client"""
return [
types.Tool(
name="execute_junos_command",
description="Execute a Junos CLI command",
inputSchema={
"type": "object",
"properties": {
"router_name": {"type": "string"},
"command": {"type": "string"}
},
"required": ["router_name", "command"]
}
),
# ... other tools
]</code></pre>
<h3>3. Junos MCP Server Processing</h3>
<p>The server's core flow for handling a tool call:</p>
<pre><code>@mcp.tool()
def execute_junos_command(router_name: str, command: str) -> str:
"""Execute a Junos CLI command on the specified router"""
if router_name not in router_configs:
return f"Error: Router '{router_name}' not found"
# Prepare connection parameters
conn_params = prepare_connection_params(
router_configs[router_name],
router_name
)
# Execute command via PyEZ
output = _run_junos_cli_command(
device_config=conn_params,
command=command,
timeout=360
)
return output</code></pre>
<h3>4. Connection Management Deep Dive</h3>
<p>The <code>prepare_connection_params</code> function handles authentication flexibility:</p>
<pre><code>def prepare_connection_params(router_config, router_name):
"""Prepare connection parameters based on auth type"""
base_params = {
"host": router_config["ip"],
"port": router_config.get("port", 22),
"user": router_config["username"],
"device_params": {"name": router_name}
}
auth = router_config.get("auth", {})
auth_type = auth.get("type", "password")
if auth_type == "password":
base_params["password"] = auth.get("password")
elif auth_type == "ssh_key":
base_params["ssh_private_key_file"] = auth.get("private_key_path")
passphrase = auth.get("passphrase")
if passphrase:
base_params["ssh_private_key_passphrase"] = passphrase
return base_params</code></pre>
<h3>5. PyEZ and NETCONF Interaction</h3>
<p>The <code>_run_junos_cli_command</code> function leverages PyEZ's Device class:</p>
<pre><code>def _run_junos_cli_command(device_config, command, timeout=360):
"""Execute CLI command using PyEZ with comprehensive error handling"""
from jnpr.junos import Device
from jnpr.junos.exception import ConnectError
try:
# Establish NETCONF session over SSH
with Device(**device_config) as dev:
# PyEZ translates CLI to RPC calls
# Example: "show bgp summary" → get-bgp-summary RPC
result = dev.cli(command, format='text', warning=False)
if isinstance(result, dict) and dev.hostname in result:
return result[dev.hostname]
return result
except ConnectError as e:
log.error(f"Connection failed: {str(e)}")
return f"Connection error: {str(e)}"
except Exception as e:
log.error(f"Command execution failed: {str(e)}")
return f"Error executing command: {str(e)}"</code></pre>
<h3>6. NETCONF Protocol Details</h3>
<p>When PyEZ executes CLI commands using the <code>.cli()</code> method, it:</p>
<ol>
<li><strong>Establishes SSH connection</strong> on port 22 (or 830 for pure NETCONF)</li>
<li><strong>Starts NETCONF subsystem</strong> over SSH</li>
<li><strong>Exchanges capabilities</strong> (hello messages)</li>
<li><strong>Sends CLI command via special RPC</strong>:
<pre><code><!-- CLI: show bgp summary -->
<rpc>
<command format="text">show bgp summary</command>
</rpc></code></pre>
</li>
<li><strong>Receives direct text output</strong> wrapped in XML response:
<pre><code><rpc-reply>
<cli>
<output>
Groups: 2 Peers: 4 Down peers: 0
Table Tot Paths Act Paths Suppressed History Damp State Pending
inet.0 156 156 0 0 0 0
</output>
</cli>
</rpc-reply></code></pre>
</li>
<li><strong>Extracts and returns the raw CLI text</strong> from the <code><output></code> section</li>
</ol>
<h3>7. Configuration Management Flow</h3>
<p>For configuration changes, the server uses PyEZ's configuration management:</p>
<pre><code>@mcp.tool()
async def load_and_commit_config(
router_name: str,
config_content: str,
config_format: str = "set"
) -> str:
"""Load and commit configuration changes"""
with Device(**conn_params) as dev:
with Config(dev, mode='exclusive') as cu:
# Load configuration
cu.load(config_content, format=config_format)
# Show the diff
diff = cu.diff()
# Commit with confirmation timeout
cu.commit(confirm=1) # 1 minute confirmation
# If successful, confirm the commit
cu.commit()
return f"Configuration committed successfully:\n{diff}"</code></pre>
<h3>8. Error Handling and Resilience</h3>
<p>The server implements multiple layers of error handling:</p>
<ul>
<li><strong>Connection timeouts</strong>: Default 360 seconds for long operations</li>
<li><strong>Rollback safety</strong>: Configuration changes use commit confirmation</li>
<li><strong>Graceful degradation</strong>: Connection errors return descriptive messages</li>
<li><strong>Logging</strong>: Comprehensive logging for debugging and audit</li>
</ul>
<h2>Complete Request Flow Example</h2>
<p>Let's trace a complete request through the system:</p>
<h3>Step 1: User Interaction</h3>
<pre><code>User (in GitHub Copilot): "Check if BGP is established on all edge routers"</code></pre>
<h3>Step 2: LLM Processing</h3>
<p>The LLM (GPT-4/Claude) understands the intent and generates MCP calls:</p>
<pre><code>[
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "get_router_list",
"arguments": {}
},
"id": "call_001"
}
]</code></pre>
<h3>Step 3: MCP Server Response</h3>
<pre><code>{
"jsonrpc": "2.0",
"result": {
"content": [
{
"type": "text",
"text": "Available routers: edge-01, edge-02, core-01, spine-01"
}
]
},
"id": "call_001"
}</code></pre>
<h3>Step 4: LLM Filters and Executes</h3>
<p>The LLM identifies edge routers and generates parallel BGP checks:</p>
<pre><code>[
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "execute_junos_command",
"arguments": {
"router_name": "edge-01",
"command": "show bgp summary"
}
},
"id": "call_002"
},
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "execute_junos_command",
"arguments": {
"router_name": "edge-02",
"command": "show bgp summary"
}
},
"id": "call_003"
}
]</code></pre>
<h3>Step 5: Serial Execution</h3>
<p>The MCP server processes both requests serially</p>
<h3>Step 6: NETCONF/PyEZ Execution</h3>
<p>For each router, PyEZ:</p>
<ol>
<li>Opens SSH connection</li>
<li>Initiates NETCONF session</li>
<li>Sends RPC:
<pre><code><rpc>
<command format="text">show bgp summary</command>
</rpc></code></pre>
</li>
<li>Send text output back to LLM via MCP Client</li>
</ol>
<h3>Step 7: Results Aggregation</h3>
<p>The LLM receives all results and summarizes:</p>
<pre><code>BGP Status Summary:
✅ edge-01: All 3 BGP sessions established
- 10.0.0.1: Established for 2d 14:32:10
- 10.0.0.2: Established for 5d 08:15:42
- 10.0.0.3: Established for 1d 22:47:33
❌ edge-02: 1 of 3 BGP sessions down
- 10.0.0.4: Established for 3d 11:22:55
- 10.0.0.5: Idle (Last error: Connection refused)
- 10.0.0.6: Established for 2d 09:18:21
Action Required: BGP session to 10.0.0.5 on edge-02 needs investigation.</code></pre>
<h2>Benefits of MCP for Network Automation</h2>
<ol>
<li><strong>Natural Language Interface</strong>: Network engineers can describe tasks in plain English rather than memorizing complex CLI commands</li>
<li><strong>Standardized Integration</strong>: MCP provides a consistent interface regardless of the underlying network vendor or technology</li>
<li><strong>Enhanced Safety</strong>: Built-in safeguards and clear operation boundaries prevent unintended changes</li>
<li><strong>Contextual Understanding</strong>: AI assistants can maintain context across multiple operations, understanding the broader intent</li>
<li><strong>Reduced Learning Curve</strong>: New team members can be productive faster without deep CLI expertise</li>
</ol>
<h2>The Junos MCP Server: Features and Capabilities</h2>
<p>The Junos MCP server is a Python-based implementation that leverages Juniper's PyEZ library and the FastMCP framework to provide comprehensive network management capabilities. Here are its six core tools:</p>
<h3>1. Execute Junos Commands (<code>execute_junos_command</code>)</h3>
<p>Execute any Junos CLI command on specified routers, from simple status checks to complex operational queries.</p>
<h3>2. Retrieve Configuration (<code>get_junos_config</code>)</h3>
<p>Fetch the complete device configuration with inheritance resolved and comments removed, providing a clean view of the active configuration.</p>
<h3>3. Configuration Comparison (<code>junos_config_diff</code>)</h3>
<p>Compare current configuration with previous versions using rollback numbers, essential for change validation and troubleshooting.</p>
<h3>4. Device Facts Collection (<code>gather_device_facts</code>)</h3>
<p>Collect comprehensive device information including hardware details, software versions, and operational status using PyEZ's facts gathering.</p>
<h3>5. Router Inventory (<code>get_router_list</code>)</h3>
<p>List all available routers from the configuration file, helping users understand their managed infrastructure.</p>
<h3>6. Configuration Management (<code>load_and_commit_config</code>)</h3>
<p>Apply configuration changes supporting multiple formats (set commands, text, or XML), with automatic commit functionality.</p>
<h2>Installation and Setup</h2>
<h3>Prerequisites</h3>
<ul>
<li>Python 3.11 or higher</li>
<li>Access to Juniper Junos devices</li>
<li>SSH connectivity to target devices</li>
</ul>
<h3>Standard Installation</h3>
<pre><code># Clone the repository
git clone https://github.com/Juniper/junos-mcp-server.git
cd junos-mcp-server
# Install dependencies
pip install -r requirements.txt
# Create your device configuration file
cat > devices.json << 'EOF'
{
"router1": {
"ip": "192.168.1.1",
"port": 22,
"username": "admin",
"auth": {
"type": "ssh_key",
"private_key_path": "/path/to/key.pem"
}
}
}
EOF</code></pre>
<h3>Running the Server</h3>
<p>For Claude Desktop integration (stdio transport):</p>
<pre><code>python3.11 jmcp.py -f devices.json -t stdio</code></pre>
<p>For VS Code or other HTTP-based clients:</p>
<pre><code>python3.11 jmcp.py -f devices.json -t streamable-http -H 127.0.0.1 -p 30030</code></pre>
<h3>Docker Deployment</h3>
<pre><code># Build the container
docker build -t junos-mcp-server:latest .
# Run with stdio transport
docker run --rm -it \
-v /path/to/devices.json:/app/config/devices.json \
-v /path/to/ssh/keys:/app/keys \
junos-mcp-server:latest
# Run with HTTP transport
docker run --rm -it \
-v /path/to/devices.json:/app/config/devices.json \
-v /path/to/ssh/keys:/app/keys \
-p 30030:30030 \
junos-mcp-server:latest \
python jmcp.py -f /app/config/devices.json -t streamable-http -H 0.0.0.0</code></pre>
<h2>Usage Examples and Best Practices</h2>
<h3>Example 1: Network Health Check</h3>
<pre><code>User: "Check the interface status on all routers"
AI Assistant: "I'll check the interface status across your network..."
[Executes 'show interfaces terse' on each router and summarizes results]</code></pre>
<h3>Example 2: Configuration Audit</h3>
<pre><code>User: "Compare the current BGP configuration with yesterday's on router1"
AI Assistant: "I'll compare the BGP configuration changes..."
[Uses junos_config_diff to show what changed]</code></pre>
<h3>Example 3: Automated Troubleshooting</h3>
<pre><code>User: "Why is the connection between router1 and router2 down?"
AI Assistant: "Let me investigate the connectivity issue..."
[Checks interface status, BGP sessions, routing tables, and recent configuration changes]</code></pre>
<h3>Best Practices</h3>
<ol>
<li><strong>Start with Read-Only Operations</strong>: Begin with commands that gather information before making changes</li>
<li><strong>Use Descriptive Router Names</strong>: Configure meaningful names in devices.json for clarity</li>
<li><strong>Implement Change Windows</strong>: Restrict configuration changes to approved maintenance windows</li>
<li><strong>Enable Logging</strong>: Monitor all MCP operations for audit trails</li>
<li><strong>Test in Lab First</strong>: Validate all automation scenarios in a test environment</li>
</ol>
<h2>Security Considerations</h2>
<p>Security is paramount when exposing network infrastructure to AI-driven automation:</p>
<h3>Authentication</h3>
<ul>
<li><strong>Always use SSH key authentication</strong> instead of passwords</li>
<li>Store private keys securely with appropriate file permissions</li>
<li>Rotate keys regularly according to security policies</li>
</ul>
<h3>Authorization</h3>
<ul>
<li>Implement least-privilege access for the MCP service account</li>
<li>Use Junos login classes to restrict available commands</li>
<li>Consider read-only access for most operations</li>
</ul>
<h3>Network Security</h3>
<ul>
<li>Run the MCP server within secure network segments</li>
<li>Use firewall rules to restrict access to the MCP server</li>
<li>Implement VPN or secure tunnels for remote access</li>
</ul>
<h3>Operational Security</h3>
<ul>
<li><strong>Review all AI-generated configurations</strong> before allowing execution</li>
<li>Implement approval workflows for configuration changes</li>
<li>Maintain audit logs of all operations</li>
<li>Set appropriate timeouts (default: 360 seconds) to prevent hung sessions</li>
</ul>
<h3>Corporate Governance</h3>
<ul>
<li>Ensure MCP usage aligns with corporate security policies</li>
<li>Document approved use cases and restrictions</li>
<li>Train staff on secure usage practices</li>
<li>Regular security assessments of the deployment</li>
</ul>
<h2>Future Possibilities and Conclusion</h2>
<p>The Junos MCP server represents just the beginning of AI-assisted network operations. Future enhancements could include:</p>
<ul>
<li><strong>Predictive Maintenance</strong>: AI analyzing trends to predict failures</li>
<li><strong>Automated Remediation</strong>: Self-healing networks that fix common issues</li>
<li><strong>Natural Language Policies</strong>: Define network policies in business terms</li>
<li><strong>Multi-Vendor Support</strong>: Extending MCP to work across different vendors</li>
</ul>
<h3>Conclusion</h3>
<p>The Junos MCP server democratizes network management by making sophisticated network operations accessible through natural language. By bridging the gap between AI capabilities and network infrastructure, it enables:</p>
<ul>
<li>Faster incident response through AI-assisted troubleshooting</li>
<li>Reduced configuration errors with AI validation</li>
<li>Improved documentation through AI-generated summaries</li>
<li>Enhanced learning for junior engineers working alongside AI</li>
</ul>
<p>As networks grow in complexity, tools like the Junos MCP server will become essential for maintaining operational efficiency while reducing the cognitive load on network engineers. The future of network operations is not about replacing human expertise but augmenting it with AI capabilities that handle routine tasks, identify patterns, and suggest optimizations—allowing engineers to focus on strategic initiatives and complex problem-solving.</p>
<p>Whether you're managing a small enterprise network or a large service provider infrastructure, the Junos MCP server offers a glimpse into a future where network management is more intuitive, efficient, and accessible than ever before.</p>
<div class="footer">
<p><em>For more information and to contribute to the project, visit the <a href="https://github.com/Juniper/junos-mcp-server">Junos MCP Server GitHub repository</a>.</em></p>
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