MCP-based Network Measurement Framework: Using Model Context Protocol for Intelligent Network Measurement
draft-zm-rtgwg-mcp-network-measurement-00

Network Working Group                                            G. Zeng
Internet-Draft                                                    J. Mao
Intended status: Informational                                    Huawei
Expires: 23 April 2026                                   20 October 2025

 MCP-based Network Measurement Framework: Using Model Context Protocol
                  for Intelligent Network Measurement
               draft-zm-rtgwg-mcp-network-measurement-00

Abstract

   This document proposes a framework for intelligent network
   measurement using the Model Context Protocol (MCP).  By treating
   network devices as MCP servers and network controllers as MCP
   clients, this framework enables natural language-driven, AI-assisted
   network measurement operations.  The framework leverages MCP's
   standardized communication protocol to provide real-time network
   performance monitoring, intelligent fault diagnosis, topology
   discovery, and automated measurement workflows.  This document
   describes the architecture, use cases, and security considerations
   for implementing MCP-based network measurement systems.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  MCP-Based Network Measurement Architecture  . . . . . . . . .   3
     3.1.  Architectural Components  . . . . . . . . . . . . . . . .   3
     3.2.  Communication Flow  . . . . . . . . . . . . . . . . . . .   4
     3.3.  MCP Server Capabilities . . . . . . . . . . . . . . . . .   4
     3.4.  MCP Client Capabilities . . . . . . . . . . . . . . . . .   5
   4.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Real-time Network Performance Monitoring  . . . . . . . .   5
     4.2.  Intelligent Fault Diagnosis . . . . . . . . . . . . . . .   5
     4.3.  Network Topology Discovery  . . . . . . . . . . . . . . .   6
     4.4.  Capacity Planning and Trend Analysis  . . . . . . . . . .   6
     4.5.  Security Incident Response Measurement  . . . . . . . . .   6
   5.  Protocol Operations . . . . . . . . . . . . . . . . . . . . .   6
     5.1.  Measurement Request Format  . . . . . . . . . . . . . . .   6
     5.2.  Measurement Response Format . . . . . . . . . . . . . . .   7
     5.3.  Error Handling  . . . . . . . . . . . . . . . . . . . . .   7
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   8
     6.1.  Authentication and Authorization  . . . . . . . . . . . .   8
     6.2.  Data Privacy and Confidentiality  . . . . . . . . . . . .   8
     6.3.  Measurement Tool Security . . . . . . . . . . . . . . . .   8
     6.4.  AI/LLM Security Considerations  . . . . . . . . . . . . .   9
     6.5.  Network Device Security . . . . . . . . . . . . . . . . .   9
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   8.  Normative References  . . . . . . . . . . . . . . . . . . . .   9
   9.  Informative References  . . . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   Traditional network measurement approaches often require specialized
   tools, complex configurations, and expert knowledge.  As networks
   grow in complexity and scale, there is an increasing need for more
   intelligent and automated measurement solutions.  The Model Context
   Protocol (MCP) provides a standardized framework for enabling
   communication between AI systems and external data sources.

   This document proposes leveraging MCP to create an intelligent
   network measurement framework where:

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   *  Network devices (routers, switches, firewalls) act as MCP servers

   *  Network controllers or management systems act as MCP clients

   *  Natural language queries drive measurement operations

   *  AI systems assist in analysis and decision-making

   The key benefits of this approach include:

   *  *Natural Language Interface*: Network operators can perform
      measurements using natural language queries

   *  *AI-Assisted Analysis*: Intelligent analysis of measurement
      results and anomaly detection

   *  *Standardized Communication*: Uniform protocol across different
      vendor devices

   *  *Automated Workflows*: Reduced manual intervention in measurement
      processes

2.  Terminology

   The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”,
   “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “NOT RECOMMENDED”, “MAY”, and
   “OPTIONAL” in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

   *MCP Server*: A network device that exposes measurement capabilities
   and data through the Model Context Protocol.

   *MCP Client*: A network controller or management system that
   initiates measurement requests through MCP.

   *Measurement Resource*: Data exposed by MCP servers for network
   measurement (e.g., interface statistics, routing tables).

   *Measurement Tool*: Functions exposed by MCP servers that can be
   invoked for active measurements (e.g., ping, traceroute).

3.  MCP-Based Network Measurement Architecture

3.1.  Architectural Components

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 +------------------+          MCP Protocol         +------------------+
 |                  |<----------------------------->|                  |
 |  MCP Client      |   JSON-RPC 2.0 over           |  MCP Server      |
 |  (Controller)    |   TCP/HTTP/WebSocket          |  (Network Device)|
 |                  |                               |                  |
 +------------------+                               +------------------+
         |                                                  |
         |                                                  |
         v                                                  v
 +------------------+                               +------------------+
 |                  |                               |                  |
 |  AI/LLM System   |                               |  Network         |
 |                  |                               |  Hardware        |
 +------------------+                               +------------------+

             Figure 1: MCP Network Measurement Architecture

3.2.  Communication Flow

   The communication process involves five phases:

   *  *Discovery Phase*: MCP client discovers available MCP servers and
      their capabilities

   *  *Capability Negotiation*: Client and server negotiate supported
      measurement features

   *  *Measurement Execution*: Client requests measurements using
      natural language or structured queries

   *  *Data Collection*: Server provides measurement data through
      resources or tool execution

   *  *Analysis and Response*: Client processes results, potentially
      with AI assistance

3.3.  MCP Server Capabilities

   MCP servers (network devices) MUST expose:

   Measurement Resources:

   *  Interface statistics (bandwidth, utilization, errors)

   *  Routing information (tables, protocols, neighbors)

   *  Device performance metrics (CPU, memory)

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   *  Network topology data (LLDP/CDP information)

   Measurement Tools:

   *  Connectivity tests (ping, traceroute)

   *  Performance measurements (throughput, latency)

   *  Protocol-specific diagnostics

   *  Configuration validation tools

3.4.  MCP Client Capabilities

   MCP clients (controllers) MAY provide:

   *  *Sampling capabilities*: For complex measurement scenarios

   *  *Root context*: Measurement scope and boundaries

   *  *User interaction*: For measurement confirmation and authorization

4.  Use Cases

4.1.  Real-time Network Performance Monitoring

   *Scenario*: Network operator wants to check link utilization across
   core routers.

   *MCP Interaction*:

Operator: "Show me the current utilization of all core router interfaces"
MCP Client: Discovers core routers and requests interface statistics
MCP Server: Provides Resources containing interface utilization data
MCP Client: Aggregates and presents data with AI-generated insights

4.2.  Intelligent Fault Diagnosis

   *Scenario*: Troubleshooting connectivity issues between two sites.

   *MCP Interaction*:

Operator: "Diagnose connectivity issues between Site A and Site B"
MCP Client: Identifies relevant devices and requests diagnostic tools
MCP Server: Provides Tools: [traceroute, ping, show interfaces, show route]
MCP Client: Executes diagnostic sequence and analyzes results

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4.3.  Network Topology Discovery

   *Scenario*: Automated mapping of network topology.

   *MCP Interaction*:

Operator: "Discover and map the current network topology"
MCP Client: Requests topology information from seed devices
MCP Server: Provides Resources: [neighbors table, interface status, VLAN info]
MCP Client: Builds topology graph using AI-assisted correlation

4.4.  Capacity Planning and Trend Analysis

   *Scenario*: Predict future capacity needs based on current usage
   patterns.

   *MCP Interaction*:

 Operator: "Analyze capacity trends for all WAN links"
 MCP Client: Collects historical utilization data
 MCP Server: Provides Resources: [historical statistics, error counters]
 MCP Client: AI analysis generates capacity planning recommendations

4.5.  Security Incident Response Measurement

   *Scenario*: Measure and analyze potential security threats.

   *MCP Interaction*:

Operator: "Investigate unusual traffic patterns on border routers"
MCP Client: Requests security-related measurements
MCP Server: Provides Tools: [ACL hit counts, flow analysis, threat detection]
MCP Client: Correlates security events with network measurements

5.  Protocol Operations

5.1.  Measurement Request Format

   Measurement requests MUST follow MCP protocol specifications with the
   following structure:

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   {
     "jsonrpc": "2.0",
     "method": "tools/call",
     "params": {
       "name": "network_measurement_tool",
       "arguments": {
         "target": "device_or_interface",
         "measurement_type": "ping_throughput_latency",
         "parameters": {
           "count": 10,
           "interval": 1,
           "timeout": 5
         }
       }
     },
     "id": "measurement_request_001"
   }

5.2.  Measurement Response Format

   Measurement responses MUST include:

   {
     "jsonrpc": "2.0",
     "result": {
       "measurement_id": "measurement_request_001",
       "timestamp": "2025-10-18T10:30:00Z",
       "device_id": "router_core_01",
       "results": {
         "avg_latency_ms": 25.3,
         "min_latency_ms": 24.1,
         "max_latency_ms": 28.7,
         "packet_loss_percent": 0.0,
         "throughput_mbps": 987.2
       },
       "metadata": {
         "measurement_duration": 15,
         "path_taken": ["router1", "router2", "router3"]
       }
     },
     "id": "measurement_request_001"
   }

5.3.  Error Handling

   MCP servers MUST implement appropriate error handling for:

   *  Unsupported measurement types

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   *  Device capability limitations

   *  Resource exhaustion scenarios

   *  Security policy violations

   Error responses MUST follow JSON-RPC 2.0 error format with MCP-
   specific error codes.

6.  Security Considerations

   The Model Context Protocol enables powerful capabilities through
   arbitrary data access and code execution paths.  With this power
   comes important security and trust considerations that all
   implementers must carefully address.

6.1.  Authentication and Authorization

   MCP-based network measurement systems MUST implement:

   *  *Strong Authentication*: All MCP communications MUST be
      authenticated using industry-standard mechanisms (TLS mutual
      authentication, OAuth 2.0, etc.)

   *  *Role-Based Access Control*: Different measurement capabilities
      MUST be restricted based on user roles and privileges

   *  *Device Authorization*: Network devices MUST verify client
      authorization before exposing sensitive measurement data

6.2.  Data Privacy and Confidentiality

   *  *Encryption in Transit*: All MCP communications MUST use TLS 1.3
      or higher

   *  *Data Minimization*: Only necessary measurement data SHOULD be
      exposed

   *  *Access Logging*: All measurement requests and responses MUST be
      logged for audit purposes

6.3.  Measurement Tool Security

   *  *Tool Validation*: All measurement tools exposed by MCP servers
      MUST be validated for security vulnerabilities

   *  *Resource Limits*: Measurement tools MUST implement appropriate
      resource limits to prevent DoS attacks

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   *  *Input Sanitization*: All measurement parameters MUST be validated
      and sanitized

6.4.  AI/LLM Security Considerations

   *  *Prompt Injection Protection*: Natural language interfaces MUST
      implement protection against malicious prompt injection

   *  *Result Sanitization*: Measurement results MUST be sanitized
      before AI processing

   *  *Model Security*: AI models used for analysis MUST be protected
      against adversarial inputs

6.5.  Network Device Security

   *  *Least Privilege*: Network devices MUST expose only necessary
      measurement capabilities

   *  *Rate Limiting*: Measurement requests MUST be rate-limited to
      prevent abuse

   *  *Network Segmentation*: MCP traffic SHOULD be isolated in
      management networks

7.  IANA Considerations

   This document has no IANA actions.

8.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, 2017,
              <https://www.rfc-editor.org/info/rfc8174>.

   [RFC8259]  Bray, T., "The JavaScript Object Notation (JSON) Data
              Interchange Format", STD 90, RFC 8259, 2017,
              <https://www.rfc-editor.org/info/rfc8259>.

9.  Informative References

   [MCP-SPEC] Anthropic, "Model Context Protocol Specification
              2025-06-18", URL https://modelcontextprotocol.io/
              specification/2025-06-18/basic, 2025.

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   [RFC1157]  Case, J., Fedor, M., Schoffstall, M., and J. Davin,
              "Simple Network Management Protocol (SNMP)", RFC 1157,
              1990, <https://www.rfc-editor.org/info/rfc1157>.

   [RFC3954]  Claise, B., "Cisco Systems NetFlow Services Export Version
              9", RFC 3954, 2004,
              <https://www.rfc-editor.org/info/rfc3954>.

Authors' Addresses

   Guanming Zeng
   Huawei
   Email: zengguanming@huawei.com

   Jianwei Mao
   Huawei
   Email: maojianwei@huawei.com

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