draft-yang-nmrg-mcp-nm-00

Network Management Working Group Y. Yang
Internet-Draft Q. Wu
Intended status: Informational Huawei
Expires: 4 January 2026 D. Lopez
Telefonica
N. R. Moreno
Deutsche Telekom
L. Tailhardat
Orange Research
3 July 2025

Applicability of MCP for the Network Management
draft-yang-nmrg-mcp-nm-00

Abstract

The application of MCP in the network management field is meant to
develop various rich AI driven network applications, realize intent
based networks management automation in the multi-vendor
heterogeneous network environment. This document discusses the
applicability of MCP to the network management in the IP network that
utilizes IETF technologies. It explores operational aspect, key
components, generic workflow and deployment scenarios. The impact of
integrating MCP into the network management system is also discussed.

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Table of Contents

1. Introduction
2. Terminology & Notation Conventions
2.1. MCP
2.2. Others
3. Overview of key challenges for the network management
3.1. Inconsistent YANG Model Support
3.2. Partial or Non-Standard Network Management Protocols
Implementations
3.3. YANG Models Lack Integration with Open APIs
4. Architecture Overview
4.1. Encapsulating Device Operations into MCP Tools
4.2. LLM for Intent-to-Tool-Request Translation
4.3. Closed-Loop Automation Execution Workflow
4.4. Example
5. Deployment Considerations
5.1. MCP hosted within the Network Controller
5.2. MCP Server Hosted Within the Network Device
6. Impact of integrating MCP on Network Management
6.1. MCP Hosted Within the Network Controller
6.2. MCP Server Hosted within the Network Device
7. IANA Considerations
8. Security Considerations
9. References
9.1. Normative References
9.2. Informative References
Contributors
Authors' Addresses

1. Introduction

The Model Context Protocol (MCP) provides a standardized way for LLMs
to access and utilize information from different sources, interact
with tools, making it easier to build AI applications that can
interact with external LLM and network management systems.

MCP has been seen as rapid adoption internet technology. The
application of MCP in the network management field is meant to
develop various rich AI driven network applications, realize intent
based networks management automation in the multi-vendor
heterogeneous network environment. By establishing standard
interfaces for tool encapsulation, intent translation, and closed-
loop execution within the network management system, MCP enables AI
Agents to have:

* Unified operation abstraction through normalized MCP tool
definitions

* Seamless LLM integration via the structured protocol

* Automation Execution Ability

This document discusses the applicability of MCP to the network
management plane in the IP network that utilizes IETF technologies.
It explores operational aspect, key components, generic workflow and
deployment scenarios. The impact of integrating MCP into the network
management system will also be discussed.

2. Terminology & Notation Conventions

The following terms are used throughout this document:

2.1. MCP

* *MCP Protocol*: MCP is an open standard designed to facilitate
communication between LLMs and external data sources or tools.

* *MCP Host*: The entity initiating the LLM request.

* *MCP Client*: A built-in module within a host, specifically
designed for interaction with the MCP server.

* *MCP Server*: A dedicated server that interacts with MCP clients
and provides tools.

* *CLI*: Command Line Interface

2.2. Others

* *LLM*: Large Language Model

* *NETCONF*: Network Configuration Protocol [RFC6241]

* *RESTCONF*: RESTful Network Configuration Protocol [RFC8040]

* *SNMP*: A Simple Network Management Protocol [RFC2576]

3. Overview of key challenges for the network management

In large scale network management environment, a large number of
devices from different vendors need to be uniformly managed, which
can lead to the following issues or challenges:

3.1. Inconsistent YANG Model Support

Different vendors implement different YANG models (standard or
proprietary), leading to:

* Lack of uniform data structures for configuration/state retrieval.

* Requirement for vendor-specific adaptations in automation scripts.

Also IETF standard device models has slow adoption. Similar device
models are defined in Openconfig or other SDOs, therefore the current
YANG device models ecosystem is fragmented.

3.2. Partial or Non-Standard Network Management Protocols
Implementations

Some vendors only partially support standard Network management
protocols, and proprietary extensions may break interoperability.
Other vendors might choose non-stanard network management protocol or
telemetry protocol such as gnmi [I-D.openconfig-rtgwg-gnmi-spec],
grpc [I-D.kumar-rtgwg-grpc-protocol]. A significant number of
network operators continue to rely on legacy network management
mechanisms such as SNMP.

3.3. YANG Models Lack Integration with Open APIs

Today, open API has been widely adopted by the northbound interface
of OSS/BSS or Network orchestrators while YANG data models have been
widely adopted by the northbound interface of the network controller/
orchestrator or the southbound interface of the network controller/
orchestrator. However Open API ecosystem and YANG model ecosystem
are both built as silo and lack integration or mapping between them.

4. Architecture Overview

The LLM model with MCP support and its ability to comprehend diverse
complex requirements and deliver corresponding functionalities, is
well-suited for large scale multi-vendor network management
environments, effectively addressing the aforementioned challenges in
Section 3. Therefore, we have introduced the MCP protocol in the
network management environments for building an intelligent network
management and control platform.

4.1. Encapsulating Device Operations into MCP Tools

* Objective: Standardize device operations into modular, reusable
tools.

* Implementation:

- Tool Abstraction: Vendor-specific commands are wrapped into
discrete MCP Tools with uniform schemas.

- Tool Registry: A centralized database stores MCP Tools with
metadata (e.g., names, descriptions, parameters).

* Benefits:

- Eliminates manual translation of commands across different
vendors

- Enabling the plug-and-play integration of new device types.

4.2. LLM for Intent-to-Tool-Request Translation

* Objective: Allow AI models (such as Claude) to understand natural
language commands and trigger operations.

* Workflow:

- Intent Recognition: The LLM first analyzes the user's natural
language query to identify：

o The user's intent or goal

o Required actions or operations

o Entities, parameters, and constraints mentioned

o Context from previous interactions

- Tool Discovery and Toolchain Generation: The LLM access tool
descriptions provided by MCP servers, and matches the
identified intent with available tools.

- Parameter Extraction and Mapping: The LLM maps natural language
references to structured parameter names and extracts relevant
information from the user query.

- Structured Invocation Generation: The LLM generates properly
formatted tool calls following MCP's protocol.

* Benefits:

- Bridge natural language to tool invocation requests in a fixed
format, then return this request to the client, enabling the
client to properly parse the request.

4.3. Closed-Loop Automation Execution Workflow

* Objective: Realize the closed loop of "voice/text commands →
automatic execution".

* A general workflow is as follows:

- User Input Submission: An operator submits a natural language
request to the MCP client. And The MCP client forwards this
request to the LLM.

- LLM Intent Processing: The LLM parses the input, identifies the
operational intent, and forwards a structured request to the
MCP client, which queries the MCP Server to retrieve the
available tools. The information would include the functional
description, required parameters of tools.

- LLM Toolchain Decision:

o The LLM evaluates the context and if tools are required,
select and sequence tools.

o The decision is sent back to the MCP Client and then MCP
Client will execute tools via server.

- Tool Execution: The MCP Server executes the translated commands
on target devices and returns results to the client.

- Result Aggregation & Feedback: The MCP Client collates tool
outputs (success/failure logs) and forwards them to the LLM for
summarization.

* Benefits:

- Tools safely retry/rollback.

- Full traceability of LLM decisions and tool executions.

4.4. Example

Take multi-vendor network management as an example, the MCP server is
deployed locally on the network controller, and the tools are
integrated into the MCP server. The server provides the following
registered tool descriptor information:

Tools description: It describes the name, use, and parameters of
tools.

Tools implementation: MCP implementation describes how the tools are
invoked.

See Tool descriptor information example as follows:

# Tool Descriptor
[
{
"name": "batch_configure_devices",
"description": "Batch Configure Network Devices"，
"parameters": {
"type": "object",
"properties": {
"device_ips": {"type": "array", "items": {"type": "string"}, "description": "Device IP List"}，
"commands": {"type": "array", "items": {"type": "string"}, "description": "CLI Sequence"}，
"credential_id": {"type": "string", "description": "Credential ID"}
}，
"required": ["device_ips", "commands"]
}
},
{
"name": "check_device_status",
"description": "Check the Status of Network Devices"，
"parameters": {
"type": "object",
"properties": {
"device_ip": {"type": "string"},
"metrics": {"type": "array", "items": {"enum": ["cpu", "memory", "interface"]}}
}
}
}
]
# Tool Implementation
from netmiko import ConnectHandler
from mcp_server import McpServer

app = FastAPI()
server = McpServer(app)

#Connection Pool Management
devices = {
"192.168.1.1": {"device_type": "VendorA-XYZ"，"credential": "admin:XYZ@password"},
"192.168.1.2": {"device_type": "VendorB-ABC","credential":"admin:ABC@passowrd"}
....
}

@server.tool("batch_configure_devices")
async def batch_config(device_ips: list,commands: list,credential_id: str):
results = {}
for ip in device_ips:
conn = ConnectHandler(
ip = ip,
username = devices[ip]["credential"].split(':')[0],
password = devices[ip]["credential"].split(':')[1],
device_type = devices[ip]["device_type"]
)
output = conn.send_config_set(commands)
results[ip] = output
return {"success": True, "details": results)

@server.tool("check_device_status")
async def check_status(device_ip: str, metrics: list):
status = {}
if "cpu" in metrics:
status["cpu"] = get_cpu_usage (device_ip)
if "memory" in metrics:
status["memory"] = get_memory_usage(device_ip)
return status

Suppose a user submits a request (via the client) such as "Configure
OSPF Area 0 with process ID 100 for all core switches in the Beijing
data center," the MCP client retrieves the necessary tooling
descriptor information from the MCP server and forwards it along with
the request to the LLM. The LLM determines the appropriate tools and
responds in JSON format as follows:

{
"method": "batch_configure_devices",
"params": {
"device_ips":["192.168.10.1",....,"192.168.10.10"],
"command": [
"router ospf 100",
"network 192.168.0.0 0.0.255.255 area 0"
]
}
}
}

The MCP server executes the network management operation in JSON
format and returns the results to the MCP client, which forwards them
to the LLM. The LLM parses the response, generates a natural-
language summary, and sends it back to the client for final
presentation to the user. See natural lanauge summary example as
follows:

{
"192.168.10.1": "Configure Successfully, take 2.3 seconds",
"192.168.10.2": "Error: no response from the device",
}

5. Deployment Considerations

This section describes MCP deployment requirements for network
management environments, followed by implementation scenarios. Key
architectural requirements include:

* Function-Specific MCP Servers: To maintain proper architecture and
performance with growing tool volumes, servers should be
categorized by network management function. Typical categories
include network log analysis, device configuration management,
energy consumption management, and security operations.

* Secure and Scalable Architecture: The architecture must:

- Enforce strict access controls limiting MCP operations to
authorized AI models and users

- Scale efficiently with increasing network device counts while
maintaining performance

* Automated Workflows: MCP implementations should support LLM-
coordinated automation of:

- Real-time monitoring and diagnostics

- Fault remediation workflows

- Other common management operations to reduce operator workload

While these core requirements apply universally, operational
characteristics vary based on deployment location (on-premises vs.
remote). The following subsections detail these deployment
scenarios.

5.1. MCP hosted within the Network Controller

+--------------+
| User |
+-------+------+
|
Natural Language
Command |
.................|............................
. | .
. +-------+------+ +-----------+.
. | MCP Client +-------+ LLM |.
. +-------+------+ +-----------+.
. | .
. Tools Request .
. | .
. +-------+------+ .
. | MCP Server | Network .
. +-------+------+ Controller .
. | .
.................|............................
|
Netconf/Telemetry
+-------------------+------------------+
| | |
| | |
+-----+--------+ +-------+------+ +------+-------+
| Network | | Network | | Network |
| Device | | Device | | Device |
+--------------+ +--------------+ +--------------+

In this senario, the MCP server is deployed within the network
controller, which could potentially be a cloud environment. The MCP
server acts as a protocol converter, transforming NETCONF's XML/YANG
models into JSON-RPC 2.0 format for AI system. - Scope: The MCP
Server, colocated with the MCP Client and LLM model, is hosted within
a cloud environment, the network devices stay as it is. - Key
Characteristics: - Centralized Management: A single MCP Client
instance can manage all network devices in geographically dispersed
network. - Scalability: Cloud-native scaling accommodates dynamic
tool registry updates and high request volumes.

5.2. MCP Server Hosted Within the Network Device

In this senario, the MCP server is deployed within the network
devices. The MCP server acts as a protocol converter, transforming
CLI into JSON-RPC 2.0 format for AI system. - Scope: The MCP server
is colocated with network devices. The MCP Client operates in a
cloud environment, requesting distributed MCP Server via public/
private APIs. - Key Characteristics: - Low Latency: Direct access to
network devices minimizes tool execution delays

6. Impact of integrating MCP on Network Management

6.1. MCP Hosted Within the Network Controller

* Pro

- Resource utilization efficiency: Controllers usually have
stronger computing and storage resources, which can better
support the operation of MCP Server and will not have a
significant impact on the performance of the network equipment
itself.

- Security:

o Security mechanisms can be implemented centrally on the
controller, and the overall security can be improved through
unified authentication, authorization and audit mechanisms.

o Reduces the risk of equipment being exposed to the network
and reduces the possibility of being attacked.

- Protocol adaptability:

o Communicating with devices through the NETCONF protocol can
better be compatible with existing devices and protocols,
reducing the need for equipment modification.

o NETCONF protocol has wide support and mature tool chains in
the industry, which is easy to develop and maintain.

* Con

- Latency and real-time performance:

o Since management instructions need to be forwarded through
the controller, latency may increase and real-time
performance may be affected.

o For some scenarios with extremely high real-time
requirements, it may not meet the requirements.

- Protocol conversion complexity:

o The MCP protocol needs to be converted to the NETCONF
protocol, which increases the complexity and development
cost of protocol conversion.

o It is necessary to deal with compatibility and consistency
issues between different protocols.

6.2. MCP Server Hosted within the Network Device

* Pro

- The protocol stack simplification:

o If you deploy the MCP Server directly on the network device,
you can skip the NETCONF protocol layer and manage the
device directly through MCP. This reduces the complexity of
protocol conversion and simplifies the overall architecture.

o It reduces the development and maintenance costs caused by
protocol adaptation, especially when the device manufacturer
supports the MCP protocol.

- Real-time performance and response speed:

o The MCP Server is directly deployed on the device, which
reduces the transmission latency in the middle and can
respond to management instructions faster, which is suitable
for scenarios with high real-time requirements.

* Con

- Device Resource Consumption:

o Network devices usually have limited resources (CPU, memory,
etc.). Deploying MCP Server may occupy a large amount of
resources, affecting the normal operation of the device.

o It is necessary to optimize and expand the hardware and
software resources of the device, which increases the
complexity of the device.

- Security and Management Complexity:

o Each device needs to manage the security of the MCP server
separately (such as authentication, authorization, audit,
etc.), which increases the complexity of management.

o Each device needs to independently deploy and maintain the
MCP Server, which increases the operation and maintenance
cost.

- Incompatible with Legacy devices:

o Legacy devices do not have the ability to support MCP
servers and still need NETCONF to implement network
configuration. This makes it impossible for the network to
form a unified control mechanism.

7. IANA Considerations

This document has no IANA actions.

8. Security Considerations

The MCP protocol needs to consider scenarios where either the client
or server encounters issues, such as crashes. If one or both parties
go offline during communication, the entire process may remain stuck
waiting for messages, potentially leading to an infinite loop.
Furthermore, certain tool operations may be interrupted, and some
irreversible network management operations could be affected.

Due to network latency, some operations might not return in time, yet
from the user's perspective, these operations may appear either
unexecuted or failed. If the user then initiates another tool
request to the server, problems may occur.

For complex network management workflows, while LLM's tool invocation
process may generally function correctly, issues can arise in the
details. Users must verify each LLM operation to prevent unintended
hazardous actions.

9. References

9.1. Normative References

[RFC2576] Frye, R., Levi, D., Routhier, S., and B. Wijnen,
"Coexistence between Version 1, Version 2, and Version 3
of the Internet-standard Network Management Framework",
RFC 2576, DOI 10.17487/RFC2576, March 2000,
<https://www.rfc-editor.org/rfc/rfc2576>.

[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/rfc/rfc6241>.

[RFC8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,
<https://www.rfc-editor.org/rfc/rfc8040>.

9.2. Informative References

[I-D.kumar-rtgwg-grpc-protocol]
Kumar, A., Kolhe, J., Ghemawat, S., and L. Ryan, "gRPC
Protocol", Work in Progress, Internet-Draft, draft-kumar-
rtgwg-grpc-protocol-00, 8 July 2016,
<https://datatracker.ietf.org/doc/html/draft-kumar-rtgwg-
grpc-protocol-00>.

[I-D.openconfig-rtgwg-gnmi-spec]
Shakir, R., Shaikh, A., Borman, P., Hines, M., Lebsack,
C., and C. Morrow, "gRPC Network Management Interface
(gNMI)", Work in Progress, Internet-Draft, draft-
openconfig-rtgwg-gnmi-spec-01, 5 March 2018,
<https://datatracker.ietf.org/doc/html/draft-openconfig-
rtgwg-gnmi-spec-01>.

Contributors

Guanming Zeng
Huawei
China
Email: zengguanming@huawei.com

Authors' Addresses

Yuanyuan Yang
Huawei
China
Email: yangyuanyuan55@huawei.com

Qin Wu
Huawei
China
Email: bill.wu@huawei.com

Diego Lopez
Telefonica
Spain
Email: diego.r.lopez@telefonica.com

Nathalie Romo Moreno
Deutsche Telekom
Germany
Email: nathalie.romo-moreno@telekom.de

Lionel Tailhardat
Orange Research
France
Email: lionel.tailhardat@orange.com

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