The Clixon MCP Server enables AI coding agents to fetch, query, and manage network device configurations via RESTCONF.
Fetch device configuration (fetch_config): Pull configuration from RESTCONF-capable devices, with support for HTTP basic authentication and optional SSL certificate verification.
Retrieve cached configuration (get_config): Return the full configuration previously fetched and stored in memory.
Query a specific config section (get_config_path): Extract a targeted portion of the cached configuration using a dot-separated path (e.g., ietf-interfaces:interfaces.interface).
Write configuration back (write_config): Push the cached configuration back to the device via RESTCONF.
Manage the RESTCONF URL (get_config_url / set_config_url): Retrieve or update the target device URL without restarting the server.
Clear the config cache (clear_config_cache): Remove the in-memory cached configuration.
Analyze configuration (analyze_config): Use a built-in prompt to fetch and analyze device configuration details such as interfaces and routing in one step.
Discover available tools (list_tools / help): List all tools and get usage guidance from within the agent interface.
AI agent integration: Designed to integrate with AI coding agents like OpenCode, enabling natural language queries about network configurations.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Clixon MCP Server show me the current routing and interface configuration"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol server for fetching and querying network device configuration via RESTCONF. Connects to Clixon-based (or any RESTCONF-capable) devices and makes the configuration available to AI coding agents like OpenCode.
This project uses UV (https://github.com/astral-sh/uv) for package control.
uv syncRelated MCP server: network-mcp
uv run server.py --restconf-url https://localhost:8443/restconf/Argument | Default | Description |
|
| RESTCONF URL to fetch config from |
| (empty) | HTTP basic auth username for RESTCONF (optional) |
| (empty) | HTTP basic auth password for RESTCONF (optional) |
|
| Verify SSL certificates when fetching RESTCONF config |
Tool | Description |
| Fetch device configuration via RESTCONF (supports basic auth and TLS) |
| Write cached configuration back to the device via RESTCONF |
| Return the currently cached configuration |
| Extract a specific section by dot-separated path (e.g. |
| Return the RESTCONF URL used to fetch the configuration |
| Set the RESTCONF URL for fetching configuration |
| Clear the cached configuration |
| List available tools |
| Return a help message describing the server and available tools |
config://server-info — Server metadata (name, version, available tools)
analyze_config — Fetch and analyze device configuration (interfaces, routing, etc.)
Add to your opencode.json (~/.opencode/opencode.json):
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"clixon": {
"type": "remote",
"url": "http://localhost:8000/mcp",
"enabled": true
}
}
}Once connected, ask questions like:
Fetch the config and summarize it.
What interfaces are configured?
Show me the routing configuration
Are there any interfaces that are down?
The AI agent will call fetch_config to pull the RESTCONF data, cache it in memory, and then use get_config / get_config_path to answer follow-up questions about the device configuration.
Fetch network device configuration via RESTCONF.
Args:
url: RESTCONF URL, e.g. https://device:8443/restconf/data
username: HTTP basic auth username (optional).
password: HTTP basic auth password (optional).
verify_ssl: Whether to verify SSL certificates (default: False).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| username | No | ||
| password | No | ||
| verify_ssl | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions authentication (HTTP basic auth) and SSL verification, which adds useful context beyond the schema. However, it lacks details on rate limits, error handling, response format, or whether this is a read-only operation, leaving gaps for a tool that interacts with network devices.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a brief purpose statement followed by a parameter list. It's appropriately sized and front-loaded, with no wasted sentences. Minor improvements could include bolding or bullet points for better readability, but it's efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (network device interaction) and the presence of an output schema (which handles return values), the description is moderately complete. It covers parameters well but lacks behavioral context like error handling or performance considerations. With no annotations, it should do more to guide safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides clear explanations for all 4 parameters (url, username, password, verify_ssl), including examples and defaults, adding significant meaning beyond the bare schema. This effectively documents the parameters, though it could benefit from more detail on URL format or authentication requirements.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('fetch') and target ('network device configuration via RESTCONF'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from its siblings (get_config, get_config_path), leaving ambiguity about when to use one versus the others.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the sibling tools (get_config, get_config_path). The description mentions the protocol (RESTCONF) but doesn't specify use cases, prerequisites, or alternatives, offering minimal contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the currently cached RESTCONF configuration.
Call fetch_config first to load configuration from a device.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns cached data and requires a prior fetch_config call, adding useful behavioral context. However, it doesn't cover aspects like error handling, performance, or what happens if cache is empty, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose and followed by a prerequisite note. Every sentence adds value without waste, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, an output schema exists, and no annotations, the description is mostly complete. It explains the purpose and prerequisite, but could benefit from more detail on cache behavior or output format, though the output schema mitigates this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, and the baseline for this case is 4, as it avoids redundancy while being complete for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Return') and resource ('currently cached RESTCONF configuration'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'get_config_path' beyond mentioning 'fetch_config' as a prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by stating 'Call fetch_config first to load configuration from a device,' which guides when to use this tool. It implies an alternative (fetch_config) but doesn't explicitly mention when not to use it or compare with other siblings like get_config_path.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract a specific section from the cached configuration by dot-separated path.
Args:
path: Dot-separated path into the config, e.g.
"ietf-interfaces:interfaces" or
"ietf-interfaces:interfaces.interface"
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool extracts from 'cached configuration', which implies read-only behavior, but doesn't mention error handling, performance, or what happens if the path doesn't exist. It adds some behavioral context but lacks completeness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a clear 'Args' section with examples. Every sentence earns its place with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (1 parameter, no annotations, but with output schema), the description is mostly complete. It explains the purpose and parameter semantics well, but since there's an output schema, it doesn't need to describe return values. However, it could better address behavioral aspects like error cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It adds significant meaning by explaining the 'path' parameter as a 'dot-separated path into the config' with concrete examples, which clarifies the parameter's purpose and format beyond the schema's basic type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Extract') and resource ('specific section from the cached configuration'), and distinguishes it from siblings by specifying the dot-separated path mechanism. It's not just a tautology of the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (extracting sections by dot-separated path), but doesn't explicitly mention when not to use it or name alternatives like 'fetch_config' or 'get_config'. It implies usage through the example paths.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0fetch_configget_configget_config_pathScored across 3 tools
Each tool has a clearly distinct purpose: fetch_config retrieves configuration from a device, get_config accesses the cached configuration, and get_config_path extracts specific sections from the cache. There is no overlap or ambiguity in their functions.
All tool names follow a consistent verb_noun pattern with snake_case (fetch_config, get_config, get_config_path). The verbs 'fetch' and 'get' are semantically related but used distinctly to differentiate between external retrieval and internal access.
With only 3 tools, the set feels thin for a network device configuration server, potentially lacking operations like update, delete, or validation. However, it covers basic fetch and query workflows, making it borderline but functional for limited use cases.
The toolset is severely incomplete for network device configuration management. It only supports fetching and reading configuration, with no tools for creating, updating, deleting, or validating configurations, leaving significant gaps that will hinder agent workflows.
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