MCP Server for vmanomaly

The implementation of Model Context Protocol (MCP) server for vmanomaly - VictoriaMetrics Anomaly Detection product.

This provides seamless integration with vmanomaly REST API and documentation for AI-assisted anomaly detection, model management, and observability insights.

Features

This MCP server enables AI assistants like Claude to interact with vmanomaly for:

• Health Monitoring: Check vmanomaly server health and build information
• Model Management: Discover UI-compatible models and validate univariate or multivariate configurations
• Data-Driven Recommendations: Profile sampled time series and run shared autotune suggestions for one production-ready model config across many returned series
• Configuration Generation: Generate complete vmanomaly YAML configurations
• Alert Rule Generation: Generate vmalert alerting rules based on anomaly score metrics to simplify alerting setup
• Documentation Search: Full-text search across embedded vmanomaly documentation with fuzzy matching

The MCP server contains embedded up-to-date vmanomaly documentation and is able to search it without online access.

The quality of the MCP Server and its responses depends very much on the capabilities of your client and the quality of the model you are using.

Requirements

• vmanomaly instance with REST API access:
  • version 1.28.3+ for the core MCP toolset
  • version 1.30.0+ for time-series characteristics and task-based shared autotune
• Go 1.24 or higher (if building from source)

Installation

Go

go install github.com/VictoriaMetrics/mcp-vmanomaly/cmd/mcp-vmanomaly@latest

Binaries

Download the latest release from Releases page and put it to your PATH.

Example for Linux x86_64 (other architectures and platforms are also available):

latest=$(curl -s https://api.github.com/repos/VictoriaMetrics/mcp-vmanomaly/releases/latest | grep 'tag_name' | cut -d\" -f4)
wget https://github.com/VictoriaMetrics/mcp-vmanomaly/releases/download/$latest/mcp-vmanomaly_Linux_x86_64.tar.gz
tar axvf mcp-vmanomaly_Linux_x86_64.tar.gz

Docker

You can run vmanomaly MCP Server using Docker.

This is the easiest way to get started without needing to install Go or build from source.

docker run -d --name mcp-vmanomaly \
  -e VMANOMALY_ENDPOINT=http://localhost:8490 \
  -e MCP_SERVER_MODE=http \
  -e MCP_LISTEN_ADDR=:8080 \
  -p 8080:8080 \
  ghcr.io/victoriametrics/mcp-vmanomaly

You should replace environment variables with your own parameters.

Note that the MCP_SERVER_MODE=http flag is used to enable Streamable HTTP mode. More details about server modes can be found in the Configuration section.

See available docker images in github registry.

Also see Using Docker instead of binary section for more details about using Docker with MCP server with clients in stdio mode.

Source Code

For building binary from source code you can use the following approach:

• Clone repo:

  git clone https://github.com/VictoriaMetrics/mcp-vmanomaly.git
  cd mcp-vmanomaly
  

• Build binary from cloned source code:

  make build
  # after that you can find binary mcp-vmanomaly and copy this file to your PATH or run inplace
  

• Build image from cloned source code:

  docker build -t mcp-vmanomaly .
  # after that you can use docker image mcp-vmanomaly for running or pushing
  

  For local UI/Copilot testing from the vmanomaly repository, build with a local tag:

  docker build -t mcp-vmanomaly:local .
  

  Then run the vmanomaly repository helper with:

  MCP_VMANOMALY_IMAGE=mcp-vmanomaly:local bin/run-mcp-http.sh
  

Configuration

MCP Server for vmanomaly is configured via environment variables:

Modes

MCP Server supports the following modes of operation (transports):

• stdio - Standard input/output mode, where the server reads commands from standard input and writes responses to standard output. This is the default mode and is suitable for local servers.
• http - Streamable HTTP. Server will expose the /mcp endpoint for HTTP connections.
• sse - Server-Sent Events. Server will expose the /sse and /message endpoints for SSE connections.

[!NOTE] The sse transport mode was officialy deprecated from MCP Specification (version 2025-03-26) and was replaced by Streamable HTTP transport (http mode). In future releases its support can be deprecated, use Streamable HTTP transport if your client supports it.

More info about transports you can find in MCP docs:

• Core concepts → Transports
• Specifications → Transports

Configuration examples

# Basic configuration
export VMANOMALY_ENDPOINT="http://localhost:8490"

# With authentication
export VMANOMALY_ENDPOINT="http://localhost:8490"
export VMANOMALY_BEARER_TOKEN="your-token"

# With custom headers (e.g., behind a reverse proxy)
export VMANOMALY_HEADERS="X-Custom-Header=value1,X-Another=value2"

# Server mode
export MCP_SERVER_MODE="http"
export MCP_LISTEN_ADDR="0.0.0.0:8080"

# Logging
export MCP_LOG_LEVEL="debug"
export MCP_LOG_FILE="/tmp/mcp-vmanomaly.log"

Endpoints

In HTTP and SSE modes the MCP server provides the following endpoints:

Setup in clients

Cursor

Go to: Settings → Cursor Settings → MCP → Add new global MCP server and paste the following configuration into your Cursor ~/.cursor/mcp.json file:

{
  "mcpServers": {
    "vmanomaly": {
      "command": "/path/to/mcp-vmanomaly",
      "env": {
        "VMANOMALY_ENDPOINT": "http://localhost:8490",
        "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
        "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
      }
    }
  }
}

See Cursor MCP docs for more info.

Claude Desktop

Add this to your Claude Desktop claude_desktop_config.json file (you can find it if open Settings → Developer → Edit config):

{
  "mcpServers": {
    "vmanomaly": {
      "command": "/path/to/mcp-vmanomaly",
      "env": {
        "VMANOMALY_ENDPOINT": "http://localhost:8490",
        "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
        "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
      }
    }
  }
}

See Claude Desktop MCP docs for more info.

Claude Code

Run the command:

claude mcp add vmanomaly -- /path/to/mcp-vmanomaly \
  -e VMANOMALY_ENDPOINT=http://localhost:8490 \
  -e VMANOMALY_BEARER_TOKEN=<YOUR_TOKEN> \
  -e VMANOMALY_HEADERS="X-Custom=value1,X-Auth=value2"

See Claude Code MCP docs for more info.

Visual Studio Code

Add this to your VS Code MCP config file:

{
  "servers": {
    "vmanomaly": {
      "type": "stdio",
      "command": "/path/to/mcp-vmanomaly",
      "env": {
        "VMANOMALY_ENDPOINT": "http://localhost:8490",
        "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
        "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
      }
    }
  }
}

See VS Code MCP docs for more info.

Zed

Add the following to your Zed config file:

  "context_servers": {
    "vmanomaly": {
      "command": {
        "path": "/path/to/mcp-vmanomaly",
        "args": [],
        "env": {
          "VMANOMALY_ENDPOINT": "http://localhost:8490",
          "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
          "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
        }
      },
      "settings": {}
    }
  }

See Zed MCP docs for more info.

JetBrains IDEs

• Open Settings → Tools → AI Assistant → Model Context Protocol (MCP).
• Click Add (+)
• Select As JSON
• Put the following to the input field:

{
  "mcpServers": {
    "vmanomaly": {
      "command": "/path/to/mcp-vmanomaly",
      "env": {
        "VMANOMALY_ENDPOINT": "http://localhost:8490",
        "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
        "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
      }
    }
  }
}

Windsurf

Add the following to your Windsurf MCP config file:

{
  "mcpServers": {
    "vmanomaly": {
      "command": "/path/to/mcp-vmanomaly",
      "env": {
        "VMANOMALY_ENDPOINT": "http://localhost:8490",
        "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
        "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
      }
    }
  }
}

See Windsurf MCP docs for more info.

Using Docker instead of binary

You can run vmanomaly MCP server using Docker instead of local binary.

You should replace run command in configuration examples above in the following way:

{
  "mcpServers": {
    "vmanomaly": {
      "command": "docker",
      "args": [
        "run",
        "-i", "--rm",
        "-e", "VMANOMALY_ENDPOINT",
        "-e", "VMANOMALY_BEARER_TOKEN",
        "-e", "VMANOMALY_HEADERS",
        "ghcr.io/victoriametrics/mcp-vmanomaly"
      ],
      "env": {
        "VMANOMALY_ENDPOINT": "http://localhost:8490",
        "VMANOMALY_BEARER_TOKEN": "<YOUR_TOKEN>",
        "VMANOMALY_HEADERS": "X-Custom=value1,X-Auth=value2"
      }
    }
  }
}

Usage

After installing and configuring the MCP server, you can start using it with your favorite MCP client.

You can start dialog with AI assistant from the phrase:

Use MCP vmanomaly in the following answers

But it's not required, you can just start asking questions and the assistant will automatically use the tools and documentation to provide you with the best answers.

Toolset

MCP vmanomaly provides tools organized into categories:

Health & Info (4 tools)

Model Configuration (4 tools)

Configuration (1 tool)

Documentation (1 tool)

Compatibility (1 tool)

Alerting (1 tool)

Analysis & Autotune (4 tools)

vmanomaly_create_autotune_task accepts optimization_n_trials, optimization_timeout, and advanced optimization_params to bound Optuna work. The MCP tool uses interactive defaults of 32 trials and 8 seconds when no optimization budget is provided, and a conservative anomaly fraction of 0.02 when anomaly_percentage is omitted; the vmanomaly API defaults are larger. Poll vmanomaly_get_autotune_task until status is done, then use the recommendation under result_data. Treat error and canceled as terminal statuses.

The list/schema endpoints intentionally expose only models supported by VMUI. Outside VMUI, documented multivariate aliases such as temporal_envelope_multivariate can still be shared-autotuned and validated as complete model configurations. Do not recommend multivariate models in VMUI.

Dialog example

This example shows data-driven model selection rather than choosing a model from the metric name:

• You: Detect sustained CPU spikes for avg by (instance) (1 - rate(node_cpu_seconds_total{mode="idle"}[5m])) at a 5m step.

• Assistant: I profiled the exact query first. The sampled series contain a trend and recurring hour-of-day/day-of-week structure, so temporal_envelope is the best online starting point. If the profile had no meaningful trend or seasonality, I would prefer mad_online for robust data, or zscore_online only for a stable light-tailed distribution.

• Assistant: I verified the model and schema, then started shared autotune with the same 5m step, anomaly_percentage=0.02, and causal exact=true validation. After sequentially polling the task to completion, I validated the returned model configuration before presenting it.

  models:
    cpu_envelope:
      class: temporal_envelope
      queries: [cpu_non_idle]
      schedulers: [cpu_5m]
      seasonalities: [hod_smooth, dow_smooth]
      detection_direction: above_expected
      min_rel_dev_from_expected: [0, 15]
  

  The exact tuned parameters depend on the sampled data; the final model spec is always validated against the running vmanomaly instance.

During this dialog, the assistant used the following tools:

• vmanomaly_timeseries_characteristics to measure the sampled data profile
• vmanomaly_list_models and vmanomaly_get_model_schema to verify the UI-compatible model
• vmanomaly_create_autotune_task and vmanomaly_get_autotune_task to tune shared parameters
• vmanomaly_validate_model_config to validate the tuned model
• vmanomaly_validate_config to validate the configuration
• vmanomaly_create_detection_task to backtest anomaly detection

Monitoring

In HTTP and SSE modes the MCP Server provides metrics in Prometheus format at the /metrics endpoint.

Tracked operations:

• mcp_vmanomaly_initialize_total - Client connections
• mcp_vmanomaly_call_tool_total{name,is_error} - Tool calls with success/error tracking
• mcp_vmanomaly_read_resource_total{uri} - Documentation resource reads
• mcp_vmanomaly_list_*_total - List operations (tools, resources, prompts)
• mcp_vmanomaly_error_total{method,error} - Errors by method and type

Example:

# Start in HTTP mode
VMANOMALY_ENDPOINT="http://localhost:8490" MCP_SERVER_MODE=http ./bin/mcp-vmanomaly

# Query metrics
curl http://localhost:8080/metrics

Roadmap

• Grafana dashboard for MCP server monitoring
• Add API compatibility matrix to gracefully handle version differences between MCP client and vmanomaly server (API is evolving, features may be unavailable)

Disclaimer

AI services and agents along with MCP servers like this cannot guarantee the accuracy, completeness and reliability of results. You should double check the results obtained with AI.

The quality of the MCP Server and its responses depends very much on the capabilities of your client and the quality of the model you are using.

Contributing

Contributions to the MCP vmanomaly project are welcome!

Please feel free to submit issues, feature requests, or pull requests.

Related Projects

• vmanomaly - VictoriaMetrics anomaly detection
• VictoriaMetrics - Time series database
• mcp-victoriametrics - MCP server for VictoriaMetrics
• Model Context Protocol - MCP specification

Support

For vmanomaly-specific questions, see the vmanomaly documentation.

For MCP server issues, please open an issue in this repository.