dbwatcher-mcp

An MCP (Model Context Protocol) server for historical performance analysis of Azure SQL databases monitored by database watcher.

Database watcher collects telemetry from Azure SQL resources and stores it in a Kusto (ADX) cluster. This MCP server queries that telemetry to help you analyze past workloads, identify performance bottlenecks, and troubleshoot issues — all through natural language conversations with Copilot.

Supported targets: Currently supports Azure SQL Database telemetry only. Azure SQL Managed Instance support is planned for a future release.

Note: This is a read-only, forensic analysis tool. It analyzes historical telemetry data — it does not connect directly to your SQL databases or make any changes.

Features

• Wait Stats Analysis - Identify bottleneck categories (CPU, IO, locks, parallelism)
• Query Analysis - Find top resource-consuming queries by CPU, reads, duration
• Per-Query Wait Breakdown - Understand what specific queries are waiting on
• Blocking Detection - Find sessions waiting on locks held by other sessions
• Resource Utilization - Analyze CPU, Data IO, and Log IO trends over time
• Performance Counters - Monitor PLE, buffer cache hit ratio, memory grants
• Disk I/O Latency - Analyze read/write latency for data and log files
• Missing Indexes - Get index recommendations to improve query performance
• Parameter Sniffing Detection - Identify queries with high execution time variance
• Diagnostic Strategy - Built-in methodology guide for systematic troubleshooting
• Visual Charts - Generate Mermaid charts for time-series data, distributions, and blocking chains

Prerequisites

• .NET 10.0 SDK (for building from source)
• Azure CLI (az login) or VS Code Azure Account extension for authentication
• Access to a Kusto cluster with database watcher telemetry

Using in VS Code

Step 1: Configure the MCP Server

Create or edit .vscode/mcp.json in your workspace:

{
  "servers": {
    "dbwatcher": {
      "type": "stdio",
      "command": "dotnet",
      "args": [
        "run",
        "--project",
        "/path/to/dbwatcher-mcp/src/DbWatcher.Mcp"
      ]
    }
  }
}

Step 2: Authenticate to Azure

Before using the server, ensure you're logged in:

az login

Or use the VS Code Azure Account extension and sign in.

Step 3: Start a Diagnostic Session

Open Copilot Chat in VS Code and start with:

1. "Connect to my Kusto cluster at https://mycluster.eastus.kusto.windows.net, database 'dbwatcher'" - Establish connection
2. "List the monitored databases" - See which SQL databases have telemetry

Tip: The server exposes a diagnostic-strategy resource with the recommended diagnostic methodology. Copilot can read this automatically when you ask about troubleshooting approaches.

Step 4: Analyze Performance

Once connected, ask questions like:

• "Analyze wait stats for my database over the last 24 hours"
• "What are the top 10 queries by CPU usage?"
• "Show me blocking events from yesterday between 2pm and 4pm"
• "Are there any missing index recommendations?"
• "Check for parameter sniffing issues"

The server includes built-in thresholds and interpretations to help you understand the results.

Example Workflow

You: Connect to https://mycluster.kusto.windows.net database telemetry-db

Copilot: Connected to Kusto cluster.

You: What databases are being monitored?

Copilot: [Lists databases with telemetry]

You: Analyze wait stats for production-db

Copilot: [Shows wait stats with interpretations - e.g., "PAGEIOLATCH_SH is 35% 
         of waits, indicating memory pressure or missing indexes"]

You: Find the top queries causing those IO waits

Copilot: [Shows top queries sorted by reads with recommendations]

Visualizing Results with Charts

The MCP server returns structured JSON data that can be visualized using Mermaid charts. Copilot can generate these charts automatically when you ask for visual representations.

Example prompts:

• "Show me a chart of CPU usage over the last hour"
• "Create a pie chart of wait type distribution"
• "Visualize the blocking chain"

Copilot will generate Mermaid markdown and can write it to a .md file. Open the file and preview it (Cmd+Shift+V on Mac, Ctrl+Shift+V on Windows) to see the rendered charts.

Supported chart types:

• Line/bar charts for resource utilization over time
• Pie charts for wait type distributions
• Flowcharts for blocking chains
• Gantt charts for query execution timelines

Building from Source

# Clone the repository
git clone https://github.com/markgar/dbwatcher-mcp.git
cd dbwatcher-mcp/src/DbWatcher.Mcp

# Build
dotnet build

# Run directly (for testing)
dotnet run

Then configure VS Code to use the built project as shown in "Using in VS Code" above.

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Publishing to NuGet

# Create the NuGet package
dotnet pack -c Release

# Publish to NuGet.org
dotnet nuget push bin/Release/*.nupkg --api-key <your-api-key> --source https://api.nuget.org/v3/index.json

See aka.ms/nuget/mcp/guide for the full guide.

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Platform Support

The MCP server is built as a self-contained application. By default configured for:

• win-x64, win-arm64
• osx-arm64
• linux-x64, linux-arm64, linux-musl-x64

More Information

• MCP Official Documentation
• MCP C# SDK
• Database Watcher Overview
• VS Code MCP Servers