dive/docs/MCP_GUIDE.md

Dive MCP Server: Comprehensive Guide

This guide provides detailed instructions on how to build and run the dive Model Context Protocol (MCP) server and connect various clients to it.

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1. Building the Server

Before running the MCP server, you need to build the dive binary from source to ensure you have the latest MCP features.

Prerequisites

• Go: Version 1.24 or higher.
• Task (Optional): A task runner for simplified builds.

Standard Build (Recommended)

If you have task installed:

# Build the binary for your local platform
task build-local

The binary will be created at ./dive.

Manual Build

Alternatively, use the standard Go compiler:

go build -o dive ./cmd/dive

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2. Running the Dive MCP Server

The dive binary includes a built-in MCP server. You can run it using the mcp subcommand.

Transport Options

Dive supports three transport modes for MCP:

A. Stdio (Standard Input/Output)

Best for local AI agents running on the same machine (e.g., Claude Desktop, Cursor). This is the default transport.

# Run with default settings
dive mcp

# Recommended for production (suppresses non-protocol logs)
dive mcp --quiet

B. Streamable HTTP (Unified HTTP + SSE) - Recommended for Network

The modern standard for MCP communication over HTTP. Consolidates handshakes and messages into a single endpoint.

dive mcp --transport streamable-http --port 8080

• Endpoint: http://localhost:8080/mcp

C. SSE (Server-Sent Events) - Legacy Support

Maintained for backwards compatibility with older MCP clients. In Dive, this is internally routed through the Streamable HTTP engine for maximum robustness.

dive mcp --transport sse --port 8080

• SSE Endpoint: http://localhost:8080/sse
• Message Endpoint: http://localhost:8080/message

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3. Connecting MCP Clients

Claude Desktop

Add dive to your claude_desktop_config.json (usually found in %APPDATA%\Claude\claude_desktop_config.json on Windows or ~/Library/Application Support/Claude/claude_desktop_config.json on macOS).

Using Stdio:

{
  "mcpServers": {
    "dive": {
      "command": "/usr/local/bin/dive",
      "args": ["mcp", "--quiet"]
    }
  }
}

Using HTTP (Streamable):

{
  "mcpServers": {
    "dive": {
      "url": "http://localhost:8080/mcp"
    }
  }
}

Gemini-CLI

Gemini-CLI can connect to Dive via HTTP or Stdio.

Configuration (~/.gemini-cli/config.yaml):

mcpServers:
  dive:
    url: "http://localhost:8080/mcp"

IDEs (Cursor, VS Code via Roo Code)

1. Open the MCP settings in your IDE.
2. Add a new MCP server.
3. Choose command mode and use:
   • Command: dive
   • Arguments: mcp, --quiet

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4. Available Tools & Capabilities

Once connected, your AI agent will have access to the following tools:

Tool	Purpose
analyze_image	Get high-level efficiency metrics and layer metadata for any image.
get_wasted_space	Identify specific files that are duplicated or deleted across layers.
inspect_layer	Explore the file tree of a specific layer at a specific path.
diff_layers	Compare any two layers to see added, modified, or removed files.

Resource Templates

Agents can also "read" analysis results via URI:

• dive://image/{name}/summary
• dive://image/{name}/efficiency

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5. Powerful Usage Examples

Once the server is connected, you can use natural language prompts to perform complex analysis. Here are a few examples of what you can ask your AI agent:

A. Efficiency Health Check

"Analyze the image 'my-app:latest' and tell me the efficiency score and total wasted space. Is it performing well compared to industry standards?"

B. Identifying Bloat

"Show me the top 10 most inefficient files in 'node:20-alpine'. I want to see which layers they are located in and how much space they are wasting."

C. Troubleshooting Layer Growth

"I just added a new layer to my Dockerfile and the image size jumped by 500MB unexpectedly. Can you inspect the latest layer of 'my-app:dev' and find the specific files causing this growth?"

D. Deep Diffing for Debugging

"Compare layer 3 and layer 4 of 'custom-service:v2'. Show me exactly which files were added or modified in that transition so I can understand the build impact."

E. AI-Driven Optimization

"Use the 'optimize-dockerfile' prompt for image 'python:3.11-slim'. Based on the analysis, give me 3 specific recommendations to reduce the image size without breaking the application."

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6. Security & Performance Tuning

Security Sandbox

To prevent the AI from accessing arbitrary tarballs on your host, use the sandbox flag to restrict docker-archive lookups:

dive mcp --mcp-sandbox /home/user/allowed-images/

Analysis Caching

Image analysis is computationally expensive. Dive maintains an LRU cache to speed up repeated requests:

• --mcp-cache-size: Number of analysis results to keep (default: 10).
• --mcp-cache-ttl: How long to keep results (e.g., 24h, 1h30m).

Persistent Settings

Save your preferred MCP configuration in ~/.dive.yaml:

mcp:
  transport: streamable-http
  port: 8080
  mcp-sandbox: /tmp/images
  mcp-cache-size: 20

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7. Troubleshooting: "Missing sessionId"

If you encounter a Missing sessionId error when using SSE, ensure you are providing the Mcp-Session-Id header in your HTTP requests.

Recommendation: Switch to the streamable-http transport and use the /mcp endpoint, which handles session negotiation automatically during the initial handshake.