duplocloud-mcp 0.1.4

Model Context Protocol (MCP) server for DuploCloud.

DuploCloud MCP Server

A Model Context Protocol (MCP) server for DuploCloud. Dynamically discovers duploctl commands and exposes them as MCP tools so AI agents and compatible clients can query infrastructure state and perform auditable actions.

Built on FastMCP and the duplocloud-client Python package. Installs as a duploctl plugin — no separate command needed.

Table of Contents

• Features
• Prerequisites
• Installation
• Quick Start
• Configuration
  • Environment Variables
  • CLI Arguments
• Tool Modes
  • Expanded Mode
  • Compact Mode
• Filtering
  • Resource Filter
  • Command Filter
  • Combining Filters
• MCP Client Configuration
• Custom Tools and Routes
• Endpoints
• Docker
• Development
  • Project Structure
  • Running Tests
• References

Features

• duploctl plugin -- registers as a duploctl resource via entry points; run with duploctl mcp
• Automatic tool discovery -- all duploctl @Command methods are registered as MCP tools at startup
• Resource and command filtering -- regex-based filters to control which tools are exposed
• Pydantic model integration -- commands with models get typed input schemas instead of raw dicts
• Config display -- built-in config tool and GET /config route showing live server state
• Custom tool/route framework -- @custom_tool and @custom_route decorators with context injection
• Dual transport -- stdio (default) or HTTP for persistent servers

Prerequisites

• Python 3.10+
• DuploCloud credentials (DUPLO_HOST, DUPLO_TOKEN)
• A DuploCloud tenant (DUPLO_TENANT)

Installation

pip install duplocloud-mcp

For pinned version installs and alternative methods (GitHub release artifact, git tag), see the release notes.

For development:

git clone https://github.com/duplocloud/mcp.git
cd mcp
pip install -e ".[test]"

Quick Start

Set your DuploCloud credentials and start the server:

export DUPLO_HOST="https://your-portal.duplocloud.net"
export DUPLO_TOKEN="your-token"
export DUPLO_TENANT="your-tenant"

duploctl mcp

By default the server starts in stdio transport with compact mode (3 tools). To use HTTP:

duploctl mcp --transport http

Verify it's running:

curl http://localhost:8000/health
# {"status":"healthy","service":"duplocloud-mcp"}

curl http://localhost:8000/config
# Full server configuration including registered tools

Configuration

Every setting can be provided as a CLI argument or an environment variable. CLI arguments take precedence.

Environment Variables

Variable	Default	Description
DUPLO_HOST	--	DuploCloud portal URL (required)
DUPLO_TOKEN	--	Authentication token (required)
DUPLO_TENANT	--	Tenant name (optional but recommended)
DUPLO_MCP_TRANSPORT	stdio	Transport protocol (stdio or http)
DUPLO_MCP_PORT	8000	Port for HTTP transport
DUPLO_MCP_RESOURCE_FILTER	.*	Regex filter for resource names
DUPLO_MCP_COMMAND_FILTER	.*	Regex filter for command names
DUPLO_MCP_TOOL_MODE	compact	Tool registration mode (compact or expanded)

CLI Arguments

duploctl mcp [--transport {stdio,http}]
             [--port PORT]
             [--resource-filter PATTERN]
             [--command-filter PATTERN]
             [--tool-mode {expanded,compact}]

All duploctl global arguments (-H, -t, -T, etc.) are also accepted and passed through to the DuploCloud client.

Tool Modes

The --tool-mode flag controls how duploctl commands are exposed as MCP tools.

Expanded Mode

Registers one tool per resource+command combination.

duploctl mcp --tool-mode expanded

Produces tools like tenant_create, tenant_find, service_list, etc. Each tool has its own input schema -- commands with Pydantic models (e.g. tenant_create) get full field-level schemas so the LLM sees every field name, type, and constraint.

• Pro: Precise schemas, easy for the LLM to call correctly
• Con: Many tools (potentially hundreds), may overwhelm tool selection

Compact Mode

Default. Registers four tools total, inspired by the duploctl bitbucket pipe.

duploctl mcp --tool-mode compact

Tool	Purpose
resources	List available resources (filtered)
explain_resource	List commands available on a resource
explain_command	Show arguments and body model schema for a specific command
execute	Run any duploctl command

The intended LLM workflow:

1. resources -- get the list of available resources
2. explain_resource(resource) -- see what commands are available
3. explain_command(resource, command) -- see argument details and body schema
4. execute(resource, command, ...) -- run the command

The execute tool accepts name, args, body, query, and wait parameters. It dispatches through the same DuploClient path as the CLI, so model validation, filtering, and formatting all work the same way.

• Pro: Only 4 tools, works well with tool-count-limited clients
• Con: LLM needs multiple calls to discover schemas

Filtering

Filters use Python regex with fullmatch semantics -- the entire name must match the pattern.

Resource Filter

Expose only specific resource types:

# Only tenant tools
duploctl mcp --resource-filter "tenant"

# Tenant and service tools
duploctl mcp --resource-filter "tenant|service"

# Everything related to batch
duploctl mcp --resource-filter "batch_.*"

# All resources (default)
duploctl mcp --resource-filter ".*"

Via environment variable:

export DUPLO_MCP_RESOURCE_FILTER="tenant|service|s3"
duploctl mcp

Command Filter

Expose only specific operations across all resources:

# Read-only -- only list and find commands
duploctl mcp --command-filter "list|find"

# Only create and delete
duploctl mcp --command-filter "create|delete"

Combining Filters

Filters compose as an intersection. This exposes only list and find for tenant and service:

duploctl mcp \
  --resource-filter "tenant|service" \
  --command-filter "list|find"

Result: tenant_list, tenant_find, service_list, service_find.

MCP Client Configuration

stdio Transport (Default)

For most MCP clients (Claude Code, VS Code, etc.), use stdio transport. Add to your .mcp.json (project root) or .vscode/mcp.json:

{
  "mcpServers": {
    "duploctl": {
      "command": "duploctl",
      "args": ["mcp"],
      "env": {
        "DUPLO_HOST": "https://your-portal.duplocloud.net",
        "DUPLO_TOKEN": "your-token",
        "DUPLO_TENANT": "your-tenant"
      }
    }
  }
}

HTTP Transport

For clients that connect over HTTP (persistent server):

duploctl mcp --transport http

{
  "mcpServers": {
    "duploctl": {
      "url": "http://localhost:8000/mcp",
      "type": "http"
    }
  }
}

With Filters

{
  "mcpServers": {
    "duploctl": {
      "command": "duploctl",
      "args": ["mcp", "--resource-filter", "tenant|service"],
      "env": {
        "DUPLO_HOST": "https://your-portal.duplocloud.net",
        "DUPLO_TOKEN": "your-token",
        "DUPLO_TENANT": "your-tenant"
      }
    }
  }
}

Custom Tools and Routes

The @custom_tool and @custom_route decorators let you add ad-hoc tools and HTTP routes that receive a Ctx object with the DuploCloud client and server config injected.

from duplocloud.mcp.ctx import Ctx, custom_tool, custom_route
from starlette.requests import Request
from starlette.responses import JSONResponse

# A plain function that does the work
def get_status(ctx: Ctx) -> dict:
    tenants = ctx.duplo.load("tenant").list()
    return {
        "tenant_count": len(tenants),
        "tools": ctx.tools,
    }

# Expose as an MCP tool
@custom_tool(name="status", description="Get environment status.")
def status_tool(ctx: Ctx) -> dict:
    return get_status(ctx)

# Expose the same logic as an HTTP route
@custom_route("/status", methods=["GET"])
async def status_route(ctx: Ctx, request: Request):
    return JSONResponse(get_status(ctx))

The ctx parameter is injected automatically and hidden from the tool's input schema -- MCP clients never see it.

Mode Selector

Decorators accept an optional mode parameter to conditionally register based on server mode:

@custom_tool(name="debug_info", mode="debug")
def debug_info(ctx: Ctx) -> dict:
    """Only registered when the server runs in debug mode."""
    return {"config": ctx.config}

Endpoints

Endpoint	Method	Description
/mcp	POST	MCP protocol endpoint (StreamableHTTP)
/health	GET	Health check for load balancers
/config	GET	Live server configuration and registered tools

Docker

The Docker image uses duploctl mcp as its entrypoint. Pass arguments via CMD or at runtime:

# Default (stdio transport, compact mode)
docker run -e DUPLO_HOST=... -e DUPLO_TOKEN=... -e DUPLO_TENANT=... duplocloud-mcp

# HTTP transport
docker run -e DUPLO_HOST=... -e DUPLO_TOKEN=... -e DUPLO_TENANT=... duplocloud-mcp --transport http

# Expanded mode with filters
docker run -e DUPLO_HOST=... -e DUPLO_TOKEN=... -e DUPLO_TENANT=... duplocloud-mcp \
  --tool-mode expanded --resource-filter "tenant|service"

Development

Project Structure

duplocloud/mcp/
  __main__.py        # Legacy entrypoint (use duploctl mcp instead)
  app.py             # FastMCP instance and health route
  args.py            # Declarative CLI argument definitions (legacy)
  ctx.py             # Ctx dataclass, @custom_tool, @custom_route
  config_display.py  # Built-in config tool and route
  server.py          # DuploCloudMCP resource plugin and lifecycle coordinator
  tools.py           # ToolRegistrar (duploctl -> MCP tool conversion)
  compact_tools.py   # Compact mode tools (execute, explain, resources)
  utils.py           # Docstring template resolution
tests/
  conftest.py        # Shared fixtures
  test_args.py       # Argument parsing and env var binding
  test_ctx.py        # Ctx, decorators, drain functions
  test_custom.py     # Context injection, register_custom, build_config
  test_filters.py    # Regex filter matching behavior
  test_server.py     # DuploCloudMCP init, filter application, self-exclusion
  test_modes.py      # Expanded and compact mode tests
  test_tools.py      # ToolRegistrar param building and wrapper construction

Running Tests

pip install -e ".[test]"
pytest

References

• duploctl -- CLI and Python client for DuploCloud
• Model Context Protocol -- MCP specification
• FastMCP -- Framework powering this server
• Keep a Changelog
• Semantic Versioning