Enapter MCP Server

A Model Context Protocol (MCP) server that provides seamless integration with the Enapter EMS. This server enables AI assistants and other MCP clients to interact with Enapter sites, devices and telemetry data.

Connecting Your AI Application

The Enapter MCP Server is available as a public hosted service at https://mcp.enapter.com/mcp. It uses streamable HTTP transport and OAuth 2.0 for authentication.

For specific instructions on how to connect your preferred AI client, please refer to the following guides:

• Claude Desktop
• Claude Mobile
• Claude Code
• Copilot Studio
• ChatGPT

Self-Hosting

If you prefer to run your own instance, you can self-host the server using Docker:

docker run --rm --name enapter-mcp-server \
  -p 8000:8000 \
  enapter/mcp-server:v1.9.0

The server can be configured using environment variables and command-line arguments.

Available Tools

The server exposes the following tools for interacting with the Enapter EMS:

Tool	Description	Access	Default
search_sites	Search among all sites with name and timezone regex filtering	Read-only	Enabled
search_devices	Search devices by site, type, and name regex filtering	Read-only	Enabled
search_command_executions	Search the history of command executions	Read-only	Enabled
read_blueprint	Access device blueprint sections (properties, telemetry, alerts)	Read-only	Enabled
get_historical_telemetry	Retrieve time-series telemetry with configurable granularity	Read-only	Enabled
search_rules	Search for automation rules within a specific site	Read-only	Enabled
read_rule	Read the paginated lines of a rule's Lua script	Read-only	Enabled
execute_command	Execute a command on a device	Read-write	Disabled
create_rule	Create a disabled MCP-managed automation rule	Read-write	Disabled
edit_rule	Apply a content-match edit to a disabled MCP-managed rule	Read-write	Disabled
delete_rule	Delete a disabled MCP-managed automation rule	Read-write	Disabled

Usage Examples

Here are realistic examples of how you can interact with your Enapter devices using AI assistants:

Example 1: Diagnostic Troubleshooting

User prompt:

One of our inverters just went offline. Can you check its status and tell me what the active alerts mean?

What happens:

• Server finds the specific inverter device
• Retrieves its current connectivity status and active alerts
• Reads the device blueprint to translate alert codes into human-readable descriptions
• Presents a summary of the issue to the user

Example 2: Historical Analysis & Performance

User prompt:

What was the average power consumption and temperature for the main HVAC system over the last 7 days?

What happens:

• Server locates the HVAC system device
• Checks its blueprint to identify the correct telemetry metric names for power consumption and temperature
• Fetches the historical telemetry data for the requested time period
• Calculates and presents the averages to the user

Example 3: Auditing Command Executions

User prompt:

Check if anyone tried to turn on the water pump this morning. Were there any errors during the execution?

What happens:

• Server locates the specific water pump device
• Reads the device blueprint to find the exact command name for "turning on" the device
• Searches the command execution history for that specific command executed this morning
• Retrieves the execution status and any associated error messages
• Reports back whether the command succeeded or failed

Example 4: Auditing Automation Rules

User prompt:

Can you check the automation rules running at the Alpha site? I need to verify the logic that automatically starts the electrolyser when excess solar power is available.

What happens:

• Server searches for rules within the specified site using search_rules
• Identifies the relevant rule based on its name/slug
• Retrieves the rule's Lua script using read_rule
• Analyzes the logic and confirms the exact threshold and conditions that trigger the electrolyser

Example 5: Executing a Command (Human Confirmation Required)

⚠️ execute_command is destructive — it acts on real physical hardware (pumps, electrolysers, valves, inverters). It is disabled by default. Enable it with --command-execution-enabled on the command line or by setting ENAPTER_COMMAND_EXECUTION_ENABLED=1.

User prompt:

The electrolyser at the Alpha site has been running for a long time. Please reboot it for me.

What happens:

• Server locates the electrolyser device using search_devices
• Reads the device blueprint with read_blueprint(section="commands") to discover the reboot command and checks whether it declares a confirmation block
• The reboot command declares a confirmation with a title and description (e.g. "Reboot the electrolyser" / "This will restart the device and interrupt production."), so the assistant presents these to the human and waits for explicit approval — it does not act on its own initiative
• Only after the human confirms does the assistant call execute_command with human_confirmed_this_action=True
• The device runs the command and the tool returns the resulting CommandExecution, whose state field reports the outcome (success/error/timeout/unsync)
• The returned execution id can later be referenced or audited via search_command_executions

Example 6: Authoring and Editing MCP-Managed Automation Rules

⚠️ create_rule, edit_rule, and delete_rule are destructive — they modify automation that can execute commands on physical energy hardware. They are disabled by default. Enable them with --rule-editing-enabled on the command line or by setting ENAPTER_RULE_EDITING_ENABLED=1.

MCP-managed rules are automation rules whose lifecycle is managed through the MCP server. They follow a strict workflow:

1. Slug prefix. MCP-managed rules must have a slug starting with the reserved prefix mcp- (case-sensitive, byte-exact). The prefix is an ownership boundary: the assistant may mutate only rules explicitly marked as MCP-managed.
2. Created disabled. create_rule always creates rules disabled, so the assistant cannot publish new live automation. A human reviews the rule in the Enapter UI and enables it when ready.
3. Mutate disabled only. edit_rule and delete_rule refuse to operate on enabled rules, even when the slug has the mcp- prefix. If a human enables an MCP-managed rule and later wants the assistant to edit or delete it, the human must disable it first, ask the assistant, review the result, and re-enable in the Enapter UI. This keeps responsibility for live automation changes with the human.
4. No enable tool. The server does not expose an enable operation. Enabling is a human decision made in the Enapter UI.

User prompt:

Create a rule at the Alpha site that logs "battery low" when the battery SOC drops below 20%.

What happens:

• The assistant creates the rule with create_rule(site_id="...", slug="mcp-battery-low", script_code="..."). The rule is created disabled and uses runtime version v3.
• The human reviews the rule in the Enapter UI and enables it.

User prompt (later):

Change the threshold in the battery-low rule from 20% to 15%.

What happens:

• The assistant calls read_rule to get the current script.
• It identifies the exact snippet to change and calls edit_rule(rule_id="...", old_string="battery_soc < 20", new_string="battery_soc < 15"). The edit succeeds only if the old string appears exactly once in the script and the rule is currently disabled.
• The human reviews the change and re-enables the rule in the Enapter UI.

Support

For issues, questions, or contributions, please:

• Email us at support@enapter.com.
• Open a GitHub Issue.
• Join our Discord server.
• Check our Contributing Guide.

Privacy Policy

For information about how we handle data, please refer to the Enapter Privacy Policy.

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