Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@IndustrialOps Industry 4.0 PLC Monitor MCP Server Show the current register values for PLC_ASSEMBLY_01"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that connects LLMs to factory PLC sensors, machinery health metrics, and predictive maintenance diagnostics.
This server exposes 3 tools over standard I/O (stdio) transport:
read_plc_registersFetches current register readings (RPM, PSI, Temperature, Vibration, Status) for a designated PLC.
Parameters:
plc_id (string, required): PLC identifier (e.g., "PLC_ASSEMBLY_01", "PLC_PACKAGING_02").
get_predictive_maintenance_reportAnalyzes machinery vibration telemetry and temperatures to estimate Remaining Useful Life (RUL) hours and schedule preventative inspections.
get_energy_consumptionExposes active power draw (kW) profiles and efficiency ratings for factory equipment load planning.
Related MCP server: OPC UA MCP Server
# 1. Setup environment
cd industrialops-mcp-server
python -m venv venv
.\venv\Scripts\activate
# 2. Install package
pip install -e .{
"mcpServers": {
"industrialops": {
"command": "D:/Projects/industrialops-mcp-server/venv/Scripts/python.exe",
"args": [
"-m",
"industrialops_mcp.server"
]
}
}
}python -m pytest -o pythonpath=srcFetches active power draw (kW) metrics and energy efficiency ratings for factory machinery.
| Name | Required | Description | Default |
|---|---|---|---|
| plc_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It correctly implies a read operation but does not detail potential failures, authorization needs, or side effects. The description is adequate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that is front-loaded and contains no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and an output schema, the description provides the core functionality. However, it lacks usage guidance and parameter description, making it minimally sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% coverage (parameter descriptions missing), and the description does not elaborate on the required 'plc_id' parameter. While the parameter is intuitive given the tool name, the description adds no additional meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Fetches' and clearly identifies the resources: active power draw metrics and energy efficiency ratings. It distinguishes itself from sibling tools like read_plc_registers and get_predictive_maintenance_report by focusing on energy consumption data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus its siblings. There is no mention of prerequisites, context, or scenarios where this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Calculates machinery wear coefficients and Remaining Useful Life (RUL) hours for preventative repairs.
| Name | Required | Description | Default |
|---|---|---|---|
| plc_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It discloses that the tool calculates wear coefficients and RUL, implying a read-only, computational operation. However, it does not mention authorization, side effects, rate limits, or any computational intensity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, efficiently conveys the core purpose without extra words. However, it could be improved by integrating parameter context without significant length increase.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one required input and an output schema (not shown). The description fails to explain the input semantics or output format. Given the complexity of predictive maintenance, more detail is needed for the agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description does not mention the only parameter (plc_id) or its role. The agent has no context on what plc_id represents or how it affects the report.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it 'calculates machinery wear coefficients and Remaining Useful Life (RUL) hours for preventative repairs,' which specifies the verb, resource, and outputs. It distinguishes from siblings like read_plc_registers and get_energy_consumption.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. The context of preventative repairs is implied but no alternatives or prerequisites mentioned. The sibling tools suggest possible complementary use, but not directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetches real-time sensor metrics (RPM, PSI, Temperature, Vibration, Status) for a factory PLC (e.g. 'PLC_ASSEMBLY_01', 'PLC_PACKAGING_02').
| Name | Required | Description | Default |
|---|---|---|---|
| plc_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions 'fetches' and 'real-time' which imply a read operation, but does not disclose potential side effects, error handling for invalid PLC IDs, authentication requirements, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the key information (purpose and examples) without any fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no annotations, output schema exists), the description is adequate but lacks details on behavior for invalid inputs or how to obtain the list of valid PLC IDs. It does not explain the output format beyond mentioning metrics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by providing example PLC IDs ('PLC_ASSEMBLY_01', 'PLC_PACKAGING_02'), adding value to the parameter semantics. However, it does not specify format or enumeration constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches real-time sensor metrics (RPM, PSI, Temperature, Vibration, Status) for a factory PLC, with specific example IDs. It uses a specific verb and resource, and distinguishes from siblings like get_predictive_maintenance_report and get_energy_consumption.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for real-time sensor data but does not explicitly state when to use this tool versus alternatives or provide exclusions. No guidance on prerequisites or scenarios where other tools are better suited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0get_energy_consumptionget_predictive_maintenance_reportread_plc_registersEach tool targets a distinct aspect of PLC monitoring: real-time registers, predictive maintenance, and energy consumption. No overlap or confusion possible.
All tool names follow the consistent 'verb_noun' pattern using snake_case ('read_plc_registers', 'get_predictive_maintenance_report', 'get_energy_consumption').
With 3 tools, the server is focused and each tool earns its place. The count is within the 3-15 well-scoped range.
For a monitoring-only server, the tools cover real-time data, predictive maintenance, and energy metrics. No obvious gaps for the stated purpose.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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