Skip to main content
Glama
industrial-aiops

OT-AIops Energy

monitor_changes

Read-only

Monitor only value changes in OT points: polls a reference, applies deadband, and returns timestamped changes with hard limits, across OPC-UA, Modbus, S7, and more.

Instructions

[READ][risk=low] Capture only the value CHANGES of a point over a bounded window.

Polls ``ref`` and returns only the changes (with timestamps), not every
sample — the OT deadband-report pattern. Works across OPC-UA / Modbus / S7 /
Mitsubishi MC / EtherNet/IP. Hard-capped by duration_s and max_changes (never
an infinite loop).

Args:
    ref: Point to watch — OPC-UA node id, Modbus address, S7 address string,
        MELSEC device, or Logix tag (per the endpoint's protocol).
    endpoint: Endpoint name from config.
    duration_s: Wall-clock window in seconds (1..120, capped server-side).
    interval_ms: Poll interval in milliseconds (>=50).
    deadband: Numeric change must exceed this to count (0 = any change).
    max_changes: Stop after this many changes (1..500, capped server-side).

Returns dict: {endpoint, ref, duration_s, interval_ms, deadband, samples_polled,
    change_count, changes:[{value, previous, source_timestamp, wall_clock}]}.

Example: monitor_changes(ref="ns=2;i=5", endpoint="line1", duration_s=20, deadband=0.5).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refYes
deadbandNo
endpointNo
duration_sNo
interval_msNo
max_changesNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only and non-destructive, but the description adds significant behavioral detail: hard caps on duration and max_changes (preventing infinite loops), polling interval semantics, deadband threshold meaning, and the exact return shape with timestamps. This goes well beyond the annotations and helps the agent anticipate real-world behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured logically: summary, behavioral note, argument list, return spec, and an example. Every sentence adds value, and the formatting makes it easy to scan. Length is justified by the tool's complexity and the lack of schema documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description supplies a full return dict. It covers all six parameters, constraints, protocol support, and even gives a concrete example. In the context of many sibling monitoring tools, this description uniquely clarifies the change-only, bounded-window behavior, making it complete for an AI agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description fully carries parameter meaning. It explains each argument (ref, endpoint, duration_s, interval_ms, deadband, max_changes) with types, constraints, defaults, and protocol-specific context. The return dict is also detailed, making the tool self-contained despite the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific, actionable statement: "Capture only the value CHANGES of a point over a bounded window." It clearly identifies the resource (value changes of a point) and the action (capture) while explicitly contrasting with every-sample polling. The deadband-report pattern mention further distinguishes it from sibling historian tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear context on when to use the tool: when only changes matter, via the deadband-report pattern, and lists supported protocols. It doesn't explicitly state when not to use it or name alternative tools, but the bounded-window and change-only behavior implicitly set it apart from continuous or full-history query tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/industrial-aiops/industrial-aiops-energy'

If you have feedback or need assistance with the MCP directory API, please join our Discord server