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industrial-aiops

OT-AIops

opcua_read_history

Read-only

Read historical OPC-UA node values over a specified time window, returning raw data with timestamps and status codes, and notes if the node is not historized.

Instructions

[READ][risk=low] OPC-UA Historical Access (HDA): raw historical values over a window.

Reads stored history for a node via the server's HistoryRead service, bounded
by ``max_points``. Returns a clear 'unsupported' note when the server does not
historize the node (no crash).

Args:
    node_id: The OPC-UA node id to read history for (e.g. ns=2;i=5).
    endpoint: Endpoint name from config.
    start: ISO-8601 window start (default: 1 hour before end).
    end: ISO-8601 window end (default: now).
    max_points: Max points to return (capped server-side at 2000).

Returns dict: {node_id, supported (bool), start, end, count,
    values:[{value, source_timestamp, status_code}]}.

Example: opcua_read_history(node_id="ns=2;i=5", start="2026-06-28T08:00:00Z").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
startNo
node_idYes
endpointNo
max_pointsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, openWorldHint, destructiveHint), the description reveals that it returns an 'unsupported' note (no crash) and that max_points is capped server-side at 2000. This adds meaningful behavioral context.

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 concise and well-structured: a tag line, a one-sentence summary, parameter descriptions, return format, and an example. Every sentence is necessary and front-loaded with key information.

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

Completeness4/5

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

The description covers parameters, behavioral notes, and the return dict structure. It is largely complete given no output schema, though it could elaborate on status_code values.

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?

With 0% schema description coverage, the description fully explains all 5 parameters: node_id, endpoint, start, end, max_points. It provides formats, defaults, and an example, adding significant value over the schema alone.

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 starts with '[READ][risk=low] OPC-UA Historical Access (HDA): raw historical values over a window.' This clearly states the tool reads historical data and distinguishes it from other OPC-UA tools like browse or read node.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives like historian_query or opcua_read_node. It mentions error handling for unsupported nodes, but no guidance on selection criteria.

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

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