Skip to main content
Glama
industrial-aiops

OT-AIops Energy

diff_alias_map

Read-only

Diff a new cross-protocol asset discovery against the adopted baseline to report added, removed, renamed, and reclassified tags, with a stable or changed verdict.

Instructions

[READ][risk=low] Diff a fresh discovery run against the adopted baseline.

Loads the site's previously adopted alias map, re-runs the cross-protocol
asset model over ``feeds``, and reports how the address space moved: tags
added / removed / renamed (same ref, new alias) / reclassified (same ref+alias,
new semantic class), plus a stable|changed verdict. Adopt a baseline first
with ``adopt_alias_map``.

Args:
    feeds: Fresh per-protocol tag feeds (same shape as adopt_alias_map).
    site: Site label whose baseline to diff against. Default 'site'.

Returns dict: {site, verdict, counts:{added,removed,renamed,reclassified},
    added:[...], removed:[...], renamed:[...], reclassified:[...]}.

Example: diff_alias_map(feeds=[{"protocol":"opcua","source":"l1","tags":[...]}],
    site="plant").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteNo
feedsYes
Behavior4/5

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

While annotations already declare readOnlyHint=true and destructiveHint=false, the description adds behavioral context by explaining that the tool 're-runs the cross-protocol asset model over feeds' and reports how the address space moved. It also discloses the return structure with counts and lists, which is valuable beyond the annotations. There is no contradiction with annotations.

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 well-structured and front-loaded: it opens with a concise one-line summary, then explains the process, defines arguments, describes the return dict, and provides a concrete example. Every sentence adds useful information without unnecessary verbosity.

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?

Given that there is no output schema, the description includes the full return dict structure with counts and lists, which is important for the agent. It also gives a full example invocation. The main gaps are the site default inconsistency and the fact that 'feeds' format is only described by reference to 'adopt_alias_map,' which might require the agent to look up that tool's schema. Overall, the description is robust.

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

Parameters3/5

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

The schema has zero description coverage, so the description must compensate for parameter meaning. It explains 'feeds' as fresh per-protocol tag feeds of the same shape as adopt_alias_map, and 'site' as the site label whose baseline to diff against, defaulting to 'site.' This adds meaning not present in the schema, but the description relies on referencing another tool's shape for 'feeds' and there is a minor discrepancy: the schema default for 'site' is null, while the description says default is 'site.'

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] Diff a fresh discovery run against the adopted baseline,' which clearly states the tool's function with a specific verb (diff) and resource (alias map). It distinguishes itself from the sibling tool 'adopt_alias_map' by explaining it diffs against an adopted baseline, making the purpose unambiguous.

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?

The description provides explicit usage context by stating 'Adopt a baseline first with adopt_alias_map,' which is a clear prerequisite. It also describes the categories of output (added, removed, renamed, reclassified) and the stable|changed verdict, implying when this tool is appropriate. It does not explicitly mention alternatives, but the prerequisite and operational detail offer solid guidance.

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