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LCM2M

lcm2m-caddis-mcp

by LCM2M

Get one org unit

caddis_get_org_unit
Read-onlyIdempotent

Fetch a single organizational unit with its equipment and child units, returning data in TOON-encoded format.

Instructions

Fetch a single organizational unit with its direct equipment and child org units. For the whole tree, use caddis_get_tree instead.

Responses are TOON-encoded (toonformat.dev) — a token-efficient JSON dialect mixing YAML-style indentation with CSV-style tables. Example:

name: Caddis Co timezone: America/Denver equipment[3]{id,name,tags,current_status.status,current_status.reason_id}: 1,Mill A,"["cnc","critical"]",running,null 2,"Press, Big",null,down,3 3,Lathe C,null,null,null

  • Object fields: key: value; nested objects indent their children.

  • Uniform arrays of objects: field[N]{cols}: followed by N indented comma-separated rows in column order.

  • Nested objects inside table rows are recursively flattened to dotted columns (e.g. current_status.status, input_setup.cycle.logic); a null parent yields null across all its dotted columns (see row 3 above).

  • Primitive arrays at object level: field[N]: a,b,c inline.

  • Arrays inside table cells are JSON-stringified into a single cell value (JSON.parse() to recover); empty arrays render as null (see rows 1–3 tags column).

  • Strings with commas/colons/quotes/leading whitespace are double-quoted (escapes: \\, \"); other strings, numbers, booleans, and null are bare.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orgUnitIdYesNumeric identifier (accepts either string or number form)
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. The description adds behavioral context about response format (TOON-encoded) and what data is returned (direct equipment and child org units), though it does not detail authentication or rate limits. This adds value beyond annotations.

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

Conciseness3/5

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

The description is front-loaded with the main purpose and alternative, but then includes an extensive, verbose explanation of the TOON encoding format with examples. This reduces conciseness, as the format details could be more compact or placed elsewhere.

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?

Given the tool has one parameter, no output schema, and annotations, the description fully covers what the tool does, what it returns, and how to parse the response. It also distinguishes from sibling tools, making it complete for an AI agent.

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?

Schema description coverage is 100%, and the schema already describes 'orgUnitId' as a numeric identifier. The description does not add any new information about parameters, so it meets the baseline for high coverage.

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 clearly states 'Fetch a single organizational unit with its direct equipment and child org units,' which is a specific verb (fetch) and resource (org unit). It distinguishes from sibling 'caddis_get_tree' by noting that alternative is for the whole tree.

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

Usage Guidelines5/5

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

Explicitly provides when-to-use (fetch one org unit) and when-not-to-use ('For the whole tree, use caddis_get_tree instead'), offering a clear alternative.

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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