Skip to main content
Glama
LCM2M

lcm2m-caddis-mcp

by LCM2M

Equipment schedule

caddis_get_equipment_schedule
Read-onlyIdempotent

Retrieve the current schedule for a piece of equipment, showing inheritance source and resolved timezone.

Instructions

Current schedule for a piece of equipment, including where the schedule was inherited from (equipment vs org unit vs company) and the resolved timezone.

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
equipIdYesNumeric identifier (accepts either string or number form)
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds value by detailing the response format (TOON) and the data included (schedule, inheritance, timezone), which is beyond the annotations.

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

Conciseness2/5

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

The description is lengthy due to a detailed tutorial on the TOON format. While informative, it is not concise for an AI agent, and much of the content could be abbreviated or moved to 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?

No output schema exists, so the description must fully explain the response. It does so with a clear example and detailed format rules, ensuring the agent can parse and understand the output.

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 coverage is 100% for the single parameter equipId, which is already described as numeric identifier. The description adds nothing beyond the schema, so baseline 3 applies.

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 it returns the current schedule for a piece of equipment, including inheritance and timezone. This is specific and distinguishes it from sibling tools like get_equipment or get_org_unit_schedule.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives such as get_org_unit_schedule. The description lacks usage context, prerequisites, or comparison with sibling 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/LCM2M/lcm2m-caddis-mcp'

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