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LCM2M

lcm2m-caddis-mcp

by LCM2M

Get company details

caddis_get_company
Read-onlyIdempotent

Fetch the active company's name, timezone, and point-of-contact to confirm which company the session is scoped to.

Instructions

Fetch the active company (name, timezone, point-of-contact, and other top-level settings). Useful as a first call to confirm which company the session is scoped to.

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

No arguments

Behavior5/5

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

The description discloses the non-standard TOON encoding format in extensive detail, including syntax rules, null handling, and quoting. Annotations already declare the tool as readOnly, openWorld, and idempotent. The description adds significant behavioral transparency beyond annotations, fully informing the AI agent of what to expect in the response.

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

Conciseness4/5

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

The description is front-loaded with purpose and usage, followed by a detailed format explanation. While the format section is lengthy, it is well-structured and every sentence provides value for understanding the response. Slight verbosity keeps it from a 5, but it remains clear and organized.

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 fully explains the return format and structure, including examples. Given the tool's zero parameters, the description covers all necessary context: what it does, when to use it, and how to interpret the response. It is complete for effective use.

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

Parameters4/5

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

The tool has zero parameters, so the description cannot add meaning beyond the schema. Per guidelines, baseline is 4. The description does not need to elaborate on parameters since there are none.

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 the tool fetches the active company's top-level settings (name, timezone, point-of-contact). This distinguishes it from sibling tools that deal with devices, equipment, runs, etc. The verb 'Fetch' and specific resource 'company' with listed fields make 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 explicitly recommends using this tool 'as a first call to confirm which company the session is scoped to', providing clear context for when to use it. It does not explicitly mention when not to use it or alternatives, but given the uniqueness of this tool among siblings (no other company-fetching tool), this is not necessary.

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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