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optimize-overseas

LACRM MCP Server

Get Company Schema

get_company_schema

Retrieve the complete field schema needed for creating or editing companies in Less Annoying CRM. Get all required and optional fields, their types, formats, and custom field indicators before creating a company.

Instructions

Get complete field schema for creating/editing companies.

Returns ALL fields (both fixed system fields and custom fields) with:

  • name: The parameter name to use in create_contact (with is_company=true)

  • required: Whether this field must be provided

  • type: Field type (Text, Array, etc.)

  • input_format: Exact format expected for the value

  • is_custom_field: Whether this is a custom field (true) or built-in (false)

  • notes: Additional guidance for using this field

ALWAYS call this before creating a company to understand what data is needed. Note: Companies are created using create_contact with is_company=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the burden of disclosing behavior. It thoroughly describes the output structure (fields like name, required, type, input_format, is_custom_field, notes) and confirms this is a read-only schema retrieval. It also reveals the important relationship to create_contact. However, it doesn't mention potential edge cases such as dynamic custom fields based on account configuration, so not a perfect 5.

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 moderately long but efficiently structured with bullet points, intro sentence, and a final usage note. Every element adds meaning, but the bullet list could potentially be condensed without losing core details. Thus 4.

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?

The description fully covers the tool's purpose, usage instructions, and return format. Since there is no output schema, it compensates by enumerating all returned attributes and their meanings. For a zero-parameter tool with no annotations, this is comprehensive and leaves no critical gaps.

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 accepts zero parameters, so the schema provides no property definitions. Per baseline, a 0-parameter tool scores 4, and the description adds no input parameter semantics (it describes output fields instead). No deduction needed.

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's function: retrieving the complete field schema for creating or editing companies. It distinguishes itself from siblings like get_contact_schema by specifying company context and referencing create_contact with is_company=true.

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?

The description explicitly instructs to ALWAYS call this tool before creating a company to understand required data. It also clarifies that companies are created via create_contact with is_company=true, which prevents misuse. While it doesn't list alternatives, the context makes the use case unambiguous.

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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