Admin Substitute

quotes.ai_price_suggestion

Read-onlyIdempotent

AI-powered pricing intelligence. Analyses the 930+ item supplier catalog, company price book, and historical quotes to suggest optimal pricing with detailed reasoning. Returns line items, adjustments (urgency/complexity/location), market context, and confidence scores. ACCC-compliant: suggestions only, human decides final price. Requires Growth+ plan.

Input Schema

TableJSON Schema

Name	Required	Description
`trade`	No	Trade type (e.g. "plumbing", "electrical", "hvac"). Optional — auto-detected from company profile if omitted.
`leadId`	No	Optional lead ID to pull additional context (customer name, service type, address).
`suburb`	No	Suburb/location for area-based pricing adjustments (e.g. "Bondi", "Pilbara"). Optional.
`urgency`	No	Job urgency level. "urgent" = same/next day (+15-25%). "emergency" = after-hours/immediate (+50-100%). Defaults to "standard".
`complexity`	No	Job complexity. Affects labour time and pricing. Defaults to "moderate".
`description`	Yes	Plain English job description. Be specific: include scope, materials, access conditions. Required.
`includeBreakdown`	No	Include detailed material + labour breakdown. Defaults to true.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`suggestion`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context: it is AI-powered, uses specific data sources, returns line items, adjustments, market context, and confidence scores. It also notes compliance and plan requirements, exceeding what annotations provide.

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 four sentences long, front-loaded with the main purpose, and includes necessary details without redundancy. Every sentence adds value.

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 the existence of an output schema and annotations, the description provides sufficient context: data sources, return types, compliance, and plan requirement. It does not discuss limits or pagination, but for a suggestion tool, this is adequate.

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%, so each parameter is documented. The description does not significantly elaborate on parameter meanings beyond the schema, but the tool purpose and return types are clear. Baseline score of 3 is appropriate.

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 purpose: 'AI-powered pricing intelligence' that analyzes supplier catalog, price book, and historical quotes to suggest optimal pricing. It distinguishes itself from sibling tools like quotes.create by emphasizing it is a suggestion tool, not a quote creation tool.

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 implies when to use the tool (for price suggestions before finalizing a quote) and mentions ACCC compliance and human decision requirement, but does not explicitly state alternatives or when not to use it. Sibling tool names provide context for differentiation.

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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TDQS

A3.8/5.0

Disambiguation4/5

Most tools have distinct resource+action naming (e.g., leads.create, jobs.list), but some overlap exists in analytics tools (dashboard, detailed, financials) and workflow automations (process_lead vs google_ads_pipeline). Descriptions clarify purposes, so slight confusion is possible but limited.

Naming Consistency5/5

All tools follow a consistent `domain.action` pattern (e.g., leads.create, billing.status). No mixing of camelCase or snake_case. Even complex names like lifecycle.assess or scaling.readiness_score adhere to the convention.

Tool Count1/5

95 tools is far beyond typical well-scoped servers (3-15). While the server aims to cover an entire business management platform, this volume is overwhelming for an agent, making selection and memory difficult.

Completeness5/5

The tool surface is exceptionally comprehensive, covering formation, hiring, compliance, funding, leads, quotes, jobs, invoicing, payments, marketing, analytics, integrations, webhooks, workflows, and more. Almost no obvious gaps in the lifecycle of running a trade business.

Resources

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