Admin Substitute

formation.recommend_trade

Read-only

AI Trade Recommendation Engine — "I have $X and want to start a trade business" → ranked recommendations. Takes budget, state, goals, physical capability, risk tolerance and preferences, then scores ALL 9 trade categories across 4 factors (budget fit, time to revenue, revenue upside, barrier to entry) and returns top 5 ranked recommendations with: startup costs, licence requirements, projected Year 1 revenue (3 scenarios), break-even timeline, quick-win first actions, risks, and scalability rating. Perfect for "what trade should I start?" questions.

Input Schema

TableJSON Schema

Name	Required	Description
`state`	Yes	Australian state/territory: NSW, VIC, QLD, SA, WA, TAS, NT, ACT. Required.
`budget`	Yes	Available startup budget in AUD. Required.
`suburb`	No	Optional suburb for demand matching.
`hasVehicle`	No	Already has a work vehicle?
`preferIndoor`	No	Prefers indoor work?
`preferOutdoor`	No	Prefers outdoor work?
`riskTolerance`	Yes	Risk appetite. Required.
`existingSkills`	No	Freetext existing skills list.
`timeCommitment`	Yes	How involved they want to be. Required.
`physicalCapability`	Yes	Can they do physical work? Required.
`wantsLicencedTrade`	No	Open to 3-4yr apprenticeship for licensed trade?
`wantsToHireQuickly`	No	Wants to scale with employees fast?
`targetMonthlyRevenue`	No	Target monthly revenue in AUD.
`wantsRecurringRevenue`	No	Prefers recurring/maintenance revenue streams?

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.5/5.0

Behavior5/5

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

Annotations confirm readOnlyHint=true (safe, non-destructive). The description adds detail about the scoring process (4 factors, all 9 trades, top 5 results) and output fields (startup costs, revenue scenarios, etc.), leaving no behavioral surprises.

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 a single paragraph but efficiently packs information: purpose, input specifics, scoring details, output fields, and use case. It is front-loaded and each sentence adds value, though slightly longer than minimal.

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 complexity (14 parameters, output schema exists, read-only), the description covers the core functionality and output. It mentions the 9 trades and 4 factors not in schema, and the output schema covers return structure. Missing edge cases or error conditions, but adequate for typical 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?

Schema coverage is 100% with descriptions for all parameters. The description adds value by introducing the 4 scoring factors (budget fit, time to revenue, revenue upside, barrier to entry) and grouping inputs conceptually, which goes beyond the schema.

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 is a trade recommendation engine that takes user inputs (budget, state, goals, etc.) and returns ranked recommendations with specific output details. It distinguishes itself from siblings like formation.checklist or formation.demand_hotspots by focusing on trade selection.

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 says 'Perfect for "what trade should I start?" questions', indicating when to use. However, it does not provide explicit when-not-to-use guidelines or compare with alternative tools, though siblings are available.

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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