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Oxaide — Singapore company research for AI agents

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create_audit

Create a private research job (Oxaide). Returns a job id immediately; the AI quote (S$49 Quick check / S$390 Pack / S$1500 Board pack) is emailed within ~15m. Pay with pay_audit before any work starts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefYesThe research question, in plain language (SG corporate/wealth topics: ACRA/URA/GeBIZ/CPF/REIT etc).
emailYesDelivery email. Quote, files, and refunds go here. Required: delivery is email-first.
sourceNoOptional caller tag, default "mcp".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / email / description
      Previous value: -"Delivery email — quote, files, and refunds go here. Required: delivery is email-first."New value: +"Delivery email. Quote, files, and refunds go here. Required: delivery is email-first."
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare the mutation profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false). The description adds real value beyond them: immediate job-id return, the ~15m email turnaround for the quote, pricing tiers, and the payment prerequisite.

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?

Three tight sentences, front-loaded with the action and immediate return, then timing/pricing, then the prerequisite. Every sentence carries distinct, non-redundant information.

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, yet the description covers the return (job id), the follow-up artifact (emailed quote), cost tiers, and the hard dependency on pay_audit. Annotations cover the safety profile, and 100% schema coverage handles parameters.

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 the schema already documents brief, email, and source with context (delivery is email-first, refunds route to email). The description adds no parameter-level detail beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("Create a private research job (Oxaide)") and adds the immediate outcome ("Returns a job id immediately"). It is clearly distinguishable from get_audit/list_audits by verb, though it does not explicitly name those siblings.

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?

Explicitly states the prerequisite workflow: "Pay with pay_audit before any work starts," naming the sibling tool and the ordering constraint. It provides clear context but no explicit when-not-to-use or exclusion criteria.

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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