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Singapore Buying Desk (Connective Labs)

Get Quote

request_service
Read-onlyIdempotent

Open the Get Quote form for the Singapore Buying Desk (Connective Labs Pte. Ltd.): the buyer lists the office, IT or event print items they need, sees instant all-in SGD prices, and sends the list for a firm quote. ChatGPT calls this from the Get Quote button with the business_id from the business feed (connective-labs-sg-buying-desk). Opening the form changes nothing; submitting it calls request_quote.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoOptional: lines to prefill in the form.
needed_byNoOptional: deadline to prefill, YYYY-MM-DD.
business_idYesprovider_business_id from the business feed. This desk: connective-labs-sg-buying-desk.
delivery_areaNoOptional: delivery area to prefill.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefillNo
businessYes
categoriesNo
business_idYes
how_it_worksNo
business_id_recognizedYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered; the description adds real value by clarifying 'Opening the form changes nothing', which confirms the no-side-effect behavior and defines the boundary with request_quote. It does not describe the returned form payload, but an output schema exists to carry that.

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?

Two sentences are front-loaded with the purpose and then the invocation/behavior, with no filler. The parentheticals (entity name, business_id slug) are dense but justified for disambiguation.

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 rich annotations, a full output schema, and 100% parameter coverage, the description only needs to add routing and behavior — which it does: purpose, trigger, no-op confirmation, and the hand-off to request_quote. Nothing an agent needs to call it correctly is missing.

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% and the descriptions are rich (regex-style examples, spec field lists, enums-by-example), so the schema does the heavy lifting. The description restates the business_id source already in the schema and only loosely gestures at the items prefill behavior, adding little beyond structured data. Baseline 3 applies.

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?

States a specific verb and resource ('Open the Get Quote form for the Singapore Buying Desk'), names the exact business entity, and explicitly distinguishes itself from the sibling that actually submits ('submitting it calls request_quote'). An agent can tell it apart from request_quote without opening either schema.

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?

Gives the exact trigger ('ChatGPT calls this from the Get Quote button with the business_id from the business feed') and names the alternative action and when it applies (submitting calls request_quote). Both the when-to-use condition and the responsibility boundary are explicit.

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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