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LeadProof

Create an order (PayPal approval link)

create_order

Creates the order and returns a PayPal approval link. The PERSON must open the link and approve the payment; nothing is charged until they do. Ask for the delivery email first: the CSV and the order page go there. Returns the order id, the approval link, an order-status link and a status token for get_order_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qtyYesHow many verified leads to deliver, 100 to 1000.
emailYesDelivery email. The CSV download and order updates go here.
locationsYesCities, regions or countries, e.g. ["Austin, TX", "Dallas, TX"]. Anything Google Maps can resolve.
min_reviewsNoOnly businesses with at least this many Google reviews. 0 means no filter. A higher bar costs more per lead and screens out more businesses.
business_typesYesBusiness categories to find, e.g. ["HVAC contractor"]. Each one becomes a Google Maps search term.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare the mutation profile (readOnly=false, idempotent=false, openWorld=true); the description adds the genuinely novel behavior that a PERSON must open the link and that nothing is charged until approval. That human-in-the-loop, deferred-charge semantics is exactly the context annotations cannot convey.

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?

Four tight sentences, front-loaded with the action and its return, then the approval caveat, then the email prerequisite, then the returned fields. The email sentence partially overlaps the schema's email description, a minor redundancy.

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 no output schema, the description carries the return contract itself (order id, approval link, order-status link, status token) and explains the payment-approval flow. Nothing an agent needs to call and follow up on this mutation 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%, so all five parameters including the min_reviews enum and qty bounds are already documented. The description only adds a prioritization nudge ('Ask for the delivery email first') and does not add format or constraint meaning beyond the schema, matching the baseline 3.

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+resource ('Creates the order') and immediately names the distinctive output ('returns a PayPal approval link'), which separates it from siblings like get_checkout_link and quote_lead_list. An agent can identify this as the order-creation step without opening the schema.

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?

Gives a clear prerequisite ('Ask for the delivery email first') and routes the agent to get_order_status via the status token. It implies the workflow position but never explicitly contrasts with siblings such as get_checkout_link or quote_lead_list, so when-not-to-use remains unstated.

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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