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Evercraft Deck Capital Fit

Prepare a human-confirmed Capital Fit Sprint checkout

prepare_capital_fit_checkout

After the human explicitly agrees to pay $49, create the bounded capital-research case and secure Polar checkout. Checkout creation is not payment proof.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
timelineNo
request_idNo
website_urlNo
capital_goalNo
company_nameYes
contact_nameNo
contact_emailYes
existing_pitchNo
profit_contextNo
capital_purposeYes
revenue_contextNo
business_summaryNo
collateral_contextNo
user_confirmed_paymentYes
capital_type_preferenceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations indicate a write operation (readOnlyHint=false), not idempotent, and openWorldHint=true. The description adds concrete behavioral details: it creates a case and secures a checkout, and explicitly warns that checkout creation is not payment proof. This goes beyond the annotations and helps the agent understand side effects and limitations. No contradiction with annotations.

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?

Two sentences, front-loaded with the precondition, and zero filler. Every clause adds value: the action, the resource, and the payment-proof caveat. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (15 parameters, no output schema, no schema descriptions), the description is far too sparse. It does not explain what the returned checkout looks like, how parameters map to the case, or any constraints beyond the payment precondition. An agent would struggle to correctly populate fields without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate by explaining parameters. It does not. With 15 parameters, none are described in the description beyond the implicit requirement that user_confirmed_payment must be true. The description gives no meaning to company_name, contact_email, capital_purpose, or any optional fields. This is a significant gap.

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 states a specific verb ('create') and resource ('bounded capital-research case and secure Polar checkout'), and clarifies the critical precondition ('After the human explicitly agrees to pay $49'). It clearly differentiates from sibling read-only tools (get_capital_case_status, get_capital_fit_offer) by being a write operation. The caveat 'Checkout creation is not payment proof' adds useful semantic distinction.

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 gives a clear usage context: only after explicit human agreement to pay. It implies this is the tool to create a checkout, contrasting with the sibling status/offer tools. It does not explicitly name alternatives or say when not to use it, but the precondition and action are sufficient for an agent to route correctly.

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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