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Analog-Labs

Firestarter MCP Server

by Analog-Labs

firestarter_fund_wallet

Destructive

Fund your drop wallet via Stripe Checkout to self-fund drops and make them live immediately. Deposit at least $1.00 to cover the discount pot and bypass seller approval.

Instructions

Deposit money into your drop wallet via Stripe Checkout — the prepaid balance that self-funds drops you create (firestarter_create_drop). Once a drop's whole pot (max_claims x discount) is covered by your wallet balance, it goes LIVE IMMEDIATELY with no seller approval needed, because you're paying for the discount yourself rather than asking the seller to eat it. $1.00 minimum deposit. Returns a Stripe Checkout link to open in a browser; the wallet credits once payment completes (poll with firestarter_wallet_balance). Use when an owner asks to fund/top up/add money to their drop wallet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amount_centsYesAmount to deposit, in cents. Minimum 100 ($1.00).
Behavior5/5

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

The description goes well beyond the annotations by explaining the minimum deposit ($1.00), the return of a Stripe Checkout link, and the asynchronous crediting behavior. It also clarifies the crucial 'goes LIVE IMMEDIATELY' consequence of covering a drop's pot, which is essential operational behavior not captured by the schema.

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?

The description is concise yet information-dense. It front-loads the core action, then explains the funding model, the return value, and the usage trigger in a logical order. Every sentence earns its place without 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?

For a single-parameter tool with no output schema, the description fully compensates by explaining the return value (Stripe Checkout link), asynchronous behavior, and the relationship to drop creation. It is complete for an agent to understand both the action and its downstream effects.

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?

The schema already covers the single parameter amount_cents with a clear description and minimum. The tool description reiterates the $1.00 minimum but adds no new semantic detail beyond what the schema provides. Baseline of 3 applies due to high schema coverage.

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 the core function: 'Deposit money into your drop wallet via Stripe Checkout.' It specifies the resource (drop wallet), the method (Stripe Checkout), and the purpose (self-funding drops). It also implicitly distinguishes from related tools like withdraw_wallet and wallet_balance.

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?

The description explicitly states when to use the tool: 'Use when an owner asks to fund/top up/add money to their drop wallet.' It also provides clear context about the funding mechanism and directs users to poll with firestarter_wallet_balance after payment, effectively steering them to an alternative for checking results.

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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