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tf_payment_buy_credits

POST endpoint that returns the published USDC wallet address (0x549c82e6bfc54bdae9a2073744cbc2af5d1fc6d1 on Base mainnet), a unique memo, and a quote tying the dollar amount to credits at $1 USDC = 50 credits. Use as the first step when the agent needs to buy credits to access /api/pro/* endpoints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amount_usdNoUSDC amount to convert. Minimum $1 = 50 credits.

TDQS

A4/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=false, openWorldHint=true, and idempotentHint=false, so the agent knows it is a mutating, non-idempotent call. The description adds valuable context about the address and conversion rate, but it does not disclose what mutation actually occurs (e.g., whether it creates a payment order or just returns a quote), leaving ambiguity about side effects beyond returning data.

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 exactly two sentences, front-loaded with the core behavior, and every piece of information (endpoint, address, memo, quote, rate, use case) is purposeful. No wasted words or redundant filler.

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

Completeness4/5

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

Since there is no output schema, the description effectively explains what the tool returns (address, memo, quote) and gives operational details (rate, first step). It does not mention error cases or explicitly reference the follow-up tf_payment_confirm, but the sibling list and purpose are clear enough for a simple single-parameter tool.

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% because amount_usd has a description ('USDC amount to convert. Minimum $1 = 50 credits.'). The tool description repeats the conversion rate but does not add new parameter meaning beyond what the schema already provides, so it stays at the baseline.

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 verb and resource: 'POST endpoint that returns the published USDC wallet address... a unique memo, and a quote.' It distinguishes itself from payment siblings by emphasizing its role as the initial step for buying credits, not for checking balance, confirming, or history.

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?

It provides explicit usage context: 'Use as the first step when the agent needs to buy credits to access /api/pro/* endpoints.' However, it does not explicitly mention when not to use it or compare with alternatives like tf_payment_balance or tf_payment_confirm, so it lacks full exclusions.

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., tf_btc_price vs tf_fear_greed), but there is some overlap between free and premium aggregated tools (e.g., tf_briefing, tf_premium_briefing, tf_premium_agent_context). However, descriptions explicitly differentiate them by content and cost.

Naming Consistency5/5

All tools follow a consistent pattern: 'tf_' prefix (with 'tf_premium_' for premium ones) and snake_case. Names are descriptive and predictable, e.g., tf_btc_price, tf_earthquakes, tf_premium_macro.

Tool Count3/5

27 tools is on the high side, but the server covers a broad domain (crypto, finance, earthquakes, AI trends, payment system, etc.). Each tool serves a specific purpose, so the count is borderline acceptable but feels slightly heavy.

Completeness4/5

The tool surface covers a wide range of data feeds: crypto, forex, macro indicators, earthquakes, HN, HuggingFace, prediction markets, payment system, and service status. Minor gaps exist (e.g., no dedicated stock prices tool beyond premium macro, no weather), but overall it's comprehensive for a terminal feed.