Fetches the current Bitcoin price in USD with 24h change, high, low, and volume. Source: Binance with CoinCap fallback. Cache TTL 15s. No auth required. Use for crypto trading decisions or when the agent needs a fresh BTC quote.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description goes beyond by disclosing the source (Binance with CoinCap fallback), cache TTL (15s), and that no auth is required. These are useful behavioral traits that help the agent understand freshness and reliability, exceeding the baseline set by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long, each earning its place. The first sentence states the core purpose, the second adds source and caching details, and the third provides use cases. There is no wasted text, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless tool, the description is complete. It explains what data is returned (price, 24h change, high, low, volume), where it comes from, how fresh it is, and when to use it. Without an output schema, the description fills that gap well by listing the expected fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the input schema is an empty object. The description still adds value by listing the output fields (24h change, high, low, volume), which serves as implicit return-value documentation. Since there are no parameters to describe, a baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb 'Fetches' and identifies the resource as 'current Bitcoin price in USD' with the exact data points (24h change, high, low, volume). It also names the data source and fallback, clearly distinguishing this tool from the broader crypto-related siblings like tf_crypto_movers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: 'Use for crypto trading decisions or when the agent needs a fresh BTC quote.' It does not mention when not to use or name alternatives, but the context is clear and the tool is highly specialized, so a minor deduction is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
