Fetches the top 15 trending HuggingFace models sorted by likes in the last 7 days. Each item includes id (author/name), likes, downloads, pipeline tag, and url. Source: huggingface.co/api/models. Cache TTL 10min. Use when the agent needs to surface what the open-source AI community is paying attention to right now.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds valuable behavioral context beyond annotations: the data source (huggingface.co/api/models), cache TTL (10min), and the exact item fields returned. This goes beyond a minimal safety declaration.
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: the first states the core function, the second provides item details and technical context (source, cache), and the third gives a use case. Every sentence adds value, 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, read-only tool with no parameters and no output schema, the description covers all essential aspects: what is fetched, the sort/filter criteria, the output fields, the data source, caching behavior, and a usage scenario. This is comprehensive for the tool's complexity.
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 schema coverage is trivially 100%. Per the rubric, 0 parameters warrants a baseline of 4. The description does not need to explain parameters, and the absence of parameters is clear from the schema.
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 uses a specific verb ('fetches') and clearly identifies the resource ('top 15 trending HuggingFace models') with additional qualifiers ('sorted by likes in the last 7 days'). It distinguishes itself from sibling tools by focusing on HuggingFace community trends, making it unambiguous.
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 explicitly states when to use the tool: 'Use when the agent needs to surface what the open-source AI community is paying attention to right now.' Although it doesn't mention alternatives or exclusions, the sibling tools cover clearly different domains (e.g., BTC price, climate), so the context is sufficient.
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.
