AI-analyzed trending tokens with X/Twitter engagement data and market narratives. $0.03 USDC. Twitter included free. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses payment details ($0.03 USDC, consumed on execution including timeouts) and data sources (Twitter, AI-analyzed). This adds value beyond annotations (none provided) by informing the agent of cost and data nature.
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?
Two sentences, efficiently front-loading key information (purpose, cost, inclusions). No wasted words.
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?
Covers purpose, data contents, and cost behavior. Lacks output format or field details but is acceptable given no output schema and simple list nature. Could mention if results are limited or sorted.
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?
No parameters exist, schema coverage is 100%, so baseline is 4. The description adds meaning by describing the output content (trending tokens, engagement data, narratives), enhancing understanding beyond the empty 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 states 'AI-analyzed trending tokens with X/Twitter engagement data and market narratives,' indicating specific verb (analyzed/trending) and resource (tokens). It is clear but does not explicitly differentiate from sibling tools.
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?
No guidance on when to use this tool versus siblings like alpha_search or alpha_news. The description mentions cost but lacks context for selection.
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.
Each tool targets a distinct aspect of crypto alpha research (e.g., brief, calendar, compare, deep, macro, memecoin, narrative, news, onchain, perps_funding, portfolio, prediction, risk, search, sentiment, stats, subscribe, token, trending). Descriptions clearly differentiate purposes, minimizing ambiguity.
All tool names follow a uniform 'alpha_{descriptive_noun}' pattern with snake_case, making naming predictable and easy to navigate.
With 19 tools spanning a broad range of crypto intelligence (market data, sentiment, on-chain, risk, portfolio, news, etc.), the count is well-scoped for the server's purpose—neither too few nor excessive.
The tool set covers most key areas of crypto research (price, sentiment, on-chain, risk, news, calendar, narratives, portfolio, predictions, subscriptions). Minor gaps like a dedicated volume/anomaly tool are absent, but the set is largely comprehensive.