AI-synthesized token intelligence with price, volume, holders, X/Twitter engagement data, and analysis. $0.03 USDC. Twitter included free. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Token symbol (e.g., SOL, WIF, PEPE) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. Discloses that payment is consumed on execution including timeouts, which is a key behavioral trait. However, it does not disclose return format, rate limits, or any authorization needs. The cost and timeout info add value but are not comprehensive.
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?
Description is a few sentences long, front-loaded with the main purpose and key details (cost, Twitter included). No wasted words. Could be slightly more structured, but it is concise and readable.
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?
Given a single parameter and no output schema, the description reasonably covers what the tool does, what data it returns (price, volume, holders, engagement, analysis), and cost behavior. It does not explain return schema, but since none is provided, this is acceptable. The description is fairly complete for a simple tool.
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?
Schema coverage is 100% with one required parameter 'symbol'. The schema description already explains 'Token symbol (e.g., SOL, WIF, PEPE)'. The description adds no additional meaning beyond the schema. Baseline 3 is appropriate since schema does the heavy lifting.
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?
Clearly states it provides token intelligence with specific data points (price, volume, holders, X/Twitter engagement, analysis). The verb is implicit but the resource and scope are well-defined. However, it does not explicitly distinguish from similar alpha_* tools in the sibling list, leaving the agent to infer differentiation.
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 explicit guidance on when or when not to use this tool. The mention of a $0.03 cost implies it is for paid detailed analysis versus free alternatives, but no alternatives are named or contrasted. Sibling tools exist (e.g., alpha_brief, alpha_deep) but description does not help the agent decide between them.
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.