One-call token brief bundling market data, X/Twitter sentiment, on-chain transfers, and risk research with a unified AI synthesis. $0.20 USDC. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | Chain filter (solana, base, ethereum). Default: auto-detect | |
| symbol | No | Token symbol (e.g., SOL, ETH) | |
| address | No | Token contract address (enables on-chain transfer analysis) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It only reveals that payment is consumed on execution, including timeouts. However, it does not mention whether the tool is read-only, idempotent, or any side effects. The response format is vaguely described as 'unified AI synthesis.' More detail on what gets modified or what the return structure is would improve transparency.
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 very concise: two sentences. The first sentence front-loads the main purpose, and the second adds critical cost information. Every word earns its place; no redundancy or fluff.
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?
The tool has no output schema, so the description should clarify what the output looks like. It says 'unified AI synthesis' but does not specify format (text, JSON, etc.). It does cover cost and timeout behavior, which is important. Given the complexity of a paid tool, more detail on the return value would make it complete.
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 description coverage is 100%, so the schema already documents each parameter (chain, symbol, address). The description adds no extra semantic meaning beyond what is in the schema, such as that address enables on-chain analysis, which is already stated. The bundling aspect is implicit but not tied to specific parameters.
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 clearly states the tool's purpose: it bundles market data, sentiment, on-chain transfers, and risk research into a unified AI synthesis. It also mentions the cost and payment consumption, making the scope unambiguous. This distinguishes it from sibling tools like alpha_news or alpha_onchain, which focus on individual aspects.
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 implies the tool is for obtaining a comprehensive token brief in one call, but it does not explicitly state when to use it over alternatives (e.g., alpha_sentiment for sentiment only). There is no guidance on when not to use it or what prerequisites are needed. The mention of cost and timeout consumption provides some context but not comparative guidance.
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.