Wallet portfolio analysis — token holdings, diversification score, risk exposure, AI rebalancing suggestions. $0.05 USDC. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | Chain for lookup (auto-detect from address format if omitted) | |
| wallet | Yes | Wallet address (EVM 0x... or Solana base58) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It details the cost policy including consumption on timeouts, which is valuable. However, it does not mention other behavioral aspects such as rate limits, authentication requirements, or data freshness, leaving gaps.
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 two concise sentences: the first lists core features, and the second states the cost and execution policy. No extraneous information, and critical details are 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?
Given the lack of an output schema, the description adequately lists the main return components (holdings, diversification, risk, rebalancing). However, it omits details on response format, error handling, or processing time, which would be helpful for a paid 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?
The input schema has 100% coverage, fully documenting both parameters (wallet, chain). The description does not add any additional parameter-level meaning beyond what is already in the schema, resulting in a baseline score.
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 'Wallet portfolio analysis' and lists specific outputs (token holdings, diversification score, risk exposure, AI rebalancing suggestions). It effectively distinguishes the tool from sibling tools like alpha_brief, alpha_compare, and alpha_token, which cover different aspects of crypto analysis.
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?
While the description implies use for wallet portfolio analysis, it does not explicitly state when to use this tool over alternatives or when not to use it. The cost detail ($0.05 USDC) provides a practical constraint, but no direct comparison with sibling tools is given.
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.