get_whale_labelLook up an Ethereum address against our curated label DB (CEX hot wallets, known market makers, suspected funds). Lets agents distinguish mechanical MM flow from alpha-generating activity.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Ethereum address to look up (0x-prefixed, 40 hex chars). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds context about the label DB contents (CEX hot wallets, etc.), which is beyond the annotations. However, it does not disclose other behavioral traits like rate limits or data freshness.
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 short sentences, each serving a clear purpose: first defines the action, second explains the utility. No redundant words or information.
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 lookup tool with one parameter, the description is reasonably complete. It explains the tool's purpose and the type of output (labels). However, it could benefit from mentioning how to interpret the label or whether there are multiple label categories, but it's mostly sufficient.
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 for the single parameter 'address', with a complete description in the schema. The description does not add extra semantics beyond what the schema provides, so a baseline of 3 is appropriate.
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 ('Look up') and clearly states the resource ('curated label DB') and the types of labels ('CEX hot wallets, known market makers, suspected funds'). It also explains the purpose (distinguishing mechanical MM flow from alpha-generating activity), which distinguishes it from sibling tools that track flows or trades.
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 when to use the tool (when you need to classify an address), but it does not explicitly state when not to use it or provide alternatives. Siblings like get_whale_flow or get_whale_trades serve different purposes, but the description doesn't guide 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.
Many tools are specialized, but several pairs have fuzzy boundaries: e.g., get_funding_rates vs get_top_funding_rates, get_basic_macro vs get_macro_context, get_simple_iv vs get_options_iv. An agent could easily select the wrong one.
Most tools follow a 'get_X' pattern with descriptive noun phrases. There are a few exceptions like 'create_api_key' and 'search_markets', but overall the convention is consistent and readable.
With 47 tools, the server is overloaded. While the domain is broad, this many tools makes discovery and selection difficult for an agent, reducing coherence.
The tool set covers an impressively wide range: macro data, funding, prediction markets, OI history, whale tracking, risk analytics, position sizing, backtesting, and signal generation. It leaves no obvious gaps for a crypto trading assistant.