Central Command — x402 Trading Intelligence

Hyperliquid Whale Intelligence

cc.hyperliquid_data

Read-onlyIdempotent

Call cc.hyperliquid_data — Whale positions and activity alerts from Hyperliquid DEX showing large trader positioning. 30-min cache. Purpose: Whale positions and activity alerts from Hyperliquid DEX showing large trader positioning. 30-min cache. Behavior: READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Responses may be cached (~1800s). Auth: X-Api-Key or x402 payment proof (X-PAYMENT / __x_payment). Anonymous unauthenticated calls receive HTTP 402 with payment accepts. Cost: $0.003 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 30/min (per API key). Tier: premium. Returns: Large positions (size, entry, PnL, leverage) plus recent whale open/close alerts. Guidelines: Use for research / signal context. Pair with cc.agent_strategy (paper) before any live order. Do not invent fills from this data alone. Tags: whales, hyperliquid, dex, smart-money, positioning.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`__x_payment`	No	Optional x402 payment proof (same value as X-PAYMENT header). Use when retrying after HTTP 402 if your MCP client cannot set custom headers. Not a business parameter. Optional.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	True when the gateway HTTP status is 2xx.
`data`	No	Parsed JSON body from the endpoint (shape varies by slug).
`error`	No	Error message when ok is false.
`status`	Yes	Upstream HTTP status from x402-gateway.
`billing`	No	Optional payment / cost metadata when present.
`endpoint`	Yes	Catalog slug that was invoked (e.g. funding-rates).

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses READ-ONLY behavior, no order placement or fund movement, response caching (~1800s), authentication methods (X-Api-Key or x402), HTTP 402 behavior for anonymous calls, cost per call, rate limits, and explicitly states billing is not a side effect. This goes far beyond the annotations, which already indicate read-only and idempotent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is logically organized with labeled sections, but the opening sentence and the 'Purpose' section repeat the exact same content ('Whale positions and activity alerts... 30-min cache'), adding redundancy without new information. It's detailed and mostly earns its length, but the duplication is a clear flaw.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, behavior, auth, cost, rate limit, return values, and usage guidelines, making it a fully self-contained reference. The output schema further handles return details, so the description complements it well and equips an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage for the single optional __x_payment parameter, explaining it as an x402 payment proof for retrying after HTTP 402. The tool description adds context about anonymous calls receiving HTTP 402 and the auth flow, complementing the schema's explanation and giving a slight score boost over baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides 'Whale positions and activity alerts from Hyperliquid DEX showing large trader positioning,' which is specific to Hyperliquid whale data and distinguishes it from siblings like cc.coinglass_data. It also mentions the 30-min cache, adding further specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Guidelines' section explicitly advises use for research/signal context, names cc.agent_strategy as a companion for paper trading before live orders, and warns against inventing fills from this data alone. This provides clear when-to-use and complementary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0

Disambiguation2/5

Multiple tools have overlapping or unclear boundaries. The AI chat tools cc.squirrel_chat, cc.squirrel_chat_v2, and cc.openclaw_chat all provide conversational trading assistance with near-identical descriptions, while cc.central_signal and cc.external_signal both normalize signals for execution. Additionally, cc.asset_scanner, cc.auto_fetch_market_data, cc.data_tools, and cc.ma_fetch all supply technical indicator data with significant overlap.

Naming Consistency4/5

All tools share the 'cc.' prefix and use snake_case consistently, which creates a uniform feel. However, naming style mixes nouns (cc.asset_scanner, cc.data_tools) with verbs (cc.auto_fetch, cc.list_catalog) and compound forms (cc.strategy_backtest, cc.trade_builder), so it is not a strict verb_noun pattern. Minor deviations keep it from a 5.

Tool Count2/5

With 33 tools, the server is well beyond the typical well-scoped range of 3-15 and even above the 'heavy' 16-25 range. While the trading intelligence domain can be broad, this count feels overstuffed rather than curated, especially given the many overlapping data and AI tools.

Completeness3/5

The core trading workflow is covered: market data, technical analysis, signals, strategy backtesting, paper trading, and live execution all have tools. However, there is no dedicated account management tool (e.g., get_balance, list_positions) and no explicit delete_strategy, with these operations buried inside cc.agent_strategy's action parameter. Notable gaps remain for a complete lifecycle.

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