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Central Command — x402 Trading Intelligence

by io.github.tlefko

Liquidation Heatmap

cc.liquidation_heatmap
Read-onlyIdempotent

Call cc.liquidation_heatmap — Liquidation cluster visualization data showing where leveraged positions would be force-closed at each price level. Purpose: Liquidation cluster visualization data showing where leveraged positions would be force-closed at each price level. 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: $0.005 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: 20/min (per API key). Tier: premium. Returns: 3D matrix: price levels x leverage tiers x liquidation volume. Identifies magnetic price targets. 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: liquidations, heatmap, leverage, magnetic-levels, risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rangeNoPrice range: 5%, 10%, 20% Optional.
symbolYesParameter `symbol` (string). Required.
__x_paymentNoOptional 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
NameRequiredDescriptionDefault
okYesTrue when the gateway HTTP status is 2xx.
dataNoParsed JSON body from the endpoint (shape varies by slug).
errorNoError message when ok is false.
statusYesUpstream HTTP status from x402-gateway.
billingNoOptional payment / cost metadata when present.
endpointYesCatalog 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?

Beyond annotations (readOnlyHint, idempotentHint), the description adds rich behavior: 'READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Responses may be cached (~1800s).' It also discloses auth methods, cost, rate limit, and return format, providing a complete behavioral picture with no contradiction.

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

Conciseness5/5

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

Despite length, the description is well-structured with labeled sections (Purpose, Behavior, Auth, Cost, Rate limit, Returns, Guidelines, Tags). The first sentence is front-loaded with purpose, and each section earns its place by conveying essential operational details without fluff.

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?

This tool has complex context—payment proofs, caching, rate limits, output schema—and the description covers all of it: auth requirements, cost, caching, rate limit, return structure, and safe usage guidance. The output schema exists, so extra return detail is bonus, making the description complete for safe and correct invocation.

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

Parameters3/5

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

Schema coverage is 100% with each parameter already described (symbol, range, __x_payment). The description does not add further parameter-specific semantics beyond saying the return is a 3D matrix, which relates to output. Per rubric baseline, 3 is appropriate when the schema does the heavy lifting.

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 opens with a specific verb+resource: 'Call cc.liquidation_heatmap — Liquidation cluster visualization data showing where leveraged positions would be force-closed at each price level.' This clearly states what the tool does and distinguishes it from siblings like cc.liquidation_max_pain by focusing on cluster visualization and magnetic price targets.

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 description provides explicit context: 'Use for research / signal context. Pair with cc.agent_strategy (paper) before any live order. Do not invent fills from this data alone.' This tells when to use it, how to combine it with a sibling tool, and warns against misusing it for fill data. It also explains auth and cost prerequisites.

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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