Central Command — x402 Trading Intelligence

Orderflow Footprint Data

cc.coinglass_footprint

Read-onlyIdempotent

Call cc.coinglass_footprint — Fetches footprint/orderflow data showing buy vs sell volume imbalance at each price level. 5-minute cache. Purpose: Fetches footprint/orderflow data showing buy vs sell volume imbalance at each price level. 5-minute cache. Behavior: READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Responses may be cached (~300s). 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: 30/min (per API key). Tier: premium. Returns: Orderflow footprint matrix: buy/sell volume at discrete price levels revealing absorption, exhaustion, and imbalance. 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: orderflow, footprint, volume-profile, microstructure.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes	Trading pair e.g. BTC Required.
`__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

A4.1/5.0

Behavior5/5

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

The description goes far beyond the readOnlyHint, detailing cache behavior (~300s), authentication methods (X-Api-Key or x402), cost per call, rate limits, and the HTTP 402 response for anonymous calls. This is rich behavioral context not available in annotations.

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 well-structured with labeled sections, but the first sentence is almost verbatim repeated in the 'Purpose:' section ('Fetches footprint/orderflow data showing buy vs sell volume imbalance at each price level. 5-minute cache.'). This duplication wastes space and reduces conciseness.

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?

Despite having an output schema, the description proactively covers return values, auth, cost, rate limits, and usage guardrails. It is thorough for a premium, pay-per-use data tool and leaves little ambiguity about selection and 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 description coverage is 100%: both 'symbol' and '__x_payment' already have descriptive text. The description itself adds no parameter-specific details beyond what the schema provides, so the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states a specific verb ('Fetches'), resource ('footprint/orderflow data'), and scope ('buy vs sell volume imbalance at each price level'). While it doesn't explicitly name sibling alternatives, the specialized focus on orderflow footprint distinguishes it from broader tools like cc.coinglass_data.

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

Usage Guidelines4/5

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

Provides clear usage context: 'Use for research / signal context' and 'Pair with cc.agent_strategy (paper) before any live order.' It also warns against over-interpretation ('Do not invent fills from this data alone'). No explicit exclusions of alternatives, but the guidance is useful.

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

All Central Command — x402 Trading Intelligence tools Inspect

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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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