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Central Command — x402 Trading Intelligence

by io.github.tlefko

Aggregated Open Interest

cc.open_interest
Read-onlyIdempotent

Call cc.open_interest — Historical and current aggregated open interest across all major exchanges with 30-min cache. Purpose: Historical and current aggregated open interest across all major exchanges with 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: $0.001 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: 60/min (per API key). Tier: standard. Returns: Time-series OI data plus current OI breakdown per exchange for requested symbol. 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: open-interest, derivatives, positioning, leverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesSymbol to query e.g. BTC Required.
intervalNoTime interval: 1h, 4h, 1d Optional.
__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

A4.4/5.0
Behavior5/5

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

The description goes well beyond annotations by disclosing read-only behavior, response caching (~1800s), authentication requirements (X-Api-Key or x402 payment), costs, rate limits, and HTTP 402 behavior for unauthenticated calls. It also clarifies that billing is not a side effect. These details give the agent essential operational context without contradicting the 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 (Behavior, Auth, Cost, Returns, Guidelines), but it repeats the opening sentence verbatim in the 'Purpose:' section and includes a tags list that adds marginal value. It is more verbose than necessary, though each section is scannable.

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

Completeness4/5

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

The description covers purpose, behavior, auth, cost, rate limits, return summary, and usage workflow, making it highly complete for a tool that also has annotations and an output schema. It could mention potential error cases or asset classes, but the provided information is already substantial.

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?

The input schema already provides descriptions for all three parameters with 100% coverage, so the description adds little semantic detail beyond the schema. It mentions the __x_payment parameter in context of authentication but does not offer additional constraints or usage syntax.

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 historical and current aggregated open interest across major exchanges, with a 30-min cache. It specifies the return content (time-series data plus per-exchange breakdown) and distinguishes itself from sibling tools focused on other data types like funding rates or liquidations.

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 includes explicit usage guidelines: use for research/signal context, pair with cc.agent_strategy for paper trading before live orders, and warns against inventing fills from this data. This provides both when-to-use and when-not-to-use guidance, and names a complementary sibling tool.

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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