Central Command — x402 Trading Intelligence

Moving Average Data

cc.ma_fetch

Read-onlyIdempotent

Call cc.ma_fetch — Multi-period moving average values (SMA/EMA at various lengths) via Coinglass with 30-min cache. Purpose: Multi-period moving average values (SMA/EMA at various lengths) via Coinglass 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: MA values at 7, 25, 50, 99, 200 periods across multiple timeframes for trend analysis. 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: moving-averages, trend, sma, ema, technical.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	Yes	Parameter `symbol` (string). 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

A4.5/5.0

Behavior5/5

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

The description discloses read-only behavior, caching (~1800s), auth requirements (X-Api-Key or x402 proof), cost per call, rate limit (60/min), and that payment is billing not a side effect. This goes well beyond the readOnlyHint annotation by adding operational context.

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 clear sections, but it duplicates the opening sentence in the 'Purpose' line ('Multi-period moving average values...' appears twice). This repetition and the inclusion of tags/tier make it slightly verbose, though still readable.

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?

The description covers all key operational aspects: behavior, auth, cost, rate limit, return summary, and usage guidelines. With an output schema present, it doesn't need to explain return values in detail, making it fully adequate for an agent to select and invoke the tool.

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 covers all parameters (100% coverage), but descriptions are minimal. The tool description adds general context about the data returned but does not specify symbol format or examples, so it provides limited added value beyond the schema 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 'Multi-period moving average values (SMA/EMA at various lengths) via Coinglass' and specifies the exact periods returned (7, 25, 50, 99, 200), making the tool's function unambiguous and distinguishing it from sibling data tools like funding rates or open interest.

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 guidance: 'Use for research / signal context. Pair with cc.agent_strategy (paper) before any live order.' It also cautions against 'inventing fills' from this data alone, clearly defining when to use and when not to rely on it.

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

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?