Central Command

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

A4.1/5.0

Behavior5/5

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

The description goes beyond annotations by detailing caching (30-min cache), authentication (X-Api-Key or x402 payment), billing ($0.001 USDC per call), rate limit (60/min), and that it is read-only with no side effects. This fully discloses behavioral traits.

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

Conciseness4/5

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

The description is well-structured with a clear front-loaded first sentence and logical sections (purpose, behavior, auth, cost, rate limit, returns, guidelines). However, it redundantly states the purpose line twice, slightly reducing conciseness.

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 output (MA periods and timeframes), caching, billing, auth, rate limits, and usage guidelines. Given the presence of an output schema and rich annotations, it is largely complete. It lacks error handling details but is sufficient for most use cases.

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%, so the input parameters are already fully documented. The description adds marginal value by explaining the optional payment parameter's use case, but does not enrich the meaning of the required 'symbol' parameter beyond the schema.

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 it fetches multi-period moving average values (SMA/EMA) via Coinglass. The verb 'fetch' and resource 'moving average data' are specific. However, it does not distinguish from sibling tools like cc.central_signal or cc.indicator_generator, which may also provide technical indicators.

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?

The description explicitly states 'Use for research / signal context' and advises pairing with cc.agent_strategy (paper) before live orders. It also warns not to invent fills from this data. This provides clear usage context, though no alternative tools are mentioned.

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

A4.1/5.0

Disambiguation2/5

Many tools serve overlapping purposes, such as multiple Coinglass data tools (cc.coinglass_data, cc.funding_rates, cc.open_interest, etc.) and multiple AI chat assistants (cc.squirrel_chat, cc.squirrel_chat_v2, cc.openclaw_chat). The distinctions are subtle, likely causing agent misselection.

Naming Consistency5/5

All tool names follow a consistent `cc.<snake_case>` pattern, with verbs like `list_catalog`, `cc.ma_fetch`, and `cc.trade_builder`. No mixing of conventions.

Tool Count3/5

33 tools is on the high side but reasonable for a comprehensive crypto trading platform. However, significant redundancy (e.g., multiple data sources for similar indicators) suggests some could be consolidated, making the surface feel heavier than necessary.

Completeness4/5

The tool set covers most aspects of crypto trading: market data, technical indicators, signals, execution, backtesting, AI analysis, and blockchain RPC. Minor gaps exist (e.g., portfolio management), but the surface is largely complete for the intended domain.

Resources

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