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by co.supabase.rtcelwjnrbmfmrywacky

AI Trading Chat

cc.squirrel_chat
Read-onlyIdempotent

Call cc.squirrel_chat — Full AI trading assistant with real-time market data, position analysis, chart drawing commands, and multi-tool execution. Supports 60+ data actions including candles, funding rates, open interest, liquidations, order book, and more. Returns structured analysis with optional chart annotations. Purpose: Full AI trading assistant with real-time market data, position analysis, chart drawing commands, and multi-tool execution. Supports 60+ data actions including candles, funding rates, open interest, liquidations, order book, and more. Returns structured analysis with optional chart annotations. Behavior: READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Live / near-real-time data. Auth: X-Api-Key or x402 payment proof (X-PAYMENT / __x_payment). Anonymous unauthenticated calls receive HTTP 402 with payment accepts. Cost: $0.002 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: 10/min (per API key). Tier: premium. Returns: AI response with market analysis, trade suggestions, and optional draw_commands for chart annotations. Token usage included for billing. Guidelines: Pass required parameters exactly; omit unknown fields. On 402, settle payment then retry with X-PAYMENT. Tags: chat, ai, analysis, trading, market-data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNoActive chart symbol (e.g. BTC-USDT) Optional.
messageYesUser message / question / instruction Required.
positionsNoCurrent open positions for context Optional.
timeframeNoActive chart timeframe (e.g. 4H) 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.
conversation_idNoExisting conversation UUID for context continuity 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds significant context: authentication methods (X-Api-Key or x402), cost ($0.002 USDC per call), rate limit (10/min), return type (AI response with optional draw_commands), and billing details. All are consistent with annotations and provide essential behavioral information beyond annotations.

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 labeled sections (Behavior, Auth, Cost, Returns, Guidelines, Tags) and front-loaded purpose. However, it is somewhat repetitive (e.g., 'Full AI trading assistant...' appears twice). Still, every sentence adds value, so it's efficient but not maximally concise.

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?

Given the tool's complexity (6 params, output schema exists, many siblings), the description covers all essential aspects: purpose, capabilities, behavioral constraints, authentication, pricing, rate limits, return format, and usage tips. It leaves no critical gaps for an agent to make an informed decision.

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% for all 6 parameters. The description adds marginal value: explains the __x_payment parameter's special use for retries. Baseline 3 is appropriate; the description does not significantly enhance parameter understanding beyond the schema.

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 verb 'Call' and the resource 'full AI trading assistant'. It enumerates capabilities (real-time data, position analysis, chart drawing) and distinguishes from specific data tools like coinglass_data or funding_rates by emphasizing its comprehensive chat interface. It effectively conveys the tool's scope and uniqueness.

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 includes a dedicated 'Guidelines' section advising to pass required parameters exactly and handle 402 errors with X-PAYMENT. It also states read-only behavior. However, it does not explicitly mention when not to use this tool or provide alternatives (e.g., squirrel_chat_v2). This is a minor gap.

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.

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