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by co.supabase.rtcelwjnrbmfmrywacky

OpenClaw Strategy Agent

cc.openclaw_chat
Destructive

Call cc.openclaw_chat — Autonomous AI agent specialized in strategy development, backtesting, and continuous market monitoring. Uses indicator libraries, pattern recognition, and instrument specifications. Purpose: Autonomous AI agent specialized in strategy development, backtesting, and continuous market monitoring. Uses indicator libraries, pattern recognition, and instrument specifications. Behavior: conversational AI that CAN place/cancel orders and manage positions when the linked account allows it. Treat as potentially destructive. Confirm intent before asking it to trade live. Auth: X-Api-Key required (and linked exchange credentials for execution actions). Cost: $0.025 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: enterprise. Returns: Structured AI analysis with computed indicators, detected patterns, strategy recommendations, and task management for autonomous execution. Guidelines: Prefer paper/simulation paths. For live money require explicit human confirmation (confirm_live / action=execute). Report real HTTP errors; never invent proxy failures. Tags: ai, strategy, autonomous, backtesting, patterns, indicators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesParameter `message` (string). Required.
__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_idNoParameter `conversation_id` (string). 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

A4.5/5.0
Behavior5/5

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

Discloses that the tool can place/cancel orders and manage positions, and explicitly states to treat it as potentially destructive. This aligns with annotations (destructiveHint=true) and adds context about needing confirmation for live trading. Also covers cost and rate limits.

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 it is verbose and contains redundancy (e.g., 'Autonomous AI agent specialized in...' appears twice). Some sentences could be combined or trimmed without losing clarity.

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?

Covers all critical aspects: behavior, authentication, cost, rate limits, tier, return type, and usage guidelines. Given the presence of an output schema and annotations, the description provides sufficient contextual completeness for an autonomous AI agent tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all parameters. The description adds value by explaining the purpose of __x_payment (retry after HTTP 402) which is not in the schema. It also clarifies that conversation_id is optional and that unknown extras are ignored.

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 it is an autonomous AI agent for strategy development, backtesting, and market monitoring. It distinguishes itself from sibling tools by explicitly mentioning its ability to place/cancel orders and manage positions, which is unique among the listed siblings.

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 explicit guidelines: prefer paper/simulation, require human confirmation for live trading, and correctly report errors. It also explains authentication and cost context. However, it does not explicitly state when not to use it or name alternative tools.

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