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al-one

MCP Server for stock and crypto

给出投资建议

trading_suggest

Provides AI-driven investment recommendations for stocks and cryptocurrencies based on data analysis, suggesting buy, sell, or hold actions with confidence scores and reasoning.

Instructions

基于AI对其他工具提供的数据分析结果给出具体投资建议

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes股票代码或加密币种
actionYes推荐操作: buy/sell/hold
scoreYes置信度,范围: 0-100
reasonYes推荐理由

Implementation Reference

  • The handler function decorated with @mcp.tool(), defining the tool 'trading_suggest'. It structures the input parameters into a JSON-like dictionary for investment suggestions.
    @mcp.tool(
        title="给出投资建议",
        description="基于AI对其他工具提供的数据分析结果给出具体投资建议",
    )
    def trading_suggest(
        symbol: str = Field(description="股票代码或加密币种"),
        action: str = Field(description="推荐操作: buy/sell/hold"),
        score: int = Field(description="置信度,范围: 0-100"),
        reason: str = Field(description="推荐理由"),
    ):
        return {
            "symbol": symbol,
            "action": action,
            "score": score,
            "reason": reason,
        }
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions AI-based analysis and investment suggestions, but doesn't disclose critical behavioral traits such as whether this is a read-only operation, if it requires specific permissions, latency, rate limits, or how the AI processes data. For a tool that outputs investment advice, this lack of transparency is a significant gap.

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 a single, efficient sentence in Chinese that directly states the tool's function. It's front-loaded with the core purpose and avoids unnecessary details. However, it could be slightly more structured by explicitly mentioning the input parameters or output, but overall it's concise and to the point.

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

Completeness2/5

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

Given no annotations, no output schema, and a tool that provides AI-based investment suggestions (a complex operation), the description is incomplete. It doesn't cover behavioral aspects, return values, error handling, or how it integrates with other tools. The agent lacks sufficient context to use this tool effectively beyond the basic input parameters.

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 has 100% description coverage, with clear parameter definitions (symbol, action, score, reason). The description doesn't add any meaning beyond the schema, such as explaining the relationship between parameters or providing examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema adequately documents the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool '基于AI对其他工具提供的数据分析结果给出具体投资建议' (provides specific investment suggestions based on AI analysis of data from other tools), which gives a general purpose but lacks specificity about what kind of investment suggestions (e.g., trading signals, portfolio advice) and doesn't clearly distinguish from sibling tools like 'binance_ai_report' or 'stock_indicators_*' that might also provide analytical insights. It's vague but not tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when AI analysis is available from other tools, but it doesn't specify when to use this tool versus alternatives like 'binance_ai_report' (which might provide AI-based reports) or 'stock_indicators_*' (which provide raw indicators). No explicit guidance on prerequisites, timing, or exclusions is provided, leaving the agent to infer context.

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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