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MCP Server for stock and crypto

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Python

获取加密货币分析报告

binance_ai_report

Retrieves AI analysis reports from Binance for cryptocurrencies. Enter a symbol like BTC or ETH to get the report.

Instructions

获取币安对加密货币的AI分析报告，此工具对分析加密货币非常有用，推荐使用

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo加密货币币种，格式: BTC 或 ETHBTC

Implementation Reference

  • mcp_aktools/__init__.py:469-506 (handler)
    The main handler function implementing the binance_ai_report tool. It calls Binance's AI report API and extracts translated module overviews and point contents.
    def binance_ai_report(
    symbol: str = Field("BTC", description="加密货币币种，格式: BTC 或 ETH"),
):
    res = requests.post(
        f"{BINANCE_BASE_URL}/bapi/bigdata/v3/friendly/bigdata/search/ai-report/report",
        json={
            'lang': 'zh-CN',
            'token': symbol,
            'symbol': f'{symbol}USDT',
            'product': 'web-spot',
            'timestamp': int(time.time() * 1000),
            'translateToken': None,
        },
        headers={
            'User-Agent': USER_AGENT,
            'Referer': f'https://www.binance.com/zh-CN/trade/{symbol}_USDT?type=spot',
            'lang': 'zh-CN',
        },
        timeout=20,
    )
    try:
        resp = res.json() or {}
    except Exception:
        try:
            resp = json.loads(res.text.strip()) or {}
        except Exception:
            return res.text
    data = resp.get('data') or {}
    report = data.get('report') or {}
    translated = report.get('translated') or report.get('original') or {}
    modules = translated.get('modules') or []
    txts = []
    for module in modules:
        if tit := module.get('overview'):
            txts.append(tit)
        for point in module.get('points', []):
            txts.append(point.get('content', ''))
    return '\n'.join(txts)
  • mcp_aktools/__init__.py:465-468 (registration)
    The tool is registered with FastMCP using the @mcp.tool decorator, with title and description in Chinese.
    @mcp.tool(
    title="获取加密货币分析报告",
    description="获取币安对加密货币的AI分析报告，此工具对分析加密货币非常有用，推荐使用",
)
  • mcp_aktools/__init__.py:469-471 (schema)
    Input schema: accepts a single 'symbol' parameter (default 'BTC') with Pydantic Field describing it as a cryptocurrency symbol in format like BTC or ETH.
    def binance_ai_report(
    symbol: str = Field("BTC", description="加密货币币种，格式: BTC 或 ETH"),
):

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only says '获取报告' (get report) without mentioning any side effects, output format, data source specifics, or limitations. This is minimal for a tool that returns an analysis report.

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 short, but the second sentence ('此工具对分析加密货币非常有用，推荐使用') is subjective filler that doesn't add functional information. The first sentence earns its place, but the second is unnecessary.

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?

The tool has no output schema and no annotations, yet the description does not explain what the report contains or how the agent should use it. The single parameter is documented, but overall the description is insufficient to fully inform an agent about expected return values or context.

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 schema description for 'symbol' is clear ('加密货币币种，格式: BTC 或 ETH'), providing 100% coverage. The tool description itself adds no additional parameter meaning, so the baseline 3 is appropriate.

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 the tool fetches Binance's AI analysis report on cryptocurrencies, using the specific verb '获取' (get/fetch) and identifying the resource and scope. It is distinct from sibling crypto tools like crypto_prices or crypto_sentiment_metrics, though it doesn't explicitly compare to them.

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 only usage guidance is '此工具对分析加密货币非常有用，推荐使用' (this tool is very useful for analyzing cryptocurrencies, recommended to use), which is a vague recommendation rather than specific when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions.

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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