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24mlight

A Share MCP

normalize_index_code

Convert common stock index codes to Baostock format for consistent data retrieval in A-share market analysis.

Instructions

Normalize common index codes to Baostock format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes

Implementation Reference

  • The MCP tool handler function for 'normalize_index_code'. It is decorated with @app.tool() for registration, logs the tool call, and delegates execution to the core logic via run_tool_with_handling.
    @app.tool()
    def normalize_index_code(code: str) -> str:
        """Normalize common index codes to Baostock format."""
        logger.info("Tool 'normalize_index_code' called with input=%s", code)
        return run_tool_with_handling(
            lambda: normalize_index_code_logic(code),
            context="normalize_index_code",
        )
  • Core helper function implementing the normalization logic, mapping common index names/codes to Baostock standard format (sh.000300 etc.). Includes input validation.
    def normalize_index_code_logic(code: str) -> str:
        validate_non_empty_str(code, "code")
        raw = code.strip().upper()
        if raw in {"000300", "CSI300", "HS300"}:
            return "sh.000300"
        if raw in {"000016", "SSE50", "SZ50"}:
            return "sh.000016"
        if raw in {"000905", "ZZ500", "CSI500"}:
            return "sh.000905"
        raise ValueError("Unsupported index code. Examples: 000300/CSI300/HS300, 000016, 000905.")
  • mcp_server.py:58-58 (registration)
    Registers the helper tools, including 'normalize_index_code', by calling the register_helpers_tools function.
    register_helpers_tools(app)
  • The registration function for helper tools. It defines and registers 'normalize_index_code' (and others) using @app.tool() decorators when called.
    def register_helpers_tools(app: FastMCP):
        """Register helper/utility tools with the MCP app."""
    
        @app.tool()
        def normalize_stock_code(code: str) -> str:
            """Normalize a stock code to Baostock format."""
            logger.info("Tool 'normalize_stock_code' called with input=%s", code)
            return run_tool_with_handling(
                lambda: normalize_stock_code_logic(code),
                context="normalize_stock_code",
            )
    
        @app.tool()
        def normalize_index_code(code: str) -> str:
            """Normalize common index codes to Baostock format."""
            logger.info("Tool 'normalize_index_code' called with input=%s", code)
            return run_tool_with_handling(
                lambda: normalize_index_code_logic(code),
                context="normalize_index_code",
            )
    
        @app.tool()
        def list_tool_constants(kind: Optional[str] = None) -> str:
            """
            List valid constants for tool parameters.
    
            Args:
                kind: Optional filter: 'frequency' | 'adjust_flag' | 'year_type' | 'index'. If None, show all.
            """
            logger.info("Tool 'list_tool_constants' called kind=%s", kind or "all")
            freq = [
                ("d", "daily"), ("w", "weekly"), ("m", "monthly"),
                ("5", "5 minutes"), ("15", "15 minutes"), ("30", "30 minutes"), ("60", "60 minutes"),
            ]
            adjust = [("1", "forward adjusted"), ("2", "backward adjusted"), ("3", "unadjusted")]
            year_type = [("report", "announcement year"), ("operate", "ex-dividend year")]
            index = [("hs300", "CSI 300"), ("sz50", "SSE 50"), ("zz500", "CSI 500")]
    
            sections = []
    
            def as_md(title: str, rows):
                if not rows:
                    return ""
                header = f"### {title}\n\n| value | meaning |\n|---|---|\n"
                lines = [f"| {v} | {m} |" for (v, m) in rows]
                return header + "\n".join(lines) + "\n"
    
            k = (kind or "").strip().lower()
            if k in ("", "frequency"):
                sections.append(as_md("frequency", freq))
            if k in ("", "adjust_flag"):
                sections.append(as_md("adjust_flag", adjust))
            if k in ("", "year_type"):
                sections.append(as_md("year_type", year_type))
            if k in ("", "index"):
                sections.append(as_md("index", index))
    
            out = "\n".join(s for s in sections if s)
            if not out:
                return "Error: Invalid kind. Use one of 'frequency', 'adjust_flag', 'year_type', 'index'."
            return out
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'normalize' but doesn't disclose behavioral traits like whether this is a read-only transformation, what errors might occur (e.g., invalid codes), or the output format. For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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

Conciseness5/5

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

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and target format, making it easy to parse quickly. This is appropriately concise for a simple transformation tool.

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 low parameter coverage, the description is incomplete. It doesn't explain what 'Baostock format' means, provide examples, or clarify the transformation's scope. For a normalization tool in a financial data context, more detail is needed to ensure correct usage.

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

Parameters2/5

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

The input schema has 1 parameter with 0% description coverage, and the tool description doesn't add any parameter semantics. It doesn't explain what 'code' should contain (e.g., examples of 'common index codes'), expected formats, or constraints. With low schema coverage, the description fails to compensate, leaving the parameter meaning unclear.

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 'normalize common index codes to Baostock format', which provides a clear verb ('normalize') and resource ('common index codes'), but it's vague about what 'common index codes' specifically refers to and doesn't distinguish from its sibling tool 'normalize_stock_code'. The purpose is understandable but lacks specificity about the domain or input scope.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for 'common index codes', or differentiate from 'normalize_stock_code' (a clear sibling tool for stock codes). Without any usage context, the agent must infer when this tool is appropriate.

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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