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jjmerri

E*TRADE MCP Server

by jjmerri

etrade_get_quote

Retrieve real-time stock quotes and market data for specified symbols using E*TRADE's Market API, including price, volume, bid/ask spreads, and optional earnings dates.

Instructions

Get stock quote for one or more symbols.

Args: symbols: Comma-separated list of stock symbols (e.g., "AAPL", "MSFT,GOOGL") require_earnings_date: If true, return next earnings date skip_mini_options_check: If true, skip check for mini options

Returns: Quote data including price, volume, bid/ask, and other market information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolsYes
require_earnings_dateNo
skip_mini_options_checkNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • MCP tool handler for 'etrade_get_quote', decorated with @mcp.tool(). Retrieves authenticated market client and delegates to its get_quote method.
    @mcp.tool()
    def etrade_get_quote(symbols: str, require_earnings_date: bool = False,
                         skip_mini_options_check: bool = False) -> dict:
        """
        Get stock quote for one or more symbols.
        
        Args:
            symbols: Comma-separated list of stock symbols (e.g., "AAPL", "MSFT,GOOGL")
            require_earnings_date: If true, return next earnings date
            skip_mini_options_check: If true, skip check for mini options
            
        Returns:
            Quote data including price, volume, bid/ask, and other market information
        """
        client = get_market_client()
        return client.get_quote(symbols, require_earnings_date, skip_mini_options_check)
  • Core helper method in MarketClient that performs the actual HTTP API call to retrieve quote data from E*TRADE.
    def get_quote(self, symbols: str, require_earnings_date: bool = False, 
                  skip_mini_options_check: bool = False) -> Dict[str, Any]:
        """
        Get quote for one or more symbols
        
        Args:
            symbols: Comma-separated list of symbols (e.g., "AAPL,MSFT,GOOGL")
            require_earnings_date: If true, return next earnings date
            skip_mini_options_check: If true, skip check for mini options
            
        Returns:
            Quote response data
        """
        url = f"{self.base_url}/v1/market/quote/{symbols}.json"
        
        params = {}
        if require_earnings_date:
            params["requireEarningsDate"] = "true"
        if skip_mini_options_check:
            params["skipMiniOptionsCheck"] = "true"
        
        response = self.session.get(url, params=params)
        response.raise_for_status()
        
        return response.json()
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions what data is returned but doesn't cover critical aspects like rate limits, authentication requirements (implied by sibling tools but not stated), error handling, or whether this is a read-only operation. The return statement adds some value but is insufficient for a mutation-free tool with zero annotation coverage.

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 a clear purpose statement followed by Args and Returns sections. Every sentence earns its place by explaining parameters or outputs. It could be slightly more front-loaded by integrating the return info into the opening, but it's efficiently sized without waste.

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

Completeness3/5

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

Given 3 parameters with 0% schema coverage and an output schema (implied by 'Has output schema: true'), the description does a decent job. It explains all parameters and outlines return data, but lacks context on authentication (suggested by sibling tools), error cases, or usage limits. The output schema reduces the need for detailed return explanations, but behavioral gaps remain.

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 description coverage is 0%, so the description must compensate. It provides clear semantics for all three parameters: symbols (format and examples), require_earnings_date (effect on return data), and skip_mini_options_check (behavioral effect). This adds significant value beyond the bare schema, though it doesn't detail all possible return fields exhaustively.

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's purpose: 'Get stock quote for one or more symbols.' It specifies the verb ('Get') and resource ('stock quote'), and distinguishes it from siblings like option chains or product lookup. However, it doesn't explicitly differentiate from similar quote tools that might exist elsewhere, keeping it at 4 rather than 5.

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?

No guidance is provided on when to use this tool versus alternatives. While siblings include authentication and option-related tools, the description doesn't mention when to choose this over etrade_lookup_product or other potential quote tools. It only states what it does, not when it's 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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