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darved2305

groww-mcp

by darved2305

get_quote

Retrieve real-time stock quotes including LTP, bid/ask prices, open/high/low values, and volume data for NSE or BSE symbols to support trading decisions.

Instructions

LTP, bid/ask, open, high, low, volume for a symbol (NSE/BSE)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock symbol, e.g. RELIANCE, TCS, INFY
exchangeNoExchange — NSE or BSENSE

Implementation Reference

  • The handler implementation for the 'get_quote' tool. It registers the tool and executes the logic to fetch quote data using growwClient.
    server.tool(
      "get_quote",
      "LTP, bid/ask, open, high, low, volume for a symbol (NSE/BSE)",
      {
        symbol: z.string().describe("Stock symbol, e.g. RELIANCE, TCS, INFY"),
        exchange: z.enum(["NSE", "BSE"]).default("NSE").describe("Exchange — NSE or BSE"),
      },
      async ({ symbol, exchange }) => {
        try {
          const sym = normalizeSymbol(symbol);
          const q = await growwClient.getQuote(sym, exchange);
          const emoji = pnlEmoji(q.change);
    
          const text = [
            `${q.symbol}.${q.exchange} ${emoji}`,
            `${q.name}`,
            `${"─".repeat(40)}`,
            `LTP: ${formatCurrencyExact(q.ltp)}  (${formatPercent(q.changePercent)}, ${pnlSign(q.change)})`,
            `Open: ${formatCurrencyExact(q.open)}  |  High: ${formatCurrencyExact(q.high)}  |  Low: ${formatCurrencyExact(q.low)}`,
            `Prev Close: ${formatCurrencyExact(q.close)}`,
            `Bid: ${formatCurrencyExact(q.bid)}  |  Ask: ${formatCurrencyExact(q.ask)}`,
            `Volume: ${formatVolume(q.volume)}  |  Market Cap: ${formatCurrency(q.marketCap)}`,
            ``,
            `As of ${nowIST()}`,
          ].join("\n");
          return mcpText(text);
        } catch (err) {
          return mcpError(normalizeError(err));
        }
      }
    );
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 of behavioral disclosure. It mentions the data fields returned but doesn't describe any behavioral traits such as rate limits, authentication requirements, data freshness (real-time vs delayed), error conditions, or whether this is a read-only operation. The description is purely functional without operational context.

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 extremely concise—a single sentence that packs all necessary functional information without any fluff. It's front-loaded with the core purpose and uses parentheses efficiently to clarify scope. Every word earns its place, making it easy to parse quickly.

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 the complexity (financial data retrieval with multiple fields), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., structure of LTP, units for volume), error handling, or any prerequisites. For a tool with no structured output documentation, the description should provide more context about the response.

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 description adds minimal value beyond the input schema, which has 100% coverage. It mentions 'symbol (NSE/BSE)' which loosely relates to parameters but doesn't explain semantics like format requirements for symbols or the implications of choosing NSE vs BSE. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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 what data the tool retrieves (LTP, bid/ask, open, high, low, volume) and for what resource (a symbol on NSE/BSE). It uses specific financial terminology and identifies the scope. However, it doesn't explicitly distinguish this from sibling tools like 'get_ohlc_data' or 'search_stocks', which might provide overlapping or related functionality.

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. With siblings like 'get_ohlc_data' (which might provide similar OHLC data) and 'search_stocks' (which might help find symbols), there's no indication of when this specific quote retrieval tool is preferred or what its limitations are compared to other 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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