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BACH-AI-Tools

Indian Stock Exchange API2 MCP Server

price_shockers

Identify stocks with significant price movements in the Indian stock market to monitor volatility and spot potential trading opportunities.

Instructions

Price Shockers Endpoint: /price_shockers Method: GET Description: Get data for stocks that have experienced significant price changes in a short period of time. Example Request: http GET /price_shockers Example Response: ```json [ { \

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:56-61 (registration)
    The MCP server is initialized using FastMCP.from_openapi, which automatically generates tools from the OPENAPI_SPEC string. The 'price_shockers' tool is registered as part of this automatic generation process.
    mcp = FastMCP.from_openapi(
        openapi_spec=openapi_dict,
        client=client,
        name="indian_stock_exchange_api2",
        version=__version__
    )
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 but fails to deliver. It redundantly includes HTTP metadata (endpoint path, GET method) instead of describing actual behavior like data freshness, rate limits, what constitutes a 'shocker,' or the response structure. The example response is truncated and incomplete.

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

Conciseness2/5

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

The description is poorly structured, beginning with markdown headers (###) and including redundant HTTP implementation details (Endpoint, Method) that waste tokens. The actual semantic content is a single sentence. The inclusion of incomplete code examples that truncate mid-string further degrades structural quality.

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?

With no output schema provided, the description must compensate by describing what data is returned, but it only describes the input filter (price shockers). It fails to indicate what fields are returned (price change percentage, volume spike, timestamp, etc.), leaving the agent blind to the tool's actual utility and return format.

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?

The input schema contains zero parameters. According to calibration guidelines, this establishes a baseline score of 4. The description correctly avoids inventing parameter documentation where none exist.

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 retrieves data for stocks with 'significant price changes in a short period of time,' providing a specific verb (Get data), resource (stocks), and filter criteria (significant price changes). However, it does not explicitly differentiate from similar volatility-focused siblings like 'get_trending_stocks' or 'fetch_52_week_high_low_data.'

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 siblings. There is no mention of prerequisites, no explicit 'use this instead of X' directives, and no indication of what constitutes 'significant' price changes or what timeframes are covered.

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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