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BACH-AI-Tools

Indian Stock Exchange API2 MCP Server

bse_most_active

Retrieve current Bombay Stock Exchange stocks with highest trading volume to identify market activity and liquidity trends.

Instructions

BSE Most Active Endpoint: /BSE_most_active Method: GET Description: Get the latest most active stocks in the Bombay Stock Exchange (BSE) based on trading volume. Example Request: http GET /BSE_most_active Example Response: ```json [ { \

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:56-61 (registration)
    The tool "bse_most_active" (as defined by the operationId in the OpenAPI spec) is automatically registered by FastMCP from the OpenAPI specification provided in the `OPENAPI_SPEC` constant.
    mcp = FastMCP.from_openapi(
        openapi_spec=openapi_dict,
        client=client,
        name="indian_stock_exchange_api2",
        version=__version__
    )
Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. It clarifies that 'most active' means based on 'trading volume,' but fails to mention timeframe (what constitutes 'latest'), rate limits, required authentication, or the structure/format of returned stock data. The truncated example response compounds this lack of transparency.

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 front-loaded with the core purpose, but includes unnecessarily verbose API documentation formatting (markdown headers, '**Endpoint:**', HTTP method syntax) that adds noise for an AI agent. The severely truncated example response ending with escaped quotes ('\"') creates visual clutter and incomplete information.

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?

Without an output schema or annotations, the description should fully explain return values, but the example response is cut off mid-JSON. It omits what fields are returned (symbol, price, volume, percentage change), how many stocks are included in the results, and the critical distinction from `nse_most_active` needed for appropriate tool selection.

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, which per evaluation rules establishes a baseline score of 4. No parameter semantic information is required or expected in the description.

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?

Clearly states the tool retrieves 'the latest most active stocks in the Bombay Stock Exchange (BSE) based on trading volume,' providing specific verb, resource, and sorting criteria. However, it fails to explicitly distinguish from the sibling tool `nse_most_active` (National Stock Exchange), which is a critical distinction given both exist on the same server.

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?

Provides no guidance on when to use this tool versus the sibling `nse_most_active` tool, nor any prerequisites or conditions. The description focuses exclusively on technical endpoint documentation rather than selection criteria.

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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