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BACH-AI-Tools

Indian Stock Exchange API2 MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

commodity_futures_data_api

Access real-time and historical commodity futures data from Indian exchanges to analyze market trends and support trading decisions.

Instructions

$239

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:56-61 (registration)
    The "commodity_futures_data_api" tool is registered automatically from the OpenAPI specification by the FastMCP.from_openapi method. The definition for this tool resides within the OPENAPI_SPEC string constant at line 26.
    mcp = FastMCP.from_openapi(
    openapi_spec=openapi_dict,
    client=client,
    name="indian_stock_exchange_api2",
    version=__version__
)

Tool Definition Quality

D1.5/5.0
Behavior1/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. The string '$239' reveals nothing about read/write status, side effects, authentication requirements, or rate limits.

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

Conciseness1/5

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

While brief, the token '$239' contains zero actionable information and fails to earn its place. It is not appropriately sized or structured as a functional description.

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

Completeness1/5

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

For a financial data tool with numerous sibling market data endpoints, the description fails to explain what commodity futures data is returned, what timeframes are covered, or how to interpret the output.

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 (properties is an empty object), establishing a baseline score of 4 per evaluation guidelines.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description '$239' is completely opaque and fails to state what the tool does. It is neither a specific verb+resource combination nor does it distinguish from sibling market data tools like 'historical_data' or 'stock_forecasts'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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 the 13 sibling market data tools available. There are no 'when-to-use' or 'when-not-to-use' indicators.

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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