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BACH-AI-Tools

Indian Stock Exchange API2 MCP Server

historical_stats

Retrieve historical stock statistics like quarterly results for Indian companies listed on NSE and BSE to analyze performance trends.

Instructions

  • Endpoint: /historical_stats - Method: GET - Query Parameters: - stock_name (required): string - stats (required): string - Description: Retrieve historical statistics for a specific stock. - Example Request: http GET /historical_stats?stock_name=TATAMOTORS\u0026stats=quarter_results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_nameYesExample value: TCS
statsNoExample value:

Implementation Reference

  • server.py:56-61 (registration)
    The tool "historical_stats" is automatically registered by FastMCP.from_openapi based on 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__
    )
Behavior2/5

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

Discloses HTTP method (GET) which implies read-only behavior, but lacks critical behavioral context given no annotations exist. Does not specify what statistics types are available, response format, encoding, or pagination behavior. Full burden of transparency falls on description due to missing annotations.

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?

Uses structured markdown with endpoint details and code block example, which is scannable but front-loads HTTP implementation details over semantic meaning. The backslash-escaped ampersand in example URL is noisy. Could be more concise while retaining the useful example request.

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?

Incomplete for a data retrieval tool with no output schema. Missing: valid values for 'stats' parameter, distinction from 'historical_data', response structure documentation, and error conditions. The parameter contradiction further reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Major contradiction: description marks 'stats' as required, but input schema only lists 'stock_name' in required array. While description provides helpful example 'quarter_results', it fails to explain valid enum values for 'stats' parameter or semantic meaning beyond 'statistics'. Schema descriptions are minimal ('Example value: TCS'), requiring description to compensate more than it does.

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?

States clear verb-resource combination ('Retrieve historical statistics for a specific stock'), but fails to differentiate from sibling tool 'historical_data'. The distinction between 'statistics' and 'data' is implied but not explained, leaving ambiguity when to choose this over the sibling.

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 an example request showing 'quarter_results' as a stats value, but offers no explicit guidance on when to use this tool versus 'historical_data' or other data retrieval siblings. No prerequisites, rate limits, or filtering guidance mentioned.

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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