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BACH-AI-Tools

Indian Stock Exchange API2 MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

get_trending_stocks

Identify trending stocks in the Indian market by analyzing current trading activity and investor interest to support investment decisions.

Instructions

$23d

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • server.py:56-61 (registration)
    The tool "get_trending_stocks" is dynamically registered via `FastMCP.from_openapi` using the `OPENAPI_SPEC` defined in the same file. It maps the `operationId` "get_trending_stocks" from the OpenAPI definition to a tool automatically generated by `FastMCP`.
    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 carries the full disclosure burden. '$23d' reveals nothing about data sources, rate limits, caching behavior, or what defines a 'trending' stock. It fails to disclose any behavioral traits.

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 '$23d' is short, it is not 'concise' in the positive sense—it is meaningless placeholder text that wastes the agent's context window. It fails the 'every sentence should earn its place' test by providing negative information value.

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?

Despite having no input parameters (simple surface area), the tool lacks an output schema and has numerous siblings with overlapping domains (most_active, price_shockers). The description fails to explain the scope of data, time windows, or markets covered, leaving critical gaps.

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 (empty object). According to the scoring rubric, 0 parameters establishes a baseline score of 4, as there are no parameters requiring semantic explanation beyond what the schema trivially provides.

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 is literally '$23d', which appears to be a placeholder or corrupted variable reference. It contains no verb, no resource description, and does not explain that this tool retrieves trending stocks or what 'trending' means in this context.

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?

There is zero guidance on when to use this tool versus sibling alternatives like 'bse_most_active', 'nse_most_active', or 'price_shockers'. The string '$23d' provides no semantic content about usage conditions.

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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