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BACH-AI-Tools

Indian Stock Exchange API2 MCP Server

stock_forecasts

Generate stock price predictions for Indian companies using analyst forecasts and market data to inform investment decisions.

Instructions

$237

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stock_idYesExample value: TCS
measure_codeYesExample value:
period_typeYesExample value:
data_typeYesExample value:
ageYesExample value:
Behavior1/5

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

No annotations are provided, so the description must carry full behavioral disclosure burden. '$237' reveals nothing about safety profile (read-only vs destructive), side effects, rate limits, or return format. The agent cannot determine if this retrieves cached data or triggers a new forecast calculation.

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?

While physically brief (4 characters), this represents under-specification rather than efficient conciseness. The single 'sentence' fails to earn its place by communicating any actionable information. Front-loading is moot when the content is opaque.

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?

Completely inadequate for a 5-parameter financial forecasting tool with no output schema and no annotations. The description '$237' provides none of the necessary context for an agent to understand forecast retrieval semantics, data types, or expected behavior.

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

Parameters3/5

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

Schema description coverage is 100%, establishing baseline 3 per evaluation rules. However, the '$237' description adds zero semantic value about the 5 parameters (stock_id, measure_code, period_type, data_type, age). The schema descriptions are themselves minimal ('Example value: TCS' or empty strings), but the description does not compensate by explaining what 'measure_code' or 'period_type' mean.

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 '$237' is meaningless as a functional description. It contains no verb, no indication of what resource is accessed, and does not distinguish this forecasting tool from sibling tools like 'analyst_recommendations' or 'historical_data'.

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?

Absolutely no guidance provided on when to use this tool versus alternatives, prerequisite conditions, or expected workflows. The string '$237' offers no contextual clues for agent selection.

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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