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afthabvp

Indian Market MCP

by afthabvp

find_hidden_gems

Find hidden gem mutual funds with high risk-adjusted returns by filtering on Sharpe ratio, rolling return consistency, low max drawdown, and strong CAGR.

Instructions

Find hidden gem mutual funds — funds with exceptional risk-adjusted returns that are less popular. Focuses on:

  • High Sharpe ratio (best returns per unit of risk)

  • High rolling return consistency (positive in 80%+ periods)

  • Low max drawdown (less pain in crashes)

  • Strong 3Y/5Y CAGR

Example: find_hidden_gems("Small Cap", 3, 10) Example: find_hidden_gems("Flexi Cap")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNo
categoryNoMid Cap
min_age_yearsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description must disclose behavioral traits. It mentions the criteria and gives examples, but does not detail the algorithm, ranking method, or output behavior beyond the criteria. It adds value beyond the schema by listing the selection factors, but lacks depth on operational behavior.

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

Conciseness4/5

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

The description is efficiently structured with a concise purpose statement, a bullet list of criteria, and two examples. It is front-loaded and every sentence adds value. Slightly lengthy due to bullet formatting, but clear and readable.

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?

Given 3 parameters with 0% schema coverage and no annotations, the description should more thoroughly document parameters and behavior. It covers the tool's purpose and criteria but omits parameter explanations and details on how 'less popular' is measured or how results are ranked.

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?

Schema description coverage is 0%. The description does not explicitly explain any parameter; it only shows examples that indirectly demonstrate usage of category, top_n, and min_age_years. The meaning of 'min_age_years' as fund minimum age is not stated, and no default values or constraints are clarified.

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

Purpose5/5

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

The description clearly states the tool finds hidden gem mutual funds with exceptional risk-adjusted returns that are less popular. It lists specific criteria (Sharpe ratio, rolling consistency, drawdown, CAGR) distinguishing it from siblings like find_best_mutual_funds or get_top_funds_by_category.

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

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides examples and criteria, implying when to use (for hidden gems with specific risk metrics). However, it does not explicitly state when not to use or contrast with alternative tools like 'find_best_mutual_funds' or 'get_top_funds_by_category'.

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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