FinanceGenius.AI

Search Indian financial products

search_indian_products

Read-only

Search FinMall.AI's catalog of Indian financial products.

    Args:
        product_type: One of 'credit_card', 'loan', 'deposit', 'insurance', 'investment'.
                      Omit to search across all categories.
        search_query: Free-text search (matches name + description).
                      E.g. 'cashback', 'home loan', 'HDFC'.
        min_rating: Filter to products with rating >= this value (1-5 scale).

    Returns:
        Up to 10 matching products, sorted by popularity.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`min_rating`	No
`product_type`	No
`search_query`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations only mark it read-only; the description adds that results are capped at 10 and sorted by popularity. It also specifies matching semantics: free-text matches name and description, and min_rating is a >= filter. This provides useful behavioral context beyond annotations.

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

Conciseness5/5

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

The description is well-structured with Args and Returns sections and is front-loaded with the primary purpose. Every line adds value, including concrete examples, with no filler or redundancy.

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

Completeness5/5

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

With no output schema, the Returns line explains output cap and ordering. Given the simple optional-parameter interface and read-only annotations, the description covers everything an agent needs to select and invoke this tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates by documenting all three parameters. product_type lists all valid values and omission behavior, search_query defines match scope with examples, and min_rating gives the comparison and scale.

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?

Description opens with a specific verb+resource statement: 'Search FinMall.AI's catalog of Indian financial products.' This clearly identifies what the tool does and distinguishes it from calculate, compare, explain, and get-details siblings. It is not a tautology because it adds the FinMall.AI catalog scope.

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?

No guidance is provided about when to use this search tool versus siblings like get_product_details or compare_products. The only usage-related note, 'Omit to search across all categories,' addresses parameter behavior rather than tool 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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TDQS

A4.3/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: the calculate_* tools cover different financial formulas, find_best_* tools recommend different product types, and search/get/compare form a coherent product workflow. There is no realistic ambiguity between tools despite the overlap in financial domain.

Naming Consistency5/5

Tool names consistently follow a verb-first snake_case pattern, with clear groupings like calculate_*, find_best_*, and get_*. Minor use of abbreviations like CAGR and EMI is natural and does not break the naming convention.

Tool Count5/5

12 tools is well-scoped for a personal finance assistant covering calculators, product search, product details, comparisons, recommendations, and concept explanations. Each tool earns its place without redundancy or bloat.

Completeness4/5

The tool set covers core financial calculations, product discovery, comparison, recommendations, and educational explanations, which is strong for the stated purpose. Minor gaps exist around loan/insurance product comparisons and more advanced investment planning, but agents can accomplish most typical finance workflows.

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