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FinanceGenius.AI

Get product details

get_product_details
Read-only

Get full details of a specific product by its slug.

    Args:
        product_slug: The URL-safe identifier (e.g. 'hdfc-millennia-credit-card').
                      Get slugs from search_indian_products.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
product_slugYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and closed-world scope, so safety is covered. The description adds only the slug-source dependency and nothing about the shape, size, or freshness of the 'full details' returned.

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?

One-line purpose sentence is front-loaded and followed by a compact Args block. The Args formatting mirrors the schema, adding slight redundancy, but nothing is wasted.

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

Completeness4/5

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

For a single-parameter read-only lookup with full annotation coverage, the description supplies everything needed to call it correctly, including value provenance. The only omission is what fields the returned details contain, which is a minor gap without an output schema.

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?

Schema description coverage is 0%, so the description carries the semantic load: it defines product_slug as a URL-safe identifier, gives a concrete example ('hdfc-millennia-credit-card'), and tells the agent where to source valid values. That meaningfully compensates for the bare schema.

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 a specific verb and resource ('Get full details of a specific product') with the lookup key (slug), and names the sibling that produces slugs (search_indian_products). It does not say what 'full details' comprises or contrast directly with compare_products, so it is clear but not fully sibling-differentiated.

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

Usage Guidelines4/5

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

Gives a concrete prerequisite workflow: obtain the slug from search_indian_products before calling. It stops short of stating when not to use this tool (e.g. use compare_products or search_indian_products for browsing), so no exclusions are offered.

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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