compare_productsSide-by-side compare 2-4 products of the same category.
Args:
product_slugs: List of 2-4 product slugs. All must be in the same
category (e.g. all credit cards, or all FDs).
Returns:
A markdown comparison table highlighting feature differences.
| Name | Required | Description | Default |
|---|---|---|---|
| product_slugs | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal the operation is read-only and non-destructive. The description goes beyond by specifying the output as a markdown table highlighting feature differences and by establishing the same-category constraint, giving the agent useful behavioral expectations without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured with the primary purpose front-loaded, followed by a clear Args section and a Returns section. Every sentence earns its place with no irrelevant filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single parameter and no output schema, the description covers all essential aspects: what the tool does, the parameter constraint, and the return format. There is no missing information an agent would need to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only the type (array of strings) with no description, so the description must compensate. It does so thoroughly by explaining the parameter represents product slugs, the allowed count range (2-4), the same-category requirement, and concrete examples (credit cards, FDs).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('compare'), a clear resource ('products'), and the scope ('2-4 products of the same category'), which distinguishes it from single-product detail tools like get_product_details and from search or recommendation tools among the siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly communicates the context: use it for side-by-side comparison of 2-4 products that must belong to the same category. It provides examples but does not explicitly name alternatives or state when not to use it, so it stops short of a full usage guideline.
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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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.
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.
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.
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.