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Categories: Get category suggestions

get_category_suggestions
Read-onlyIdempotent
    Get smart category suggestions for a transaction description.

    Uses a 3-strategy engine: user history, Plaid mapping, and keyword rules.
    Useful when creating transactions to auto-suggest the right category.

    Args:
        description: Transaction description to match (e.g., "Starbucks", "Amazon")
        transaction_type: Optional filter — "income" or "expense"

    Returns:
        Ranked list of suggested categories
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYes
transaction_typeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable behavioral context by revealing the 3-strategy engine (user history, Plaid mapping, keyword rules), which helps the agent understand how suggestions are generated and why results may vary. It also notes the ranked list return, which is useful.

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 compact and well-structured: a one-sentence summary, a brief note on the engine, a usage context line, and clear Args/Returns sections. Every sentence earns its place, and the most important information is front-loaded.

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 read-only suggestion tool with 2 simple parameters and no output schema, the description covers the essentials: what it does, how it works, when to use it, and what it returns. It could mention that the ranked list is limited in size or that suggestions are based on the user's own data, but these are minor gaps given the tool's simplicity.

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 must compensate. It does: it explains that 'description' is the transaction description to match and gives examples ('Starbucks', 'Amazon'), and it clarifies that 'transaction_type' is an optional filter with 'income' or 'expense' values. This adds meaning beyond the bare schema, though it doesn't detail the exact format of the ranked list.

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's purpose: 'Get smart category suggestions for a transaction description.' It specifies the resource (category suggestions) and the action (get), and distinguishes it from related tools like list_categories and bulk_categorize_transactions by focusing on suggestion generation for a single description.

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?

The description says 'Useful when creating transactions to auto-suggest the right category,' which provides clear context for when to use it. It doesn't explicitly mention alternatives or when not to use it, but the use case is specific enough that an agent can infer when to invoke it.

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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