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udaysrinu

ExpensifyAI

by udaysrinu

Confirm Import

confirm_import

After reviewing imported statements, bulk-create expenses from approved rows. Each row becomes a reconciled expense using its split template or defaults to personal.

Instructions

Bulk-create expenses from approved statement rows (after import_statement review).

rows: approved items, each {date, description, amount, category?, split_ref?}. Rows with a split_ref use that saved template; otherwise 100%-personal. group_id: default group for created expenses (0 = non-group). Per-row group_id overrides. Each expense is created via the itemization engine (exact paise, reconciled). Returns a per-row result list (created id or error). Sequential to stay under rate limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
group_idNo
currency_codeNoINR

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and handles it well: it discloses sequential execution to stay under rate limits, per-row result/error returns, exact-paise/reconciled itemization engine behavior, and split_ref fallback logic. No annotation contradiction exists.

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 front-loaded: purpose first, then parameter semantics, then execution and return behavior. Every sentence earns its place, with no filler or repetition of schema defaults.

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?

The description covers input shape, defaults, per-row behavior, return format, and the required prior workflow step, which is enough for correct invocation. The main gap is currency_code semantics and slightly more explicit prerequisites for what makes rows 'approved', but the overall picture is strong.

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?

The schema provides 0% description coverage, but the description compensates by documenting the rows item shape and group_id behavior including the 0=non-group default and per-row override. However, currency_code is not described at all, leaving one of the three parameters to inference.

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 opens with 'Bulk-create expenses from approved statement rows (after import_statement review)', giving a specific verb, resource, and workflow stage. This clearly differentiates it from single-expense creation via create_expense and from the parsing/review step of import_statement.

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?

It explicitly places the tool 'after import_statement review', giving a clear precondition and context for use. It does not name alternatives or say when not to use it, but the workflow placement is enough to guide an agent to the correct stage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.