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reconcile

Read-onlyIdempotent

Reconcile expected payments against observed bank entries, identifying exact matches, discrepancies, and partial settlements with explainable scores.

Instructions

Reconcile expected payments against observed bank-statement entries, returning exact matches, short/over payments, split settlements (one-to-many), batch credits (many-to-one) and unmatched residuals, each with an explainable score and reasons.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
optionsNoOptional tuning object: 'abs_tol'/'rel_tol' (amount tolerance), 'date_window_days', 'high_threshold', 'review_threshold', 'currency_strict', 'enable_one_to_many', 'max_combination'.
expectedYesList of canonical records. Each is an object with 'id' (string) and 'amount' (number) required, plus optional 'currency' (ISO 4217), 'date' (ISO-8601), 'counterparty' (name), 'reference' (remittance/end-to-end id).
observedYesList of canonical records. Each is an object with 'id' (string) and 'amount' (number) required, plus optional 'currency' (ISO 4217), 'date' (ISO-8601), 'counterparty' (name), 'reference' (remittance/end-to-end id).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations indicate the tool is read-only, idempotent, and not destructive. The description adds behavioral context by detailing the outcome (matched categories with scores and reasons) and implies no side effects, which is consistent with annotations. No contradictions.

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?

The description is a single sentence that efficiently conveys the core action and expected outputs. It is front-loaded with the main verb and resource. While it packs information densely, it remains clear and avoids unnecessary verbosity.

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?

Given the tool's complexity (3 parameters, output schema available, annotations provided), the description adequately explains the return types (exact matches, short/over, split settlements, etc.) and suggests the algorithm's behavior. It covers the essential context for an agent to understand input/output expectations.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters well. The description summarizes the 'options' tuning object but does not add significant new semantics beyond what is in the schema. It restates the parameter roles at a high level, achieving a baseline score.

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 verb 'Reconcile' and the specific resources 'expected payments' and 'observed bank-statement entries'. It lists the types of matches returned, making the purpose unambiguous. It also distinguishes from sibling tools like list_sandbox_scenarios and explain_match, which serve different functions.

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 explains the tool's purpose for reconciliation and describes the various match types, providing clear context for when to use it. However, it does not explicitly state when not to use it or mention alternative tools for similar tasks, missing an opportunity for clearer guidance.

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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