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luke-nielsen

ledger-mcp

by luke-nielsen

sum_transactions

Sum filtered ledger transactions to obtain net total, outflow, inflow, and count, aiding discrepancy detection in rent charges and payments.

Instructions

Sum a filtered set of transactions.

Returns net (signed total), outflow (charges) and inflow (payments/credits) magnitudes, and a count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNo
sourceNo
accountNo
date_toNo
date_fromNo
categoriesNo
entry_typesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It reveals that the tool returns aggregated magnitudes and a count, implying a read operation. However, it does not explicitly state that it is read-only, mention any rate limits, authentication needs, or effects on data. The presence of an output schema partially compensates, but more transparency about permissions or side effects would be beneficial. Score 3 is reasonable as it adds some context beyond the schema but lacks completeness.

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 extremely concise: two sentences that directly state the purpose and return value. No redundant or extraneous information. It is well-structured and front-loaded with the core function.

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

Completeness2/5

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

Despite having an output schema, the description neglects to explain the filter parameters or any constraints. With 7 optional parameters and no guidance on valid values, format, or behavior when omitted, the description is incomplete for an agent to use effectively. For example, it does not clarify that 'text' likely searches transaction descriptions or that 'date_from' and 'date_to' are date strings. The sibling tools suggest a financial domain, but the description lacks sufficient context to distinguish filter fields.

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

Parameters1/5

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

The description provides no explanation for any of the 7 parameters. With 0% schema description coverage, the parameters are entirely self-documenting (names only). The description only covers return fields, not what each parameter does (e.g., format for date_from, expected values for categories). This is a critical gap.

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: summing a filtered set of transactions. It specifies the return fields (net, outflow, inflow, count), which distinguishes it from sibling tools like search_transactions (which returns individual transactions) and monthly_spending (time-series aggregation). The verb 'Sum' and resource 'transactions' are specific and unambiguous.

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

Usage Guidelines2/5

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

No usage guidelines are provided. The description does not indicate when to use this tool over alternatives, such as when a total is needed vs. individual transaction details, or how it compares to monthly_spending or transactions_by_category. There are no explicit when-to-use or when-not-to-use instructions.

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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