Moltline TaxLots

Parse Transactions

parse_transactions

Read-onlyIdempotent

Normalise an exchange CSV export into the canonical ledger rows. FREE.

Supports the Coinbase transaction-history export (Transaction Type / Asset / Quantity Transacted columns), the Kraken ledgers export (txid / refid / type / asset / amount / fee) and a generic CSV with the canonical columns ts, type, asset, qty, fiat_value, fee_qty, fee_asset, fee_fiat, counter_asset, counter_qty, account, id. Typical input {"csv_text": "...", "source_hint": "coinbase"} returns {"rows": [...], "detected": "coinbase", "unrecognised": [...], "warnings": [...]}. Rows it cannot read are returned under unrecognised with the reason, never dropped silently. In every row qty is the whole amount that entered or left the account; when a fee was paid in the same asset, fee_qty is the part of qty that was the fee. Use before ledger_lint and build_lots. Not for other exchanges: convert those to the generic columns first. Errors: on invalid, missing, or malformed input this tool never raises a protocol error; it returns {"error": ""} (for example {"error": "csv_text must be the CSV file contents"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

Name	Required	Description
`account`	No	account label to stamp on every row (defaults to the source name).
`csv_text`	Yes	the CSV file contents (up to 2 MB).
`source_hint`	No	coinbase, kraken or generic; empty to detect from the header.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 6, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds substantive behavior: unrecognised rows are returned with reasons rather than dropped, row/fee semantics are explained, and invalid input returns an error object instead of raising a protocol error, with safe retry after correction.

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 definition is front-loaded and mostly earns its space by covering supported formats, usage order, row semantics, and error behavior. It is somewhat long, and parts such as the return shape overlap with the output schema, but the structure remains clear.

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

Completeness5/5

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

Given a parsing tool with three parameters and an output schema, the description covers supported inputs, error handling, row normalization semantics, and sequencing with sibling tools. An agent has enough information to invoke it correctly without needing to infer missing behavior.

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 all three parameters are already documented in the schema. The description adds typical input/output examples and clarifies source_hint values, but it does not add much parameter semantics beyond the schema, so the baseline 3 is appropriate.

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 gives a specific verb and resource: 'Normalise an exchange CSV export into the canonical ledger rows.' It names the supported exchange formats and explicitly positions the tool before ledger_lint and build_lots, which distinguishes it from siblings.

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

Usage Guidelines5/5

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

It states when to use the tool ('Use before ledger_lint and build_lots') and when not to use it ('Not for other exchanges: convert those to the generic columns first'). The supported inputs and source_hint examples make the selection criteria concrete.

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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