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Export records as CSV, Excel or JSON

create_export

Create an export of the records (default), another dataset, or all (the year-end package) for a period, or for the entire history when period is null or omitted. Returns the export object: small exports are already ready with file (name, content_type, bytes, sha256) and a signed download_url valid until download_expires_at (files are kept 7 days); while queued or processing call get_export. Vendor presets (quickbooks, xero, koinly, cointracker, coinledger) and journal need dataset=records and format=csv (otherwise 422). Verify the downloaded file against file.sha256. With a test key pass include_simulated=true to include simulated payments.

Input Schema

NameRequiredDescriptionDefault
formatYes
periodNoRead in `timezone`. Null or omitted exports the entire history.
presetNoColumn layout for the records dataset (default generic).
datasetNoDefault records.
currencyNoReporting currency of the value columns.
metadataNoUp to 50 string key/value pairs returned on the object and in webhooks.
timezoneNoIANA zone, e.g. America/New_York. Default: the account's records timezone.
idempotency_keyNoStable key so a retry does not start a second export, e.g. records-2026-q3.
include_simulatedNoTest mode only: include rows created by simulate_test_payment, which the sandbox hides by default. Ignored with a live key.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
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 does: it discloses the return object shape (file name, content_type, bytes, sha256, signed download_url), the 7-day file retention and download_expires_at, the queued/processing states, the 422 failure mode for preset/dataset mismatches, and test-key behavior for include_simulated. This is unusually complete behavioral disclosure.

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?

Front-loaded with purpose and scope, and every sentence carries operational information. It is a single dense paragraph packing return format, polling rule, validation constraint, and test-mode caveat, which slightly hurts scanability but wastes nothing.

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?

For a 9-parameter tool with no output schema, the description supplies the return-value semantics, the async polling path, retention windows, and the failure constraint an agent would otherwise have to discover by trial. Nothing material to correct invocation is missing.

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 coverage is 89%, so the baseline is 3, but the description adds real meaning beyond it: the preset/dataset/format coupling constraint and the test-mode semantics of include_simulated. It does not restate period/timezone/currency behavior, relying on the schema for those.

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?

Opens with a specific verb+resource ("Create an export of the records") and immediately enumerates the scope options (default records, another dataset, or `all` year-end package). It is clearly distinguishable from siblings like get_export and list_records.

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?

Explicitly routes the agent: null/omitted period means entire history, and "while `queued` or `processing` call get_export" names the alternative tool and the condition that selects it. It also states the constraint that vendor presets and journal require dataset=records with format=csv or else 422.

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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