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Tokenbooks - crypto and fiat accounting and payments

Reprocess Portfolio Transaction

reprocess_transaction
Destructive

Recalculate one accounting transaction after correcting its inputs; this destructive queued job may regroup only its assigned wallet movements, and waitForCompletion can return the final state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoOptional immutable root reason; nonempty bytes are preserved exactly
timeoutMsNoMaximum wait time in milliseconds
portfolioRefYesPortfolio name or slug
workspaceRefYesWorkspace name or slug
transactionIdYesSelected portfolio transaction ID to reprocess
pollIntervalMsNoRequest-status polling interval in milliseconds
waitForCompletionNoWait for the queued reprocess request to reach a terminal state before returning

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false. The description adds useful context beyond that: 'queued job' indicates asynchronous behavior, 'may regroup only its assigned wallet movements' specifies a scope limitation, and 'waitForCompletion can return the final state' explains a return behavior option. No contradiction with annotations.

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 a single, well-structured sentence that front-loads the primary purpose ('Recalculate one accounting transaction') before adding key behavioral notes (destructive, queued, regrouping, waitForCompletion). There is zero fluff and every clause earns its place.

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

Completeness3/5

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

With no output schema, the description should clarify return behavior. It mentions 'waitForCompletion can return the final state', but does not describe the response when waitForCompletion is false or the overall job result format. For a complex destructive async operation, a bit more about expected return or failure modes would improve completeness.

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 seven parameters are well-documented in the input schema. The description does not add any parameter-specific meaning beyond what's in the schema (e.g., it mentions waitForCompletion but the schema already describes it). Thus 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 clearly states the action ('Recalculate') and the resource ('one accounting transaction'), plus the context ('after correcting its inputs'). This distinguishes it from siblings like update_transaction (which directly edits) or confirm_transaction (which confirms), making the purpose unambiguous.

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 gives a clear usage context: the tool is for recalculation after inputs have been corrected. It does not explicitly name alternatives or exclusions, but the condition 'after correcting its inputs' sufficiently guides when to use it. It also warns about the destructive and queued nature, which is relevant usage information.

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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