Moltline TaxLots

Wash Window Check

wash_window_check

Read-onlyIdempotent

Flag loss disposals with a repurchase of the same asset inside the window. FREE.

Typical input {"disposals": , "acquisitions": [{"asset": "ETH", "ts": "2026-02-10T10:00:00Z", "qty": "2"}]} returns {"flags": [{"row": "cb7", "asset": "ETH", "loss": "-410.00", "repurchases": [...]}], "rule_status": "..."}. The 30-day window before and after the sale is the one IRC 1091 uses for stock or securities; as checked on the verification date that statute had not been extended to digital assets that are not securities, so the output is a heads-up for planning, not an adjustment. Use when reviewing loss harvesting. Not a determination that any rule applies. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "disposals and acquisitions must be lists"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description
`disposals`	Yes	rows from build_lots (uses asset, disposed_at, gain, row).
`window_days`	No	days before and after the loss disposal to scan (default 30).
`acquisitions`	Yes	purchases/income rows with asset, ts, qty (ledger rows work as-is).

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds non-obvious behavior: it never raises a protocol error and instead returns an error object with remediation guidance. It also discloses the legal significance of the output (heads-up, not an adjustment) and explicitly states retrying is safe after correcting input.

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 front-loaded with the purpose, followed by a compact example, legal context, usage guidance, and error behavior. Every sentence contributes something necessary; the example JSON and error format earn their place, and the length is justified by the nuance of the feature.

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?

The description covers purpose, input expectations, output shape, error semantics, legal caveats, and safety of retry. Since an output schema exists, the return format is already structured, and the description fills the contextual gaps around when and why to use the tool, making it fully complete for correct invocation.

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 description coverage is 100%, so the schema already documents each parameter. The description adds value beyond the schema by providing typical input and output examples, clarifying that disposals come from build_lots, and explaining the 30-day default via the IRC 1091 context. This exceeds the baseline for high schema coverage.

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 opening sentence uses a specific verb and resource: 'Flag loss disposals with a repurchase of the same asset inside the window.' This cleanly distinguishes it from sibling tools such as build_lots or form_8949_rows, and the example reinforces what the tool computes.

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 explicitly states 'Use when reviewing loss harvesting' and warns 'Not a determination that any rule applies.' It provides clear context for when the tool is appropriate, though it does not name specific alternatives or exclusions, so it falls short of full routing 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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TDQS

A4.6/5.0

Disambiguation5/5

Each of the eight tools targets a distinct stage or analysis: import normalization, ledger validation, transfer pairing, lot building, Form 8949 formatting, broker-form reconciliation, wash-sale checking, and LP math. No two tools could be reasonably confused for one another.

Naming Consistency3/5

Names are all lowercase snake_case and readable, but they mix verb_object forms like parse_transactions, match_transfers, and build_lots with noun-first compound forms like ledger_lint, lp_position_math, and wash_window_check. The convention is not uniform enough to be 'mostly consistent,' though it is still predictable from the domain.

Tool Count5/5

Eight tools is a well-scoped size for a crypto tax-lot workflow; each tool covers a necessary step without redundant helpers. The count feels neither thin nor bloated.

Completeness5/5

The toolset covers the full pipeline from transaction import through linting, transfer matching, lot building, tax form row generation, 1099-DA reconciliation, and wash-sale review. The standalone LP math tool addresses a relevant adjacent need, and there are no obvious dead ends.

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