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Reconcile 1099Da

reconcile_1099da
Read-onlyIdempotent

Diff the broker's 1099-DA lines against the computed disposals. PREMIUM (license).

Typical input {"disposals": , "form_rows": [{"asset": "BTC", "date_sold": "2026-03-04", "qty": "0.5", "proceeds": "31000", "basis": "", "account": "coinbase"}]} returns {"matched": [...], "broker_missing_basis": [...], "basis_mismatch": [...], "proceeds_mismatch": [...], "unmatched_form_rows": [...], "unmatched_disposals": [...]}. A line matches a disposal on the same account (when given), asset and sale date with quantity and proceeds inside the tolerances; several lots sold in one order are summed first. Use when the form arrives and the basis column is blank for coins that came from your own wallet. Not a substitute for the broker's statement. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "disposals must be a non-empty list from build_lots"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
disposalsYesrows from build_lots.
form_rowsYes1099-DA lines: asset, date_sold, qty, proceeds, basis (may be blank), account (optional), id (optional).
money_toleranceNoallowed proceeds/basis difference in fiat units (default 1.00).
qty_tolerance_pctNoallowed quantity difference in percent (default 0.5).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses meaningful behavioral details: it never raises a protocol error but returns a structured error object, it is read-only and idempotent, and it explains the matching algorithm with tolerances and lot summation. These details give an agent accurate expectations for execution and retry.

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 dense but every section earns its place: purpose, example input/output, matching rules, usage trigger, error behavior, and safety guarantee. It is front-loaded with the core purpose and organized so an agent can quickly extract the key information.

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 reconciliation tool with matching logic, tolerance parameters, and error handling, the description covers all critical aspects: input source, return categories, matching criteria, error format, retry safety, and a non-substitute caveat. An agent has enough context to invoke the tool correctly and interpret results.

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 100% and the schema already describes each parameter. The description adds value with a realistic input example, clarifies that basis may be blank, explains that tolerances govern matches, and notes that disposals come from build_lots. This goes beyond a baseline but does not need to fully compensate since the schema is strong.

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 'Diff the broker's 1099-DA lines against the computed disposals' names a specific verb, resource, and operation. The tool is clearly distinguished from siblings like build_lots and form_8949_rows by its reconciliation role and the explicit 'Use when the form arrives...' trigger.

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 concrete use case: 'Use when the form arrives and the basis column is blank for coins that came from your own wallet.' It also warns 'Not a substitute for the broker's statement.' However, it does not explicitly name alternative sibling tools or state when not to use this tool, so it stops short of full when/when-not 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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