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Ledger Lint

ledger_lint
Read-onlyIdempotent

Find the problems that break a lot build: missing values, duplicates, negative balances. FREE.

Typical input {"ledger": } returns {"issues": [{"severity": "error", "row": "cb12", "issue": "sell of 0.5 BTC exceeds balance 0.2 in account coinbase"}], "balances": {"coinbase": {"BTC": "0.2"}}, "counts": {...}}. Balances are running quantities per account and asset in time order, ignoring transfers' fiat values. Use after parse_transactions and before build_lots. Not a tax check: it checks the ledger's arithmetic consistency only. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ledgerYescanonical rows (ts, type, asset, qty, fiat_value, fees, account...).

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

A4.6/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description reveals important behavior: it never raises a protocol error, returns an error object with remediation guidance, computes running balances in time order ignoring fiat values, and returns issues, balances, and counts. This is substantial context an agent needs to interpret results safely.

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 description is dense and front-loaded, with the core purpose stated first, followed by examples and edge-case behavior. The 'FREE.' fragment adds little and the read-only/idempotent sentence repeats annotations, but the overall structure is efficient and readable.

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 single-parameter tool with an output schema and safety annotations, the description covers the input source, pipeline position, semantics, expected output, error contract, and exclusion of tax-checking. Nothing needed for correct invocation and interpretation 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 100%, so the baseline is 3. The description adds value by explaining the ledger param should contain rows from parse_transactions, lists the canonical fields, and clarifies how balances are computed. This exceeds schema-only documentation without fully expanding every row field.

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 names a specific action ('Find the problems that break a lot build') and resource (ledger), and enumerates concrete problem types: missing values, duplicates, negative balances. It also distinguishes its role in the pipeline from siblings by placing it between parse_transactions and build_lots.

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?

It clearly states when to use the tool: after parse_transactions and before build_lots, and gives the expected input shape. It also states a when-not: 'Not a tax check.' It does not explicitly name alternative tools for other scenarios, but the pipeline positioning is strong enough 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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