Validates row and column totals in financial tables by checking footing and cross-footing, ensuring statements reconcile and arithmetic errors are detected.
Validates that row sums and column sums align (footing and cross-footing). matrix_rows is a 2D float list where the last column is row total and last row is col total.
| Name | Required | Description | Default |
|---|---|---|---|
| matrix_rows | Yes |
Changes observed during successful MCP inspections.
v1.0.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It usefully discloses the matrix contract (last column = row total, last row = column total), but says nothing about failure behavior, tolerance thresholds, or what the caller receives on a mismatch.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences: the purpose comes first, the input contract second, with no filler. Slightly telegraphic but front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description covers the input contract but never indicates the return shape (boolean, list of mismatched cells, error) or how failures are reported. Adequate but leaves a real gap for the calling agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description compensates by defining matrix_rows as a 2D float list with the total column and total row in fixed positions. It stops short of clarifying whether the corner cell is included or whether the totals are validated as data, leaving minor ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Names a specific verb (validates) and a specific check (row sums/column sums alignment, footing and cross-footing), so an agent can tell it is a numeric consistency validator. It does not explicitly distinguish itself from the audit siblings, which is the only gap.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no statement of when to prefer this over audit_balance_sheet, audit_income_statement, or run_benchmark_financial_audit, nor any prerequisites. Usage is only inferable from the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.