Wever World Data

submit_audit

Audit bounded CSV against caller-supplied expected records with explicit grouping, exact source references, lineage and integrity checks. A completed audit can report mismatches or blocked parsing. Inspect result.status, comparison, ambiguities and unsupported rules. No factual verification or inferred transformations.

Input Schema

TableJSON Schema

Name	Required	Description
`rule`	No	Supports {kind: preserve_rows} or {kind: explicit_groups, groups: [{source_records: [1,2], concatenate: {column: separator}}]}. Source indexes are one-based and exclude the header. Invalid or unsupported rule options are findings with preserving fallback. No grouping is inferred. At most 8192 UTF-8 bytes.
`csv_text`	Yes	At most 8192 UTF-8 bytes, 20 data records, 30 columns and 4096 UTF-8 bytes per cell. Exact strings are preserved.
`request_key`	Yes	Stable idempotency key shared across CSV and audit. Reuse only with identical input for the same capability.
`requested_rules`	No	Descriptive requests, each at most 200 UTF-8 bytes. Every entry is reported unsupported; these do not authorize extra transformations.
`source_metadata`	No	Descriptive metadata only, each value at most 200 UTF-8 bytes. No URL fetching or encoding conversion.
`expected_records`	Yes	Ordered expected records; field names and values are strings of at most 4096 UTF-8 bytes each.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 16, 2026

TDQS

A4.2/5.0

Behavior4/5

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

The description discloses key behavioral traits beyond the annotations: a completed audit can report mismatches or blocked parsing, and callers should inspect result.status, comparison, ambiguities and unsupported rules. It also states that no factual verification or inferred transformations occur, which is valuable and not already provided by the readOnly/destructive hints.

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 three dense sentences with no filler. It front-loads the core action and scope, then gives result-inspection guidance, then states boundary conditions. Every sentence earns its place.

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

Completeness4/5

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

Given the complexity of six parameters and a nested schema, the description is somewhat terse, but the rich input schema and presence of an output schema carry much of the load. It covers the tool's purpose, result shape, and key exclusions, making it sufficient for an agent to invoke correctly without being redundant.

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 the input schema already documents all six parameters in detail. The description adds high-level context about caller-supplied expected records and unsupported rules, but it does not add parameter-level semantics beyond what the schema provides, so the baseline of 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 opens with a specific verb and resource: 'Audit bounded CSV against caller-supplied expected records.' It further specifies the operation's scope with 'explicit grouping, exact source references, lineage and integrity checks,' making it clearly distinct from sibling tools like submit_csv or get_job.

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 clearly implies this tool is for validating a supplied CSV against expected records and inspecting comparison results, while explicitly excluding factual verification and inferred transformations. It does not name alternative tools, but the 'audit' framing and boundary statements provide enough contextual 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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