MCP Machine Learning Agent

export_parquet_audit

Export a copy of the Parquet audit log for submission, with a chosen filename and request ID recorded for traceability.

Instructions

Export a copy of the Parquet audit log for submission.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "export_parquet_audit"
  }
}

Input Schema

Name	Required	Description	Default
`filename`	No	Plain file name for the copy, written to data_parquet/.	audit_export.parquet
`request_id`	No	Your own id for this call; recorded in the audit log.	unknown

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 9, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does not say whether an existing file at the target path is overwritten, whether permissions or prior setup are needed, what the side effects are (the schema alone notes the request_id is recorded in the audit log), or whether the source log is affected beyond the word 'copy'. That is a large gap for a tool with zero annotation coverage.

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?

A single front-loaded sentence with no filler; the verb and object come first. It is arguably too terse for the behavioral gaps it leaves, but there is no wasted text.

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

Completeness3/5

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

An output schema exists, so return values need no explanation, and the schema covers both optional parameters with defaults. However, with no annotations and no mention of overwrite behavior, destination, or side effects, the description is only minimally sufficient for an agent to invoke it confidently.

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%: the schema already documents 'filename' (written to data_parquet/) and 'request_id' (recorded in the audit log) including defaults. The description adds nothing about either parameter, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Export a copy of the Parquet audit log', with the added purpose 'for submission'. It is unambiguous what the tool produces, though it offers no differentiation from siblings (none of which are audit/export tools, so that is a minor gap).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for submission' implies the usage context (producing a deliverable artifact) but there is no explicit when-to-use, when-not-to-use, or named alternative. Usage is only implied, not stated.

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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