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heavnzor
by heavnzor

proofmill_profile

Inspect column names, null counts, and cardinality to assess data quality without exposing raw row samples.

Instructions

Inspect column names, null counts and cardinality without sending raw row samples.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool inspects rather than modifies (read-only implied) and explicitly promises not to send raw row samples, which is a valuable privacy guarantee. However, it does not disclose other potential behaviors such as output format, performance characteristics, or failure modes. The description is minimal but not misleading; it covers the most critical behavioral aspect.

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 a single, concise sentence that front-loads the action ('Inspect') and the key details. It has no filler and efficiently conveys the core purpose and a key constraint. Every word earns its place, making it appropriately sized and well-structured.

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

Completeness2/5

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

The tool is simple (one parameter, no output schema), but the description is incomplete for an agent to use it correctly. It omits any explanation of the 'path' parameter, which is essential for invocation. It also does not describe the output format or any prerequisites (e.g., file type, permissions). While the tool's scope is clear, the missing parameter information makes it incomplete for reliable use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the single 'path' parameter. The description does not mention 'path' at all, leaving the agent to infer what the path points to, how it should be formatted, or whether it refers to a file path, directory, or something else. This is a critical gap because the description adds zero meaning to the parameter.

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 states a specific verb ('Inspect') and resource ('column names, null counts and cardinality'), which clearly defines the tool's function. The phrase 'without sending raw row samples' distinguishes it from siblings like proofmill_validate or proofmill_compare, which might involve data transmission. This is a precise and unambiguous purpose.

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 description implies usage context: it is for inspecting metadata rather than raw data, and explicitly mentions not sending raw row samples, which hints at a privacy/performance trade-off. However, it does not explicitly name alternatives or state conditions like 'use this when you need schema information' or 'when you need validation use proofmill_validate'. The guidance is implicit, not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.