timps-swarm

timps_dataset_agent

Validate and clean ML training datasets, then generate data cards and quality reports to ensure reliable model training.

Instructions

Validate and clean ML training datasets; generate data cards and quality reports.

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": "timps_dataset_agent"
  }
}

Input Schema

Name	Required	Description	Default
`request`	No	Plain-English task or context for the agent.
`language`	No	Primary programming language (default: python).	python

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.2.1Aug 20, 2026

TDQS

A3.5/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It states actions ('validate and clean') and outputs, but does not explain side effects, destructive potential, permissions, or what happens to the dataset (e.g., modified in place vs. copies). This matches the 'update_drive' calibration example where mutation is implied but safety/reversibility is missing.

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 sentence that efficiently front-loads the core purpose and key outputs. Every phrase adds value, with no redundant or filler content.

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?

The description adequately names the tool's purpose and outputs (data cards, quality reports), which helps for a free-form agent tool. However, it lacks detail on expected input format, return behavior, and side effects. Since there is no output schema and no annotations, more context would improve completeness, but it is not severely deficient.

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 schema already documents both parameters ('request' and 'language') clearly. The description adds no extra parameter detail, but the baseline score of 3 is appropriate since the schema handles the heavy lifting.

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 uses a specific verb ('Validate and clean') and resource ('ML training datasets'), and adds concrete outputs ('generate data cards and quality reports'). This clearly distinguishes it from sibling tools like timps_data_wrangler or timps_data_pipeline by focusing on ML-specific dataset quality artifacts.

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 for ML dataset validation and cleaning, but does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or conditions. It is inferable from the description but not explicitly guided.

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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