data-analysis-agent

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remove_outliers

Detect and remove outlier rows from numeric columns using IQR or z-score methods with adjustable thresholds. Reversible operation for data cleaning.

Instructions

Remove outlier rows in numeric column using 'iqr' (default 1.5x) or 'zscore' (default 3.0). Reversible.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "session_id": "<session_id>",
      "column": "<column>"
    },
    "name": "remove_outliers"
  }
}

Input Schema

Name	Required	Default
`column`	Yes
`method`	No	iqr
`threshold`	No
`session_id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden of disclosing side effects. It does state that the operation removes rows and is reversible, which is useful, but it does not describe the mechanism of reversibility or any other impact on the session/data beyond the row removal.

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?

A single front-loaded sentence that names the action, resource, method options, defaults, and reversibility with no filler words.

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 short description covers core purpose and method semantics, and the output schema covers return values. However, for a 4-parameter mutating tool with no annotations, it omits threshold semantics, session context, and guidance on when to use the tool versus find_outliers.

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 0%, so the description must add meaning. It usefully explains the method defaults (1.5x for iqr, 3.0 for zscore) and implies column must be numeric, but it does not explain the threshold parameter's behavior or the role of session_id.

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?

States a specific verb ('Remove'), a resource ('outlier rows in numeric column'), and method options. This clearly separates it from sibling tools like find_outliers (detection only) and remove_duplicates (different cleaning operation).

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

Usage Guidelines2/5

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

No guidance on when to choose this over find_outliers or how to decide between iqr and zscore. The intended use is only implied by the tool name and description, and no exclusions or prerequisites are mentioned.

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