data-analysis-agent

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drop_column

Remove a specific column from a dataset during an analysis session. The operation is reversible, allowing you to restore the column if needed.

Instructions

Drop a column from the dataset. 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": "drop_column"
  }
}

Input Schema

Name	Required	Description	Default
`column`	Yes
`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

B3.4/5.0

Behavior3/5

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

With no annotations, the description must carry behavioral context. It discloses that the operation is 'Reversible', which is a meaningful safety-relevant trait. However, it does not describe what happens to dependent operations, whether data is merely hidden or removed, or other side effects.

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 extremely concise: two short sentences, no filler, and the core action is front-loaded. Every word adds meaning, and the 'Reversible' note earns its place as behavioral context.

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?

For a simple two-parameter operation with an output schema, the description is minimally adequate: it names the action and adds a key reversibility detail. However, it omits usage context, parameter clarification, and side-effect detail, so it is not fully self-sufficient for an agent deciding when and how to invoke it.

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

Parameters2/5

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 explaining the parameters. It only implicitly references 'column', but does not clarify the meaning of 'session_id', column-name requirements, or how the session is used. The agent gets little beyond the raw parameter names.

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 ('Drop'), a resource ('a column'), and the target context ('the dataset'). It is distinct from sibling tools like rename_column or remove_duplicates, so an agent can identify what this tool does immediately.

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?

The description gives no explicit guidance about when to use this tool versus alternatives, and no mention of prerequisites such as an active session. The intended usage is only implied by the tool name and the verb 'Drop'.

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