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set_labels

Declare table columns as labels or coordinates to exclude them from feature matrices used for describe, project, cluster, and neighbors while retaining them for color_by and annotate; unset reverses.

Instructions

Declare columns of a table as labels / coordinates (integer class codes, year, hour, lat, lon, ids): they are excluded from the feature matrix used by describe/project/cluster/neighbors and stay available for color_by and annotate. unset=True reverses it.

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": {
      "name": "<name>",
      "columns": "<columns>"
    },
    "name": "set_labels"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes
`unset`	No
`columns`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Nov 9, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the downstream effect (exclusion from the feature matrix used by describe/project/cluster/neighbors), the retained capability (color_by, annotate), and the reversibility mechanism (unset=True). It stops short of saying whether the change persists to a saved dataset or is session-scoped.

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?

Two dense sentences, front-loaded with the core action and its effect, with the reversal flag last. No redundant restatement of the tool name or schema types.

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

Completeness4/5

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 description covers the semantic consequence of the call well. Missing pieces are the identity of 'name' and whether the labeling is persisted, which matter for a state-mutating operation.

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

Parameters4/5

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; it gives real meaning to 'columns' (what kinds of columns qualify as labels) and to 'unset' (reverses the declaration). The required 'name' parameter is never explained — presumably the dataset/table identifier — which is the remaining gap.

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 and resource ('Declare columns of a table as labels / coordinates') and enumerates concrete examples of eligible columns (integer class codes, year, hour, lat, lon, ids). It also names the sibling tools whose behavior it affects (describe/project/cluster/neighbors) and the tools that still consume the columns (color_by, annotate), so an agent can distinguish it from neighbors like subset or transform.

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

Usage Guidelines4/5

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

Usage is clearly implied: call this when you want certain columns kept out of the feature matrix but still available for coloring/annotation, and pass unset=True to reverse. It does not explicitly state when NOT to use it (e.g., versus subset/transform for actually removing columns), which keeps it short of a 5.

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