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MarkIvor

DataSearcher MCP

by MarkIvor

segment_data

Create data segments using quintiles, deciles, quartiles, tertiles, custom buckets, or RFM analysis. Apply to any column for deeper insight.

Instructions

Сегментация: квинтили/децилы/кастомные бакеты или RFM-анализ.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
columnYes
methodNoquintile
rfm_modeNo
id_columnNo
table_nameYes
date_columnNo
amount_columnNo
custom_boundsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

C2.3/5.0
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 does not mention whether the tool modifies the data, returns a new column, or has any side effects. The output schema exists (per context) but is not described here, so the agent has no idea what happens after invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely short (one sentence), which makes it easy to parse, but it is under-specified for a tool with 8 parameters. It lacks any structured guidance, so the conciseness is not beneficial; it reads more like an incomplete summary than a helpful definition.

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

Completeness1/5

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

Given the complexity of the tool (8 parameters, including an enum and RFM mode), the description is severely incomplete. It does not explain how to configure the segmentation, what inputs are required, or what the output looks like. Combined with zero parameter descriptions in the schema, this tool is nearly impossible to use correctly without external documentation.

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?

The input schema has 0% description coverage, and the tool description does not explain any of the 8 parameters. The description mentions methods but does not map them to the 'method', 'rfm_mode', or 'custom_bounds' parameters. This is a critical gap, as the agent cannot know how to configure the segmentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs segmentation with specific methods (quintiles, deciles, custom buckets, RFM). This distinguishes it from sibling tools like cluster_analysis and classify_rows. However, it does not explicitly mention the input data source (table and column), which is only implied by the schema.

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 is provided on when to use this tool versus alternatives. The description does not mention prerequisites, use cases, or exclusions. An agent must infer usage from the schema alone, which is insufficient for choosing between this and related analysis tools.

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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