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cluster_analysis

Identify natural groupings in database data using K-Means or DBSCAN, with automatic optimal cluster count selection via the elbow method.

Instructions

Кластеризация K-Means/DBSCAN с auto-выбором k (elbow).

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": {
      "table_name": "<table_name>",
      "columns": "<columns>"
    },
    "name": "cluster_analysis"
  }
}

Input Schema

Name	Required	Default
`method`	No	kmeans
`columns`	Yes
`n_clusters`	No
`table_name`	Yes
`sample_size`	No
`min_cluster_size`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations provided, the description must carry the behavioral burden, but it only mentions the algorithms and elbow-based auto-k selection. It does not disclose whether the operation writes to the database, how DBSCAN interacts with the k/n_clusters parameter, or what transformation or result the user should expect.

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

Conciseness4/5

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

The description is a single compact phrase with no wasted words and it front-loads the core method. It is concise, though it achieves conciseness by omitting important details rather than by being efficiently comprehensive.

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

Completeness2/5

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

For a six-parameter tool with a required string columns parameter and no schema descriptions, this description is insufficient. It does not explain how to format columns, when n_clusters is used, the role of sample_size or min_cluster_size, or what the output contains, even though an output schema exists.

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 needs to explain the six parameters. It only clarifies the method choices and hints at auto-selection of k, leaving table_name, columns, sample_size, and min_cluster_size unexplained.

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, 'K-Means/DBSCAN clustering with auto-selection of k (elbow),' clearly identifies the operation as clustering and names the supported algorithms. It is specific about the resource and technique but lacks an explicit verb and does not distinguish the tool from sibling tools like segment_data or classify_rows.

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?

There is no guidance on when to use cluster_analysis versus sibling tools such as segment_data or detect_patterns. The phrase 'clustering' implies the general use case, but no prerequisites, exclusions, or algorithm-selection guidance are provided.

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