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utsunomiya-mcp-server

utsunomiya-mcp-server

by takurot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

analyze_dataset

Run statistical analysis on a dataset to uncover patterns and insights. Provide a dataset ID to receive summary statistics and distributions.

Instructions

Perform statistical analysis on a dataset.

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": {
      "dataset_id": "<dataset_id>"
    },
    "name": "analyze_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesThe ID of the dataset to analyze.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description does not disclose what 'statistical analysis' entails—whether it modifies the dataset, requires permissions, or what side effects occur. The behavior is opaque.

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

Conciseness3/5

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

The description is very concise (6 words) but at the expense of informative content. It is front-loaded but insufficient.

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?

Given the output schema exists, the description should briefly indicate what kind of statistics are returned. It does not, leaving the agent uncertain about the tool's output.

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 coverage is 100% and the description adds no additional meaning to the 'dataset_id' parameter beyond the schema's description. Baseline score applies.

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 statistical analysis on a dataset, distinguishing it from sibling tools like list_datasets or query_dataset. However, it lacks specificity on the type of analysis (e.g., descriptive, inferential).

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 given on when to use this tool versus alternatives like query_dataset for raw data retrieval. The description does not specify contexts or exclusions.

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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