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utsunomiya-mcp-server

utsunomiya-mcp-server

by takurot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_dataset_schema

Retrieve the schema (column names and data types) for any Utsunomiya city open dataset by providing its dataset ID.

Instructions

Get schema information for a specific 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": "get_dataset_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesThe ID of the dataset to get schema for.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are present, so the description carries full burden for behavioral disclosure. It only states the retrieval action without indicating side effects, read-only nature, permissions needed, or any constraints. Minimal disclosure for a safe retrieval tool.

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 a single, clear sentence with no extraneous information. It is front-loaded and every word contributes to understanding.

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?

Given the tool has an output schema (not shown but flagged), the description does not need to detail return values. However, it lacks any mention of what 'schema information' entails (e.g., columns, types) and provides no context for integration with sibling tools. Adequate but not rich.

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?

The single parameter 'dataset_id' is fully described in the input schema (100% coverage). The description adds no additional meaning beyond the schema's description, so baseline score of 3 is appropriate.

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 clearly states the tool retrieves schema information for a specific dataset using a specific verb ('Get') and resource ('schema information for a specific dataset'). It distinguishes itself from sibling tools like analyze_dataset and query_dataset.

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 (e.g., analyze_dataset, list_datasets). No prerequisites or exclusions are mentioned, leaving the agent to infer usage context.

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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