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by com.partnro
OverviewDiscussionsAdmin

Summary statistics for a numeric column

dataset_stats

count, min, max, mean, median and sum of a numeric column of the Partnro dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).

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": {
      "column": "<column>"
    },
    "name": "dataset_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
columnYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
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 reveal important behavior: grouping commas and currency are handled, non-numeric rows are excluded, and their count is included. It does not state whether the operation is read-only or what the exact return format is, but the disclosed edge-case handling is genuinely useful.

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 dense sentence with the metric list front-loaded and edge-case behavior in a parenthetical. Every part adds information, with no filler or repetition of the schema.

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?

For a simple one-parameter tool with no output schema, the description covers the purpose, the input semantics, and the edge-case behavior. It is slightly incomplete in not stating the output shape or explicitly saying this is a read-only operation, but those are minor for a stats-only tool.

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 0%, so the description must compensate. It adds meaning by specifying that 'column' refers to a numeric column and that non-numeric/currency-formatted values are special-cased. However, it does not clarify how column names should be passed, whether names are case-sensitive, or what values are valid.

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 names a specific resource (a numeric column of the Partnro dataset), a specific operation (collecting summary statistics), and the exact metrics computed. This clearly distinguishes it from sibling tools like dataset_search or dataset_top, even without naming them.

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

Usage Guidelines3/5

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

The usage context is implied but not explicit: the title and description indicate it is for numeric column summaries, but there is no explicit statement of when to prefer this over dataset_top, dataset_search, or dataset_compare. No alternatives or when-not-to-use guidance is given.

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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