data-analysis-agent

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group_analysis

Aggregate a metric by one or more categorical dimensions, applying mean, sum, count, median, min, or max to summarize grouped data.

Instructions

Aggregate a metric column grouped by categorical dimension(s) (e.g. group_by='Category', metric='Sales', aggregation='sum'). Aggregations: 'mean', 'sum', 'count', 'median', 'min', 'max'.

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": {
      "session_id": "<session_id>",
      "group_by": "<group_by>",
      "metric": "<metric>"
    },
    "name": "group_analysis"
  }
}

Input Schema

Name	Required	Default
`metric`	Yes
`sample`	No
`group_by`	Yes
`session_id`	Yes
`aggregation`	No	mean

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 4, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are present, so the description carries the full behavioral burden. It states what the operation does but does not disclose whether it mutates the session, requires a saved dataset, how it handles missing values, or what output the agent should expect. This is a meaningful transparency gap.

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 concise and front-loaded with the core purpose, followed by a useful example and allowed aggregations. The aggregation list is slightly redundant with the enum but not harmful.

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 tool has five parameters, no annotations, and several analytical sibling tools, the description is not complete enough for confident selection and invocation. It omits usage guidance, the meaning of session_id and sample, and any behavioral notes. The presence of an output schema helps with return expectations but does not fill these gaps.

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 schema has 0% description coverage, so the description must compensate. It does clarify group_by as categorical dimensions, metric as the column to aggregate, and gives an example mapping parameters. However, session_id and sample are left unexplained, and the aggregation list merely repeats the schema enum.

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 a specific operation: aggregate a metric column grouped by categorical dimension(s), with a concrete example. This distinguishes it from unrelated siblings, but it doesn't explicitly contrast it with similar analytical tools like compute_statistic or frequency_analysis.

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?

The description provides no guidance on when to use this tool versus alternatives, no exclusions, and no mention of related tools. The agent must infer usage context from the name and general wording.

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