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sheets_analyze_trends

Read-onlyIdempotent

Runs linear regression on a spreadsheet column to identify trends, helping you see whether values rise, fall, or stay flat over a selected range.

Instructions

Performs linear regression on a column to identify trends

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": {
      "spreadsheet_id": "<spreadsheet_id>",
      "range": "<range>",
      "value_column": "<value_column>"
    },
    "name": "sheets_analyze_trends"
  }
}

Input Schema

Name	Required	Description
`range`	Yes	The A1 notation range to analyze (first row treated as headers)
`value_column`	Yes	0-based column index of the values to analyze
`spreadsheet_id`	Yes	The ID of the spreadsheet

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 13, 2026

TDQS

C2.9/5.0

Behavior2/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds nothing beyond restating the operation and omits the most important behavioral fact for a stats tool: with no output schema, it never says what is returned (slope, intercept, R², p-value).

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?

A single front-loaded sentence with no waste. It is appropriately short, though the brevity is partly why behavioral detail is missing.

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?

With no output schema and a non-trivial statistical operation, the description should disclose the return shape and any assumptions (missing values, minimum rows needed). None of that is present, leaving the agent unable to predict the result.

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 description coverage is 100%, so range, value_column, and spreadsheet_id are already documented, including the header-row convention. The description adds only the numeric/regression framing of value_column, which is marginal; baseline 3 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?

States a specific verb (performs linear regression) and resource (a column) with the goal (identify trends). It is distinguishable from the data-manipulation siblings, though it doesn't name what makes it unique versus the summary/read tools explicitly.

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 on when to use this tool versus alternatives such as sheets_summarize_range or sheets_read_range, and no stated prerequisites or exclusions. The agent must infer usage entirely.

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