Koalr

AI Tool Adoption Trend

get_ai_adoption_trend

Read-onlyIdempotent

Get the daily trend of AI tool adoption across Copilot, Cursor, and Claude over the last 30 days (organization-wide). Use this to track whether AI tool adoption is growing or declining. Read-only.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_ai_adoption_trend"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedNov 11, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

removedInput schema / properties / days

Removed value: -{
-  "description": "Number of days to look back for trend data (default: 90)",
-  "type": "number"
-}

AddedJul 9, 2026
RemovedApr 9, 2026
First observedMar 31, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already cover safety (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), and the description reinforces read-only. It adds behavioral facts not in the annotations: organization-wide aggregation, a fixed 30-day window, daily granularity, and the three tools covered. It doesn't describe return shape or data freshness, so it falls short of 5.

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?

Two sentences, front-loaded with the operation and scope, followed by the use case and a read-only note. No filler.

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?

Complete for a zero-parameter read tool. Since no output schema exists, an agent might still want to know the return shape (counts vs. deltas, per-tool breakdown), but the description is largely sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters, so baseline is 4. The description compensates by fixing the implicit scope in prose: org-wide, last 30 days, daily granularity, and three named tools.

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?

States a specific verb (get daily trend), resource (AI tool adoption), scope (organization-wide, last 30 days), and enumerates the tracked tools (Copilot, Cursor, Claude). It distinguishes itself from get_ai_adoption_summary by emphasizing the daily time series over a summary.

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

Usage Guidelines4/5

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

Provides clear intended use: 'Use this to track whether AI tool adoption is growing or declining.' However, it doesn't explicitly name get_ai_adoption_summary or state when to prefer this time-series tool over it.

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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