How many AI plans were published, how many clients viewed them, view rate and average minutes to view.
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_coach_engagement"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| sinceDays | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
readOnlyHint=true already declares this is a safe read, so the bar is lower, and the description adds value by disclosing the four metrics returned in the absence of an output schema. It says nothing about aggregation scope, the time window (despite a sinceDays default of 30), or whether zero-data periods are included.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single short sentence with no filler, but it is a noun-phrase/question fragment that lacks a leading verb, so the reader must reconstruct the intent. Front-loading the resource ('AI plans') before the metrics would read better.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description does partially discharge the burden by listing the returned metrics. It is still incomplete for an analytics tool: the time window, whose data is measured, and the units/format of 'average minutes to view' are all unstated.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the single parameter sinceDays is never mentioned in the description, so its meaning, default of 30 days, and 1-365 bounds are only inferable from the JSON schema property name. The description fails to compensate for the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The sentence enumerates the metrics returned ('plans published', 'clients viewed', 'view rate', 'average minutes to view'), which together with the tool name identifies it as an AI-coach engagement report. However, it is a question fragment with no verb or resource statement, so an agent must infer that this retrieves metrics rather than, say, computing them client-side.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to call this versus nearby siblings such as get_client_ai_history, get_ai_coach_preferences_for_client, or get_client_progress. The agent gets no context about scope (per client, per coach, or organization-wide) that would tell it whether this tool is the right one.
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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