Flag unusually high or low metric values using a z-score threshold. Use it to detect marketing anomalies and investigate outliers in GrowthMCP analytics.
Flag unusually high/low observations using a z-score threshold.
| Name | Required | Description | Default |
|---|---|---|---|
| values | Yes | ||
| z_threshold | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the algorithmic approach (z-score threshold) but remains silent on side effects, read-only nature, authentication needs, and output shape (though an output schema exists). It adds some behavioral context but leaves notable gaps.
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, front-loaded sentence with no filler. Every word contributes to conveying the tool's purpose and method.
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 an output schema, return values needn't be explained, and the tool is simple (two params). However, the description omits usage context, parameter detail, and any behavioral traits, leaving it merely adequate for an agent to invoke correctly without deeper understanding.
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 description coverage is 0%, so the description must compensate. It only indirectly references 'observations' (values) and 'z-score threshold' (z_threshold) without explaining formats, defaults (2 is only in the schema), or what the array should contain. This is minimal compensation for fully undocumented parameters.
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 description states a specific verb ('Flag') and resource ('observations') plus the method ('z-score threshold'), making the function clear. It does not explicitly distinguish itself from sibling analysis tools like calculate_growth_metrics or investigate_growth_issue, so it falls short of a 5.
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?
There is no guidance on when to use this tool versus its siblings, nor any exclusions or prerequisites. The phrase 'using a z-score threshold' implies a statistical context but does not tell an agent when this tool is the right choice over alternatives like analyze_growth_query or investigate_campaign.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.