Identify unusual spikes or dips in pageviews and revenue using robust historical scores. Metricairn screens signals over a chosen day range, not significance tests.
Unusual spikes/dips (screening signals, not significance tests) in pageviews and revenue (robust historical scores).
| Name | Required | Description | Default |
|---|---|---|---|
| days | 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?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the bar is lower. The description adds genuinely valuable interpretive context: these are screening signals rather than significance tests, telling the agent how to treat and communicate results. It doesn't disclose the lookback window or return specifics, but the caveat is the important disclosure for a detection tool.
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 minimal waste. It is somewhat telegraphic and relies on stacked parentheticals ('screening signals, not significance tests' and 'robust historical scores'), which slightly hurts parseability but keeps it compact.
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?
An output schema exists, so return values need not be explained, and annotations cover safety. What's missing is any explanation of the sole parameter's time-window semantics and how spikes/dips are reported relative to sibling tools. Adequate but with a clear gap for a tool whose only knob is undocumented.
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?
The schema has 0% description coverage on the single 'days' parameter, so the description must carry meaning. It only implies a historical lookback ('historical scores') without stating that 'days' controls the window or what happens at the default of 30. The parameter's role is left for the agent to guess.
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 outcome: detecting unusual spikes/dips in pageviews and revenue using robust historical scores, which goes beyond restating the tool name. It clarifies the statistical character (screening signals, not significance tests). However, it never names or distinguishes itself against siblings like investigate_change or compare, so an agent must infer the boundary.
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?
The phrase 'screening signals, not significance tests' implies a triage/first-pass use case, which is useful context, but the description gives no explicit when-to-use guidance nor names any alternative. The distinction from investigate_change (root-cause) or compare is left entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.