Detect unusual daily spikes or drops in website traffic by analyzing historical data with adjustable sensitivity settings.
Identify unusual daily spikes or drops in traffic.
| Name | Required | Description | Default |
|---|---|---|---|
| siteUrl | Yes | The URL of the site | |
| days | No | Number of days to look back for baseline (default: 30) | |
| threshold | No | Sensitivity threshold (Standard Deviations, default: 2.5) |
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 bears full responsibility for behavioral disclosure. It states the tool identifies anomalies but does not clarify if it is read-only, how anomalies are calculated, whether it modifies data, or what to expect regarding rate limits or authentication. This is insufficient for an unannotated 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?
The description is a single, concise sentence that front-loads the purpose. While efficient, it could be slightly expanded to include usage guidance or behavioral details without becoming verbose. No redundancy, but the lack of structure beyond a single sentence reduces the score from 5 to 4.
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?
Given the absence of an output schema and annotations, the description should explain what the tool returns (e.g., list of anomalies, scores, dates). It only states it 'identifies' anomalies, leaving the agent without information on output format, pagination, or error handling. This is a significant gap for a tool with no structured return documentation.
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 100%—all three parameters have clear descriptions in the schema. The tool description ('Identify unusual daily spikes or drops in traffic') adds minimal context beyond the schema, so the baseline score of 3 is appropriate.
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 uses a specific verb ('Identify') and clearly states the resource ('unusual daily spikes or drops in traffic'). It distinguishes this tool from siblings like analytics_trends or analytics_drop_attribution by focusing specifically on anomaly detection.
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 explicit guidance on when to use this tool versus alternatives (e.g., analytics_trends for overall patterns, analytics_drop_attribution for drop attribution). The description only implies usage for identifying anomalies but provides no when-not-to-use or alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.