statsView counts, success rates, duplicates prevented, embedding status, and common context tags. Use these metrics to identify patterns and reinforce effective solutions.
Counts, success rate, duplicates prevented, embedding status, most common context tags.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds value by enumerating the metrics returned, but it does not disclose additional behavioral details such as output structure, potential latency, or any dependence on prior operations. With annotations present, this is adequate but not exceptional.
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 sentence that front-loads the key information with a compact comma-separated list. Every word contributes meaning, and there is no redundancy or filler.
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?
For a parameterless, read-only statistics tool with safe annotations, the description covers the main dimensions an agent would need: what the tool reports. The absence of an output schema means the description bears some responsibility for explaining return values, and the high-level list is sufficient for basic invocation, though it doesn't detail types or structure.
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 tool takes zero parameters, and the schema confirms this with 100% coverage. Per the calibration rule, a zero-parameter tool receives a baseline of 4 since there are no parameter semantics to clarify. The description appropriately focuses on output rather than inputs.
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 clearly identifies the tool as providing catalogue statistics through a specific list of outputs: counts, success rate, duplicates prevented, embedding status, and common context tags. It distinguishes itself from the sibling tools (all action-oriented) by describing a read-only reporting function, though it lacks an explicit imperative verb like 'returns' or 'provides'.
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 usage is implied by the tool name and description: call when you need aggregate statistics about the catalogue. However, there is no explicit guidance on when to prefer this tool over siblings, nor any mention of alternatives or exclusions, leaving the agent to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.