Check cognitive memory retention to spot declining quality, view distribution buckets, and receive recommendations for improvement.
Retention dashboard. Returns avg retention, retention distribution (buckets: 0-20%, 20-40%, etc.), trend (improving/declining/stable), and recommendation. Lightweight alternative to full system_status focused on memory quality.
| Name | Required | Description | Default |
|---|---|---|---|
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Changes observed during successful MCP inspections.
v2.6.0v2.3.0v2.1.27Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It clearly describes the tool as a read-only dashboard and enumerates its outputs at a useful level of detail, including bucket ranges and trend labels. It does not explicitly say 'does not modify memory,' but the dashboard framing and return-value list strongly imply a non-destructive reporting operation.
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?
Two tight sentences, with the core identity front-loaded ('Retention dashboard') followed by a compact list of return values and the differentiating alternative. Every sentence earns its place and there is zero redundancy.
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 tool with no output schema and no annotations, the description provides a complete picture of what the agent can expect: the type of result, the buckets, trend options, and a recommendation, plus the sibling-tool relationship. Nothing essential is missing for selecting and invoking this tool correctly.
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 has zero parameters and 100% schema description coverage, so there is no parameter semantics to clarify. Per the rubric, a zero-parameter tool receives a baseline of 4; the description correctly adds no irrelevant parameter talk.
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 resource (retention dashboard) and details exactly what it returns: average retention, retention distribution buckets, trend, and recommendation. It also distinguishes itself from system_status as a focused, lightweight alternative, making its purpose unmistakable.
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 description explicitly positions this tool as a lightweight alternative to system_status and focuses on memory quality, giving clear context for when to choose it over a broader status check. It does not enumerate exhaustive exclusion conditions, but the guidance provided is sufficient for a zero-parameter read-only tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.