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get_hot_keys

Retrieve persisted hot key tracking data to identify top keys by access frequency. Use it to find cache-busting keys, uneven access patterns, or keys that dominate throughput.

Instructions

Get hot key tracking data from persisted storage. BetterDB periodically scans keys using LFU frequency scores (when maxmemory-policy is an LFU variant) or OBJECT IDLETIME / COMMANDLOG-derived frequency. Each snapshot captures the top keys ranked by access frequency. Use this to find cache-busting keys, uneven access patterns, or keys that dominate throughput. The signalType field in each entry indicates which detection mode was active (lfu or idletime).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax entries to return (default 50, max 200)
endTimeNoEnd time (Unix timestamp ms)
startTimeNoStart time (Unix timestamp ms)
instanceIdNoOptional instance ID override
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full responsibility for behavioral disclosure. It explains the detection modes (LFU or idletime), the source (persisted storage), and the signalType field. It lacks mention of potential performance impact or prerequisites, but otherwise provides sufficient transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (~4 sentences) and front-loaded with the main purpose. Every sentence contributes valuable information without redundancy. Efficient structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the moderate complexity (4 optional parameters, no output schema), the description adequately covers the tool's context: what data is returned (top keys, signalType), when to use it, and underlying mechanism. It could provide more detail on the return format, but overall complete enough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for all 4 parameters, so the baseline is 3. The description does not add significant value beyond what the schema already provides for each parameter (e.g., limit default/max, timestamps). It adds no per-parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get hot key tracking data from persisted storage.' It elaborates on the method (LFU or idletime) and provides concrete use cases, making the verb-resource pair specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes explicit usage guidance: 'Use this to find cache-busting keys, uneven access patterns, or keys that dominate throughput.' However, it does not provide alternative tools or when-not-to-use directives, which would be helpful given the large sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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