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memory_clusters

Detect clusters of related memories by similarity, with adjustable minimum size, score threshold, and algorithm selection.

Instructions

Detect clusters of related memories.

Args: min_cluster_size: Minimum memories to form a cluster (default: 2) min_score: Minimum similarity score to consider connected (default: 0.3) algorithm: "connected_components" (default) or "louvain" Louvain uses embedding similarity for content-based clustering.

Returns: List of clusters with member IDs, sizes, and common tags

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_cluster_sizeNo
min_scoreNo
algorithmNoconnected_components

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It explains the return format (clusters with IDs, sizes, tags) and algorithm behavior, but does not explicitly state that it is read-only or non-destructive. A clearer safety indication would improve this.

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 with a clear purpose statement and a bulleted list of parameters. Every sentence adds value; no redundancy.

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

Completeness5/5

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

Given that there is an output schema (described in text) and no nested objects, the description adequately covers inputs, algorithm choices, and output structure. It is self-contained for a clustering tool among many memory tools.

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

Parameters5/5

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

The input schema has 0% description coverage, but the description adds detailed explanations for each parameter (min_cluster_size, min_score, algorithm) and the algorithm options, enabling correct agent invocation.

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 detects clusters of related memories, with a specific verb and resource. It distinguishes from sibling tools like memory_find_duplicates or memory_related by focusing on clustering.

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

Usage Guidelines3/5

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

Usage is implied (when you want to find clusters), but there is no explicit guidance on when to use this tool vs alternatives, nor when not to use it. The algorithm options are explained but not in comparison to other tools.

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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