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detect_themes

List auto-detected topic clusters from your vault's knowledge graph. Drill into a cluster by its ID or label to see member notes.

Instructions

List auto-detected topic clusters across the vault (served from the community-detection cache). Pass a theme id or label to drill into one cluster. To recompute with a different Louvain resolution, call reindex({ resolution: X }) first — detect_themes itself is a read-only tool. Each returned cluster carries staleMembersFiltered — the number of cached nodeIds that no longer exist in the vault and were dropped on this read. A positive value means the cached community row is lagging; the filter also regenerates summary so it stays consistent with the filtered nodeIds. Broken-wikilink stub targets are excluded by default; pass includeStubs: true to include them. When the overall vault graph has LOW modularity (<0.3), the response includes { warning, modularity } at the envelope top-level — the clusters aren't clearly separable on this graph and may not reflect meaningful themes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
themeIdNoDrill into a single cluster by its id or label.
includeStubsNoDefault `false`. Set `true` to include unresolved wiki-link targets (`frontmatter._stub: true`) in cluster membership. Older cached community data may still carry stub-dominated clusters until the next reindex regenerates the community table.
Behavior5/5

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

No annotations provided, so description carries full burden. It details read-only nature, caching, staleMembersFiltered, filtering of stubs, and modularity warnings. Very comprehensive.

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?

Description is front-loaded with main purpose and then provides necessary details in a logical order. Every sentence adds value; no fluff.

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?

Despite no output schema, description explains response contents (staleMembersFiltered, warning, modularity) and edge cases. Fully adequate for a read-only tool.

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?

Schema coverage is 100%, but description adds meaning for both parameters: themeId explains drilling, includeStubs explains default behavior and caching implications.

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?

Description clearly states it lists auto-detected topic clusters (specific verb+resource). It distinguishes itself from siblings like reindex by noting that computation is done elsewhere.

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

Usage Guidelines5/5

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

Explicitly tells when to use reindex instead for recomputation. Also explains drilling into a cluster with themeId or label.

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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