memory_communities

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

Annotations already declare readOnlyHint=true (safe read). The description adds that the process is agent-driven with no LLM in the server (deterministic algorithm), and that it runs on demand via weighted label propagation. This provides useful behavioral context beyond the annotations.

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?

Three sentences with no redundant words. The first sentence explains what it does and how, the second describes the output, and the third gives usage context. Information is front-loaded and every part adds value.

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?

Given the complexity of community detection and lack of output schema, the description explains the algorithm, output contents, and usage context well. It could mention potential computational cost or size limits, but overall it is complete enough for an agent to decide when to use.

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?

Schema coverage is 100% with both parameters (limit, min_size) clearly described. The description does not add any additional semantic information about parameter usage or defaults beyond what the schema provides, so baseline 3 is appropriate.

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 states the tool detects communities over the entity graph via weighted label propagation and returns top entities with linked memories. It distinguishes itself from chunk-level search by providing a corpus-level view, making its purpose specific and distinct from siblings.

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?

Explicitly contrasts with chunk-level search and advises using it to synthesize named themes for global questions like 'what are the main themes?'. However, it does not explicitly list when not to use or mention specific sibling alternatives beyond the implicit contrast.

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