graphify_label_communities

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

Annotations indicate non-destructive and non-read-only behavior. The description adds value by explaining that host-LLM sampling may be used without an API key, that names are generated with cost implications, and that only the largest communities are named. It does not disclose if the tool modifies the graph permanently or any rate limits, but overall adds useful behavioral context beyond 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?

The description is concise, with a single opening sentence stating purpose, then a clear bullet-style list for each parameter. No unnecessary text or repetition. Every sentence 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 tool's complexity (4 optional parameters with defaults) and the presence of an output schema, the description covers the main inputs and behavior well. However, it does not mention prerequisites (e.g., need for an existing graph or communities) or the exact side effect of naming (whether it persists or is ephemeral). Still, it is largely complete for typical 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?

The description adds significant meaning to three of the four parameters ('method', 'limit', 'sample_size'), explaining their options, defaults, and behavioral effects. However, the 'as_json' parameter is not mentioned at all, leaving its purpose unclear. Since schema coverage is 0%, the description compensates well for most but not all parameters.

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's purpose: 'Give the Leiden communities human-readable names.' It uses a specific verb ('give') and resource ('communities'), and the outcome is clear. Among siblings like graphify_communities (which likely lists or computes communities) and graphify_build, this tool uniquely handles naming, providing good differentiation.

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 provides detailed guidance on the 'method' parameter, explaining when to use 'auto', 'sampling', 'cli', or 'placeholder', including fallback logic and cost considerations for 'limit'. However, it does not explicitly state when to use this tool versus alternatives (e.g., other graphify tools) or when not to use it.

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