get_top_entities

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

No annotations are provided, so the description must fully inform the agent. It discloses that the tool returns a ranked list with scores, types, and other metrics, and lists available metrics. However, it does not specify if the results are computed on the fly or retrieved from stored data, whether the graph must be pre-built, or if there are any side effects (though it is likely read-only). The description is adequate but lacks depth on behavioral traits beyond the obvious.

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 two sentences long, front-loaded with the core purpose in the first sentence, followed by return details and a use case. No redundant or vague language. Every word contributes meaning.

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 has three parameters, no output schema, and no annotations, the description covers the function, return format, and typical use case. It does not mention prerequisites (e.g., graph must exist or metrics already computed) or clarify if the ranking uses precomputed values. However, for a straightforward retrieval tool, the context provided is mostly sufficient.

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 input schema has 100% description coverage, providing clear definitions for metric, limit, and entity_types. The description adds value by explaining the output nature (ranked list of top entities) and the significance of the results ('most important, central, well-connected'), which goes beyond the schema alone. It also hints at 'other metrics' returned, which is not detailed in the schema.

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 specifies the action (get), the resource (top-ranked entities), and the context (by a specific metric like PageRank, betweenness, or degree). It also describes the return format (ranked list with scores, types, and other metrics). However, it does not explicitly differentiate from sibling tools such as 'analyze_graph_pagerank' or 'get_entity_metrics', which might compute or return similar information.

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 states it is 'useful for discovering the most important, central, or well-connected entities', implying a use case. However, it provides no guidance on when to use this tool versus alternatives (e.g., when you need a simple ranked list vs. detailed analytics). With many sibling tools that may overlap, explicit usage guidelines are missing.

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