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

The description adds value beyond the read-only and idempotent annotations by specifying the exact output content (relation type and confidence of each edge). This gives the agent a concrete expectation of what the tool returns, though it omits any potential edge cases like pagination or limits.

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, with the first sentence immediately stating the primary function and output. The second sentence provides usage context. Every word earns its place, with no redundancy or filler.

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?

This is a simple 2-parameter tool with an output schema available. The description covers the purpose, usage context, and output content sufficiently. There is no need for further details about return values or edge cases, making the description complete for this complexity level.

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%—both 'label' and 'relation_filter' are described in the input schema. The description does not add any parameter-specific details that are not already present, so the baseline score of 3 applies.

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 uses a specific verb ('Get') and resource ('all concepts directly connected to a given concept'), and clarifies the output includes relation type and confidence. It also references sibling tools (query_graph, get_node) to distinguish from related exploration tools, making the purpose unmistakable.

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 explicitly states when to use this tool ('to explore what's related to a concept you already found via query_graph() or get_node()'), providing clear context. It does not mention when not to use it or name alternatives, but the guidance is sufficient for typical selection.

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