brain_path

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

With no annotations, the description carries the full burden, but 'BFS over all edge types' is a genuinely useful disclosure — it tells the agent paths are unweighted and that every relationship type is traversable. It omits what happens when no path exists, whether traversal is directed, and any cost or depth 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?

One front-loaded sentence with the resource, the operation, and the algorithm; nothing wasted.

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?

An output schema exists, so return values need not be explained. Still, for a pathfinding tool with zero parameter documentation and no annotations, the absence of no-path/error behavior and directedness leaves real gaps.

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 0% and the properties are bare strings, so the description is the only source of meaning. 'Two brain nodes' implies from_id/to_id are node identifiers, but no format, ID source, or error behavior for invalid IDs is given.

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?

States a specific verb+resource ('shortest path between two brain nodes') and even names the algorithm, so an agent knows exactly what it computes. It does not differentiate itself from siblings like brain_neighbors or brain_context, which could also traverse graph relationships.

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?

No guidance on when this is preferable to brain_neighbors, brain_search, or brain_context. The algorithm note hints at the use case (unweighted reachability) but the agent must infer all routing decisions.

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