hub_trajectory

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

With no annotations provided, the description carries the full burden of behavioral disclosure and largely succeeds. It states the tool is deterministic ("not an ML forecast"), explains what each return field means, and reveals a limitation: "Weight is task-count now; weighted by real durations once logd records them." While it doesn't explicitly declare read-only or side-effect-free behavior, the "Returns:" phrasing implies a safe query operation.

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 and well-structured: it leads with the core purpose, lists return values, provides a use-case sentence, and then notes the current weight limitation. Every sentence adds value, and the length is appropriate for the tool's complexity—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?

The tool has no output schema, so the description's explanation of each return field (ready, blocked, layers, criticalPath, cycles) is essential and provided. It also covers the deterministic nature and current weight behavior. The main omission is an explicit statement about permissions or side effects, but the description and schema together give a fairly complete picture for a read-only analysis tool.

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 covers 100% of parameters (full and project), each with a clear description. The tool description itself adds no extra parameter semantics, but the schema already provides adequate meaning, so the baseline score of 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 opens with a specific verb+resource: "Deterministic dependency-graph plan over tasks' depends_on". It clearly distinguishes itself from an ML forecast and enumerates the exact outputs (ready, blocked, layers, criticalPath, cycles), making its purpose unmistakable and differentiating it from sibling tools like hub_graph or hub_next.

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 an explicit use case: "Use to see 'given deps, what is the actual order / what is the critical path to a milestone'." This is clear context for when to invoke the tool, though it does not mention when not to use it or name alternative tools, which would make it fully explicit.

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