constraints_for_task

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

The description explains the tool's scope (segment, ancestors, descendants, not recency-limited) and that it surfaces conflicts. Without annotations, this gives a clear behavioral model. It omits potential side effects, but the description suggests it is read-only.

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?

Two sentences, both relevant and front-loaded. No extraneous information, every word earns its place.

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?

Despite minimal schema and no annotations, the description provides comprehensive context: purpose, usage, scope, and behavior. The output schema covers return format, so completeness is satisfied.

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 full meaning to the single parameter 'segment' by explaining it is the starting point and that the tool expands to ancestors and descendants. This compensates for the 0% schema description coverage.

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 retrieves all decisions and open questions that constrain a task, from the task's segment and its ancestors/descendants, with the purpose of surfacing conflicts before implementation. This differentiates it from sibling tools like context_pull which may provide general context.

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 advises using this tool 'before implementing a task' and 'before coding,' providing clear context for when to invoke it. It does not explicitly mention when not to use it, but the specificity implies it's for constraint discovery.

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