causal_trace

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

The description states 'Read-only — does not modify any stored data,' which is a key behavioral trait. It also describes the return format: 'Returns an ordered chain of concepts with confidence scores plus the matching solution; returns an empty chain with a message if no causal path is found.' No annotations are provided, so the description adequately covers transparency.

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, well-structured, and front-loaded with the core purpose. It covers prerequisites, behavior, return value, example, and sibling differentiation in a few sentences without extraneous detail. Every sentence adds value.

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 parameter count (4) and absence of an output schema, the description fully explains what the tool returns (ordered chain or empty chain with message), prerequisites, and alternatives. It provides all necessary context for an agent to decide when and how to invoke the 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?

While the input schema already describes all four parameters (100% schema coverage), the description adds value by providing an example invocation and explaining the purpose of each parameter in context (e.g., 'max_depth' is mentioned with default 5). The example clarifies how to use the tool effectively.

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 'Root Cause Analysis through memory' and explains the tool traces causal chains from root cause to symptom and surfaces a solution. It distinguishes from siblings like recall_best_solution and syndicate_search by specifying different use cases.

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?

Explicitly states prerequisites (prior learning via learn_from_attempts or brain_from_git) and provides clear guidance on when to use this tool versus alternatives: 'Use recall_best_solution for direct topic lookup, syndicate_search for community patterns, and causal_trace when you have a symptom and need the full root-cause chain.'

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