reasoning_trace

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

With no annotations, the description carries the full burden. It discloses the main workflow (recall, extract, build, optional store) and mentions the potential mutation of storing an inference memory. However, it does not specify side effects of storing (e.g., whether memories are modified, duplicate handling, reversibility) or any prerequisites. This is a moderate level of disclosure.

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 a single sentence that front-loads the main purpose and then provides supporting details, including scholarly citations. It is efficient with no redundant phrasing, though the citations add length without utility for tool invocation.

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?

With four parameters, no annotations, and no output schema, the description is insufficiently complete. It gives a high-level workflow and domain context (math reasoning) but omits parameter semantics, return format, and potential side effects. An agent would need to rely on inference or external knowledge to use this tool correctly.

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 description coverage is 0%, so the description must compensate. It loosely maps 'store conclusion' to store_conclusion and 'recall evidence' to problem/topic, but does not explain top_k at all, nor the exact format/role of each parameter. The description adds some high-level meaning but leaves significant ambiguity for the agent to safely invoke the tool.

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 identifies the tool's action (build a replay-friendly reasoning trace), source (stored memories), and key sub-steps (recall evidence, extract quantities, build per-step trace, optionally store conclusion). It distinguishes itself from siblings like recall (simple retrieval) and reason (general reasoning) by specifying its trace-building and optional inference-memory storage behavior.

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 implies usage for math reasoning circuits with stored memories, but does not explicitly state when to use this tool over alternatives like 'recall_trace' or 'numeric_reasoning'. No exclusions or alternative recommendations are provided, so the agent gets a sense of context but not clear selection guidance.

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