get_journal_summary

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

With no annotations, the description carries the full burden. It clearly indicates a read operation ('queries') with no side effects, and specifies it accesses a local DB. However, it does not disclose potential failure modes or prerequisites like connection requirements, preventing a perfect score.

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, front-loaded sentence that wastes no words. It names the resource, the verb, and the exact data fields, making it both concise and informative.

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?

For a no-parameter read tool with an output schema, the description is complete: it states the purpose and the specific data returned. No additional context is necessary given the simplicity and the presence of the output schema to define return values.

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 tool has zero parameters, and the input schema is empty, so the description adds no parameter meaning. Per the rubric, 0 parameters earns a baseline of 4. The description correctly omits any parameter details.

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 queries a local Trading Journal DB and lists the specific data retrieved (win rate, total trades, average confidence, recent trade logs). This distinct resource and data scope differentiates it from sibling tools like log_journal_entry (writing) and get_open_positions.

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 retrieving journal statistics but does not explicitly state when to use this tool over alternatives or provide exclusions. It lacks guidance on when to prefer this over other trading data tools, so it only partially addresses the dimension.

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