list_journal_presets

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

With no annotations, the description adequately discloses that it is a read-only listing tool that returns preset names and keys. It does not mention any destructive behavior, auth needs, or rate limits, but those are not applicable here.

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 that are well-structured and front-loaded. The first sentence states the purpose, and the second provides usage guidance and examples. No wasted words.

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 tool's simplicity (one optional parameter, no output schema needed), the description is complete. It explains what the tool does, what it returns, and how to use the results. No gaps remain.

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 does not mention the optional 'category' parameter; the schema already fully describes it (100% coverage, with enum and description). The description adds no extra semantic value beyond the schema, so 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 clearly states it lists all available named journal/conference presets and institution group presets, with specific examples like UTD24, FT50, AJG/ABS tiers, and top AI conferences. It sets proper expectations for what the tool returns.

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 says 'Call this first to discover which preset keys to pass to search_in_journal_list or the institution_group parameter,' providing clear when-to-use guidance and hinting at when not to use (when keys are already known). Could be improved with explicit when-not conditions.

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