librarian_ask

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

Since no annotations were provided, the description must carry the full behavioral burden. It explicitly states the types of grounded answers returned (WHAT / WHERE file:line / TOOLS / NEXT), which dictates the expected output structure to the AI.

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 overly verbose and academic. While the information is highly valuable, the structure is dense and lacks efficient phrasing, sacrificing conciseness for exhaustive coverage.

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 its length, the description is highly complete. It covers the scope, return formats, usage rationale, and parameter details, leaving very little ambiguous for the agent.

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 coverage is 0%, so the description must provide all value. It defines the optional 'caller' parameter, advising the user to leave it empty if not needed, adding necessary semantic context beyond the schema's types.

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 tool's purpose is exceptionally clear: to answer questions about the PC's entire system corpus. It explicitly outlines the scope (catalog, tools, machine map, etc.), immediately distinguishing it from general knowledge search or external models.

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 provides explicit usage instructions, stating that AI sessions must use this tool instead of guessing or calling premium models. This is the strongest possible guidance for directing agent workflow.

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