search_bhsa

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

Discloses behavioral traits: returns generated MQL+results or a primer depending on deployment, and includes Text-Fabric equivalent. No annotations, but description adequately covers behavior.

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?

Three concise sentences, front-loaded with primary function, no filler.

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?

Completes the picture for a simple tool with one parameter: explains output modes, deployment differences, and related tools.

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?

Single 'question' parameter is described as 'plain-language question about the Hebrew Bible', adding semantic context beyond the schema alone. Could include an example, but sufficient.

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?

Clearly states it answers plain-language questions about the Hebrew Bible. Differentiates from siblings like run_mql by explaining it can generate MQL or provide a primer.

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 describes two usage modes (with server-side LLM vs. public deploy) and directs to alternatives like write-mql prompt or run_mql for raw queries.

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