loinc_search

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

Annotations already confirm read-only and idempotent behavior. Description adds what is returned (codes, names, components, properties). No contradictions. Could mention pagination or result format, but not necessary given output schema.

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?

Extremely concise: two sentences plus a bullet list. Front-loaded with core purpose. Every sentence adds value. No fluff.

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?

Output schema exists, so return values are documented. Description covers core functionality and return content. Lacks details on edge cases (e.g., empty results), but acceptable for a search tool.

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 has 100% descriptive coverage. Description restates schema info almost verbatim (e.g., 'Search term (test name, keyword, or partial LOINC code)'). Adds minimal new meaning beyond schema.

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 searches LOINC for lab tests, clinical observations, and measurements. Lists specific use cases. Does not explicitly differentiate from sibling tools like loinc_answers or loinc_details, but purpose is unambiguous.

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?

Provides explicit use cases (finding codes, searching measurements, looking up observations). Does not specify when not to use or mention alternatives, but context is clear enough for most agents.

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