ACLM Lab Interpreter

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

Annotations include readOnlyHint=true, so the agent knows it is a safe read operation. The description adds valuable behavioral context beyond annotations by specifying that it returns risk classifications, lifestyle interventions, and medication deprescription signals, and that it uses ACLM-optimized ranges. However, it does not mention how it handles unknown lab keys or units, which is a minor gap.

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 two sentences, front-loaded with the primary verb and resource, and contains no filler or redundancy. Every clause contributes meaningful information about the tool's function and outputs.

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 no output schema, the description adequately summarizes return categories (risk classification, lifestyle interventions, deprescription signals). It covers the main inputs and their purpose implicitly. Missing details like biomarker coverage limits or handling of unavailable goals/medications, but for a read-only interpretation tool with simple parameters, it is reasonably complete.

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 only 33%, with health_goals and current_medications having no descriptions. The tool description gives some indirect meaning by mentioning 'lifestyle interventions' (ties to health_goals) and 'medication deprescription signals' (ties to current_medications), but it does not explain how these parameters affect the output or their optionality. It partially compensates for the low schema coverage but leaves gaps.

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's purpose: 'Interpret a panel of lab values against ACLM-optimized reference ranges' and lists specific outputs ('risk classification per marker, lifestyle interventions, and medication deprescription signals'). This distinguishes it from sibling tools like marker_reference (which likely handles single markers) and drug_safety/check_interactions (which focus on medications/drugs).

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 interpreting a panel of lab values, but provides no explicit guidance on when to use this tool versus siblings such as marker_reference or lifestyle_query. There are no stated exclusions or alternative tool recommendations, so the usage context is only implied, not fully articulated.

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

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

Annotations already declare readOnlyHint=true, and the description adds beyond that by specifying the return content (reference range and lifestyle intervention plan) and the ACLM-optimized nature. No contradiction with annotations; the behavior is consistent with a read-only lookup.

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 is concise and informative, with no filler. Every word contributes to understanding the tool's purpose and examples.

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 simple read-only lookup tool with one parameter, the description conveys purpose, examples, and return content. It doesn't detail output format or error handling, but with good annotations and no output schema, it's sufficiently complete for typical use.

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 schema only defines 'marker' as a string with no description. The description compensates by providing six concrete examples (apob, lp_a, hscrp, hba1c, fasting_insulin, vitamin_d) and clarifying that it's a single biomarker, significantly adding meaning beyond the 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?

The description uses the specific verb 'look up' with a clear resource: 'ACLM-optimized reference range and lifestyle intervention plan for a single biomarker.' It distinguishes itself from sibling tools like interpret_labs and lifestyle_query by focusing narrowly on single-biomarker reference lookup, with concrete examples.

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 clearly implies when to use the tool: for a single biomarker reference range and lifestyle plan. It doesn't explicitly mention alternatives or exclusions, but the single-biomarker scope provides clear context, aligning with a 'clear context, no exclusions' level.

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