ACLM Lab Interpreter

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

Annotations already declare readOnlyHint=true, so the agent knows it's a safe read operation. The description adds behavioral context beyond the annotations by specifying the nature of the output: risk classifications per marker, lifestyle interventions, and medication deprescription signals. It does not discuss how it handles unknown markers (openWorldHint=false), but the description's disclosure of output types provides useful context without contradicting annotations.

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 communicates the core purpose and outputs without any fluff. Every clause adds value: what it does, against what reference, and what it returns.

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?

The tool has moderate complexity with nested objects and no output schema. The description explains the return content, which is helpful, but it omits key contextual details: the impact of optional 'health_goals' and 'current_medications', whether the tool supports only known lab markers, and how to handle multiple lab_values entries. This leaves gaps for an agent to fully understand invocation behavior.

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 description coverage is only 33% (only 'lab_values' has a description, and it lists common keys). The description provides no additional meaning for the parameters 'health_goals' and 'current_medications', even though they are likely important for tailoring the interpretation. Given the low coverage, the description should compensate but doesn't, leaving the agent to infer the role of optional parameters.

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 a specific action ('Interpret'), a specific resource ('panel of lab values'), and a unique framework ('ACLM-optimized reference ranges'). It also distinguishes itself from the sibling tool 'marker_reference' by describing its outputs (risk classification, lifestyle interventions, medication deprescription signals), which are interpretation results rather than raw reference data.

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: when you have a panel of lab values and need interpretation against ACLM reference ranges. It clearly shows what the tool does and what it returns, which helps the agent decide when to use it. However, it does not explicitly exclude cases (e.g., when only a single marker is needed, where 'marker_reference' might be more appropriate), nor does it mention any prerequisites or limitations.

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?

With readOnlyHint=true annotations already signaling a safe read operation, the description adds useful context by specifying the output content (reference range and lifestyle plan) and the 'ACLM-optimized' qualification. It does not disclose how to handle unknown markers, but the read-only nature is consistent.

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, well-structured sentence that front-loads the action ('Look up') and the result (reference range and lifestyle plan). The parenthetical examples add value without bloating the text.

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 one-parameter lookup tool, the description adequately conveys what is returned and gives examples. However, without an output schema, it does not describe the exact response structure or error handling for unsupported markers, leaving a minor gap.

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 provides only a 'marker' string with no description, giving 0% schema coverage. The description compensates by listing concrete example values (apob, lp_a, hscrp, etc.), which clarifies the expected input format and accepted biomarkers.

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 function: looking up an ACLM-optimized reference range and lifestyle intervention plan for a single biomarker. It provides specific examples (apob, lp_a, etc.) and the 'single biomarker' scoping distinguishes it from the sibling interpret_labs tool.

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 single-biomarker reference lookups through its examples and 'single biomarker' phrasing. However, it does not explicitly mention when to prefer this over interpret_labs or state exclusions, so it lacks a direct alternative comparison.

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