ACLM Lab Interpreter
Server Details
Interpret lab values against ACLM-optimized ranges. Returns deprescription signals.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- rabyavalla/bonsai-api
- GitHub Stars
- 0
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Tool Definition Quality
Average 3.6/5 across 2 of 2 tools scored.
Each tool has a clearly distinct purpose: 'interpret_labs' handles a panel of lab values, while 'marker_reference' focuses on a single biomarker. No overlap or ambiguity.
Both tools use a consistent verb_noun pattern in snake_case ('interpret_labs', 'marker_reference'), making them predictable and easy to understand.
With only 2 tools, the server feels minimal for the domain of lab interpretation. While it covers core needs, additional tools (e.g., for trend analysis or managing patient data) might be expected.
The tools cover the basic operations of interpreting a lab panel and looking up individual markers, but lack support for tasks like updating reference ranges, managing patient history, or interpreting trends over time.
Available Tools
2 toolsinterpret_labsRead-onlyInspect
Interpret a panel of lab values against ACLM-optimized reference ranges. Returns risk classification per marker, lifestyle interventions, and medication deprescription signals.
| Name | Required | Description | Default |
|---|---|---|---|
| lab_values | Yes | Key-value pairs of biomarker names and values. Common keys: hba1c, fasting_glucose, fasting_insulin, ldl, hdl, triglycerides, apob, lp_a, hscrp, vitamin_d, b12, ferritin, tsh, free_t4, free_t3. | |
| health_goals | No | ||
| current_medications | No |
marker_referenceRead-onlyInspect
Look up the ACLM-optimized reference range and lifestyle intervention plan for a single biomarker (e.g., apob, lp_a, hscrp, hba1c, fasting_insulin, vitamin_d).
| Name | Required | Description | Default |
|---|---|---|---|
| marker | Yes |
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