Server definition
- Hash
- sha256:73bdf5ab2be05c826be3f73d0738e17c7264b050fa0745401c6cfc982b2d8067
- What it is
- What a remote MCP server returned when asked what it offers: 9 tools
The blob, as servednamed by its sha256
{
"instructions": "Clinical-grade lifestyle medicine intelligence. Plant-rich, ACLM-anchored, 47 conditions, GLP-aware. Pay-per-call USDC on Base via x402.",
"tools": [
{
"description": "Check a list of medications for food-drug interactions, nutrient depletions, and timing-critical dosing requirements.",
"inputSchema": {
"properties": {
"foods_or_supplements": {
"items": {
"type": "string"
},
"type": "array"
},
"medications": {
"items": {
"type": "string"
},
"type": "array"
}
},
"required": [
"medications"
],
"type": "object"
},
"name": "check_interactions",
"outputSchema": null
},
{
"description": "Return the food-drug safety profile for a single medication.",
"inputSchema": {
"properties": {
"medication": {
"type": "string"
}
},
"required": [
"medication"
],
"type": "object"
},
"name": "drug_safety",
"outputSchema": null
},
{
"description": "Generate an evidence-based whole-food plant-based protocol for one of 47 chronic conditions. Returns therapeutic foods, daily meal structure, foods to minimize, monitoring markers, and clinical citations.",
"inputSchema": {
"properties": {
"condition": {
"enum": [
"angina",
"anxiety",
"ascvd",
"asthma",
"atrial_fibrillation",
"autoimmune",
"barretts",
"bipolar_disorder",
"chronic_kidney_disease",
"chronic_pain",
"copd",
"depression",
"diabetes",
"diabetes_kidney_protection",
"diabetes_with_lipid_risk",
"dvt_pe_history",
"edema",
"epilepsy",
"exercise_induced_bronchospasm",
"fibromyalgia",
"gerd",
"gi_health",
"gout",
"h_pylori",
"heart_failure",
"hypercoagulable",
"hyperlipidemia",
"hypertension",
"hyperthyroidism",
"hyperuricemia",
"hypothyroidism",
"ibd",
"lupus",
"mechanical_valve",
"obesity",
"ocd_ptsd",
"other_autoimmune",
"pcos",
"peptic_ulcer",
"post_thyroidectomy",
"prediabetes",
"psoriasis",
"rheumatoid_arthritis",
"severe_acne",
"stroke_history",
"type_2_diabetes",
"weight_management"
],
"type": "string"
},
"duration_weeks": {
"default": 4,
"maximum": 24,
"minimum": 1,
"type": "integer"
},
"user_context": {
"type": "object"
}
},
"required": [
"condition"
],
"type": "object"
},
"name": "get_protocol",
"outputSchema": null
},
{
"description": "Return the protocol overview for a specific GLP-1 therapy phase (starting, titrating, maintenance, tapering, post_drug).",
"inputSchema": {
"properties": {
"glp1_phase": {
"enum": [
"starting",
"titrating",
"maintenance",
"tapering",
"post_drug"
],
"type": "string"
}
},
"required": [
"glp1_phase"
],
"type": "object"
},
"name": "glp_phase_guide",
"outputSchema": null
},
{
"description": "Generate a GLP-aware nutrition protocol composed on top of any active chronic condition. Returns protein floor (1.2-1.6 g/kg), fiber ramp schedule, GI tolerance interventions, resistance training prescription, hydration target, and phase-specific guidance.",
"inputSchema": {
"properties": {
"condition": {
"type": "string"
},
"glp1_medication": {
"enum": [
"semaglutide",
"tirzepatide",
"liraglutide",
"dulaglutide",
"compounded",
"saxenda"
],
"type": "string"
},
"glp1_phase": {
"enum": [
"starting",
"titrating",
"maintenance",
"tapering",
"post_drug"
],
"type": "string"
},
"user_context": {
"type": "object"
}
},
"required": [
"glp1_phase",
"glp1_medication"
],
"type": "object"
},
"name": "glp_protocol",
"outputSchema": null
},
{
"description": "Interpret a panel of lab values against ACLM-optimized reference ranges. Returns risk classification per marker, lifestyle interventions, and medication deprescription signals.",
"inputSchema": {
"properties": {
"current_medications": {
"items": {
"type": "string"
},
"type": "array"
},
"health_goals": {
"items": {
"type": "string"
},
"type": "array"
},
"lab_values": {
"description": "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.",
"type": "object"
}
},
"required": [
"lab_values"
],
"type": "object"
},
"name": "interpret_labs",
"outputSchema": null
},
{
"description": "Ask any lifestyle medicine question. Returns evidence-based answer with citations from a 200-chunk knowledge base spanning ACLM 6-pillars, B.O.N.S.A.I. nutrition, drug-food interactions, lab interpretation, GLP guidance, wearables, and CGM signal interpretation.",
"inputSchema": {
"properties": {
"max_results": {
"default": 3,
"maximum": 10,
"minimum": 1,
"type": "integer"
},
"pillar": {
"default": "any",
"enum": [
"nutrition",
"movement",
"sleep",
"stress",
"substance_use",
"social_connection",
"labs",
"wearables",
"any"
],
"type": "string"
},
"query": {
"type": "string"
}
},
"required": [
"query"
],
"type": "object"
},
"name": "lifestyle_query",
"outputSchema": null
},
{
"description": "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).",
"inputSchema": {
"properties": {
"marker": {
"type": "string"
}
},
"required": [
"marker"
],
"type": "object"
},
"name": "marker_reference",
"outputSchema": null
},
{
"description": "Ask a nutrition or food-as-medicine question. Returns evidence-based answer from the 200-chunk lifestyle medicine knowledge base.",
"inputSchema": {
"properties": {
"max_results": {
"default": 3,
"maximum": 10,
"minimum": 1,
"type": "integer"
},
"query": {
"type": "string"
}
},
"required": [
"query"
],
"type": "object"
},
"name": "query_nutrition_topic",
"outputSchema": null
}
]
}Verify it yourself
curl -s https://api.teppi.xyz/v1/evidence/sha256:73bdf5ab2be05c826be3f73d0738e17c7264b050fa0745401c6cfc982b2d8067 | sha256sum