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089d665 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 | """Gemeo MCP server — exposes the digital twin as an Anthropic Model
Context Protocol server (stdio transport).
Clients (Claude Desktop, Claude Code, custom agents, future tools) get:
- tools: gemeo.lookup, gemeo.state, gemeo.absorb, gemeo.evolve, gemeo.simulate, gemeo.consult
- resources: gemeo://twin/{case_id}/context, gemeo://twin/{case_id}/subgraph, gemeo://twin/{case_id}/full
Run as:
python -m gemeo.mcp_server
Register in mcp_servers.json:
{
"name": "gemeo",
"transport": "stdio",
"command": "python",
"args": ["-m", "gemeo.mcp_server"],
"description": "Gemeo digital twin — graph-RAG over patient context, SUS-aware"
}
"""
from __future__ import annotations
import asyncio
import json
import logging
from typing import Any
logger = logging.getLogger("gemeo.mcp_server")
def _serve_via_mcp_sdk():
"""Use the official MCP Python SDK if available."""
try:
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent, Resource
except ImportError as e:
raise RuntimeError(f"mcp SDK not available: {e}")
server = Server("gemeo")
# ─── Tools ─────────────────────────────────────────────────────────
@server.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="gemeo.lookup",
description=(
"GraphRAG over the patient digital twin. Returns subgraph triples + "
"KG community summaries + (global mode) cohort exemplars + PubMed literature. "
"Use whenever you need grounded clinical evidence."
),
inputSchema={
"type": "object",
"required": ["case_id", "query"],
"properties": {
"case_id": {"type": "string"},
"query": {"type": "string"},
"mode": {"type": "string", "enum": ["local", "global"], "default": "local"},
},
},
),
Tool(
name="gemeo.state",
description=(
"Return the live digital twin state for a case. "
"Pass `section` to restrict to one capability."
),
inputSchema={
"type": "object",
"required": ["case_id"],
"properties": {
"case_id": {"type": "string"},
"section": {
"type": "string",
"enum": [
"diagnoses", "risk", "trajectory", "drugs", "ddi",
"pharmacogen", "family", "reverse_pheno",
"protocol_compliance", "next_questions", "sus_check",
"cohort", "subgraph",
],
},
},
},
),
Tool(
name="gemeo.absorb",
description=(
"Extract clinical entities (HPO, gene, lab, treatment) from a free-text "
"message via LLM-based structured extraction (negation/family-history aware) "
"and feed into the twin via evolve_gemeo. Returns counts of items added."
),
inputSchema={
"type": "object",
"required": ["case_id", "message"],
"properties": {
"case_id": {"type": "string"},
"message": {"type": "string"},
"source": {"type": "string", "default": "user"},
},
},
),
Tool(
name="gemeo.simulate",
description="Monte Carlo simulation of trajectory under stochastic intervention timing/adherence.",
inputSchema={
"type": "object",
"required": ["case_id"],
"properties": {
"case_id": {"type": "string"},
"n_runs": {"type": "integer", "default": 30},
"intervention": {"type": "object"},
"horizons_months": {"type": "array", "items": {"type": "integer"}, "default": [6, 12, 24]},
},
},
),
Tool(
name="gemeo.consult",
description="Multi-specialist agent consult on the twin (geneticist+neuro+ped+imuno+cardio+farma).",
inputSchema={
"type": "object",
"required": ["case_id"],
"properties": {
"case_id": {"type": "string"},
"panel": {"type": "array", "items": {"type": "string"}},
"question": {"type": "string"},
},
},
),
]
@server.call_tool()
async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:
try:
if name == "gemeo.lookup":
from . import graphrag
res = await graphrag.retrieve(
arguments["case_id"], arguments["query"],
mode=arguments.get("mode", "local"),
)
return [TextContent(type="text", text=graphrag.format_for_llm(res))]
if name == "gemeo.state":
from . import core as gcore, llm_context
case_id = arguments["case_id"]
twin = gcore.get_gemeo(case_id) or await gcore.query_gemeo(case_id)
if twin is None:
return [TextContent(type="text", text=f"_no twin for {case_id}_")]
section = arguments.get("section")
if section is None:
return [TextContent(type="text", text=llm_context.serialize_twin_for_llm(twin))]
val = getattr(twin, section, None)
if val is None:
return [TextContent(type="text", text=f"_section `{section}` empty_")]
from dataclasses import asdict
d = asdict(val) if hasattr(val, "__dataclass_fields__") else val
return [TextContent(type="text", text=json.dumps(d, default=str, indent=2))]
if name == "gemeo.absorb":
from . import extractor
out = await extractor.absorb(
arguments["case_id"], arguments["message"],
source=arguments.get("source", "user"),
)
return [TextContent(type="text", text=json.dumps(out, default=str, indent=2))]
if name == "gemeo.simulate":
from . import core as gcore
out = await gcore.simulate(
arguments["case_id"],
n_runs=arguments.get("n_runs", 30),
intervention=arguments.get("intervention"),
horizons_months=arguments.get("horizons_months", [6, 12, 24]),
)
return [TextContent(type="text", text=json.dumps(out, default=str, indent=2))]
if name == "gemeo.consult":
from . import core as gcore
out = await gcore.consult(
arguments["case_id"],
panel=arguments.get("panel"),
question=arguments.get("question"),
)
return [TextContent(type="text", text=json.dumps(out, default=str, indent=2))]
return [TextContent(type="text", text=f"unknown tool: {name}")]
except Exception as e:
logger.exception("tool failed")
return [TextContent(type="text", text=f"_error: {e}_")]
# ─── Resources ─────────────────────────────────────────────────────
@server.list_resources()
async def list_resources() -> list[Resource]:
# Dynamic resources are hard to enumerate without a session.
# Clients can request gemeo://twin/{case_id}/<section> directly.
return [
Resource(
uri="gemeo://twin/{case_id}/context",
name="Twin LLM context",
description="Markdown block with the live patient twin (auto-injected by Gemeo's Python runtime).",
mimeType="text/markdown",
),
Resource(
uri="gemeo://twin/{case_id}/subgraph",
name="Reasoning subgraph",
description="JSON: nodes + edges + narrated paths Patient→...→Disease.",
mimeType="application/json",
),
Resource(
uri="gemeo://twin/{case_id}/full",
name="Full twin snapshot",
description="JSON: every capability for the case.",
mimeType="application/json",
),
]
@server.read_resource()
async def read_resource(uri: str) -> str:
# parse `gemeo://twin/<case_id>/<section>`
if not uri.startswith("gemeo://twin/"):
return f"_unknown resource: {uri}_"
rest = uri[len("gemeo://twin/"):]
try:
case_id, section = rest.split("/", 1)
except ValueError:
return "_malformed gemeo URI_"
from . import core as gcore, llm_context
twin = gcore.get_gemeo(case_id) or await gcore.query_gemeo(case_id)
if twin is None:
return f"_no twin for {case_id}_"
if section == "context":
return llm_context.serialize_twin_for_llm(twin)
if section == "subgraph":
from dataclasses import asdict
return json.dumps(asdict(twin.subgraph) if twin.subgraph else {}, default=str, indent=2)
if section == "full":
return json.dumps(twin.to_dict(), default=str, indent=2)[:60000]
return f"_unknown section: {section}_"
return server, stdio_server
async def main_async():
logging.basicConfig(level=logging.INFO)
server, stdio_server = _serve_via_mcp_sdk()
async with stdio_server() as (read_stream, write_stream):
await server.run(read_stream, write_stream, server.create_initialization_options())
def main():
try:
asyncio.run(main_async())
except RuntimeError as e:
# MCP SDK missing — print helpful message
print(json.dumps({"error": str(e), "hint": "pip install mcp"}))
raise
if __name__ == "__main__":
main()
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