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c05050c 9918f43 c05050c 9918f43 c05050c e28d52e c05050c e28d52e c05050c 1933348 c05050c 1933348 c05050c e28d52e c05050c 1933348 c05050c 1933348 c05050c | 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 | """MCP server stub.
The Model Context Protocol (MCP) is the standard for exposing tools to
any MCP-aware client (Claude Desktop, Cursor, ChatGPT, the Anthropic
SDK, …). This module is the wrapping seam: a single entry point that
turns this project's tools into an MCP server.
The official Python SDK is the ``mcp`` package, kept as an OPTIONAL
dependency so installs without MCP support do not pay the cost.
Usage (after ``pip install mcp``)::
python -m mcp_server # stdio transport for Claude Desktop / Cursor
To register with Claude Desktop, add to ``~/.config/claude/desktop.json``::
{
"mcpServers": {
"mealgraph": {
"command": "python",
"args": ["-m", "mcp_server"],
"cwd": "/path/to/mealgraph"
}
}
}
Only the two genuinely portable surfaces are exposed: the LP-backed
:class:`tools.QuantitiesFinder` and the closed-form
:func:`nutrition_formulas.full_assessment`. There is no LLM-driven
computation tool to wrap — clinical math runs directly in Python.
"""
from __future__ import annotations
import json
from typing import Any, Dict
def _build_server() -> Any:
"""Construct the MCP server lazily so the import is optional."""
try:
from mcp.server.fastmcp import FastMCP # type: ignore
except ImportError as e:
raise SystemExit(
"MCP support requires `pip install mcp`. The rest of the system runs without it."
) from e
from nutrition_formulas import full_assessment as _full_assessment
from tools import QuantitiesFinder
server = FastMCP("mealgraph")
qf = QuantitiesFinder()
# ----- QuantitiesFinder ------------------------------------------------
@server.tool()
def quantities_finder(payload: Dict[str, Any]) -> str:
"""Linear-program meal-quantity solver.
Args:
payload: {"foods": [{name, calories, protein, fat, carbohydrates,
estimated_g, [min_g, max_g, meal_group, estimate_weight]}, ...],
"targets": {calories, protein, fat, carbohydrates},
"meal_constraints": [...]}
Returns:
JSON {"quantities": {...}, "achieved": {...}}
"""
return qf.handle_task(json.dumps(payload))
# ----- Closed-form clinical assessment ---------------------------------
@server.tool()
def assess_user(
weight_kg: float,
height_cm: float,
age_years: float,
sex: str,
activity_level: str,
goal: str,
) -> Dict[str, Any]:
"""One-shot anthropometric + nutritional assessment.
Returns BMI, BMR, TDEE, daily_target_calories, and macro_targets
(protein_g, fat_g, carbohydrates_g). Deterministic closed-form
formulas (Mifflin-St Jeor BMR, ACSM activity multipliers); no LLM
in the path.
"""
return _full_assessment(
weight_kg=weight_kg,
height_cm=height_cm,
age_years=age_years,
sex=sex,
activity_level=activity_level,
goal=goal,
)
return server
def main() -> None:
server = _build_server()
server.run()
if __name__ == "__main__": # pragma: no cover
main()
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