# mcp/graph_metrics.py
"""
Basic graph-analytics helpers (pure CPU, no heavy maths):
  • build_nx  – convert agraph nodes/edges → NetworkX graph
  • get_top_hubs – return top-k nodes by degree-centrality
  • get_density – overall graph density
"""

from typing import List, Dict, Tuple
import networkx as nx

# ----------------------------------------------------------------------
def build_nx(nodes: List[Dict], edges: List[Dict]) -> nx.Graph:
    G = nx.Graph()
    for n in nodes:
        G.add_node(n["id"], label=n.get("label", n["id"]))
    for e in edges:
        G.add_edge(e["source"], e["target"])
    return G


def get_top_hubs(G: nx.Graph, k: int = 5) -> List[Tuple[str, float]]:
    """
    Return [(node_id, centrality)] sorted desc.
    """
    dc = nx.degree_centrality(G)
    return sorted(dc.items(), key=lambda x: x[1], reverse=True)[:k]


def get_density(G: nx.Graph) -> float:
    return nx.density(G)