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MCP: serve tools from the API process over Streamable HTTP at /mcp
- netx_api/mcp_remote: ops / topology / bizMonitor tools, Bearer auth, per-tool RBAC filtering, ?groups= filter, readOnlyHint and group meta. - Mount the official MCP Streamable HTTP app at /mcp (stateless); AuthAuditMiddleware is pure ASGI and skips /mcp. - Let uvicorn chunk MCP responses; some Windows LSPs rewrite Content-Length replies on */mcp* paths into broken chunked framing. - NETX_MCP_ALLOWED_HOSTS opts into DNS-rebinding checks (off by default so LAN and reverse-proxy hosts work; Bearer is the gate). - Remove the three stdio Python MCP packages; tests moved to /mcp, including real-TCP framing regressions. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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150 changed files with 2367 additions and 2812 deletions
286
netx_api/mcp_remote/topology/layout_sight.py
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286
netx_api/mcp_remote/topology/layout_sight.py
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"""Layout "sight" for agents: crossing hotspots + spatial blocks with coords.
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Gives enough geometry to hand-drag like a human without loading the full canvas
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into chat. Used by analyzeTopologyViewLayout(detail=hotspots|blocks).
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"""
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from __future__ import annotations
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import math
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from typing import Any
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from netx_api.mcp_remote.topology.layout_metrics import (
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collapse_links,
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segments_properly_intersect,
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top_crossing_edges,
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top_crossing_nodes,
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)
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def _short_name(name: str) -> str:
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parts = (name or "").split("-")
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return parts[1] if len(parts) >= 2 else (name or "")[:16]
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def _pos_map(nodes: list[dict[str, Any]]) -> dict[str, tuple[float, float, str]]:
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out: dict[str, tuple[float, float, str]] = {}
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for n in nodes:
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if not isinstance(n, dict):
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continue
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nid = str(n.get("fabric_node_id") or "").strip()
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if not nid:
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continue
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try:
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x, y = float(n.get("x")), float(n.get("y"))
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except (TypeError, ValueError):
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continue
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out[nid] = (x, y, str(n.get("name") or nid))
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return out
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def list_crossings(
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nodes: list[dict[str, Any]],
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edges: list[dict[str, Any]],
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*,
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limit: int = 40,
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) -> dict[str, Any]:
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pos = _pos_map(nodes)
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links = [(a, b) for a, b in collapse_links(edges) if a in pos and b in pos]
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crosses: list[dict[str, Any]] = []
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hit: dict[str, int] = {}
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edge_hit: dict[tuple[str, str], int] = {}
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total_cross = 0
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for i, (a, b) in enumerate(links):
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p1, p2 = (pos[a][0], pos[a][1]), (pos[b][0], pos[b][1])
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e1 = (a, b) if a < b else (b, a)
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for c, d in links[i + 1 :]:
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if len({a, b, c, d}) < 4:
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continue
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p3, p4 = (pos[c][0], pos[c][1]), (pos[d][0], pos[d][1])
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if not segments_properly_intersect(p1, p2, p3, p4):
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continue
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total_cross += 1
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e2 = (c, d) if c < d else (d, c)
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for nid in (a, b, c, d):
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hit[nid] = hit.get(nid, 0) + 1
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edge_hit[e1] = edge_hit.get(e1, 0) + 1
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edge_hit[e2] = edge_hit.get(e2, 0) + 1
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if len(crosses) < limit:
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crosses.append(
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{
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"e1": {
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"a": a,
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"b": b,
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"a_name": _short_name(pos[a][2]),
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"b_name": _short_name(pos[b][2]),
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},
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"e2": {
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"a": c,
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"b": d,
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"a_name": _short_name(pos[c][2]),
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"b_name": _short_name(pos[d][2]),
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},
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}
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)
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# Rank drag candidates: high crossing participation / low degree.
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adj: dict[str, set[str]] = {n: set() for n in pos}
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for a, b in links:
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adj[a].add(b)
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adj[b].add(a)
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from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import orbit_lite_suggest
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pos_xy = {k: (v[0], v[1]) for k, v in pos.items()}
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names = {k: v[2] for k, v in pos.items()}
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candidates = []
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for nid, cnt in sorted(hit.items(), key=lambda kv: -kv[1]):
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deg = len(adj.get(nid, ()))
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if deg >= 8:
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continue
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x, y, name = pos[nid]
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suggest = orbit_lite_suggest(
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nid,
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pos_xy,
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links,
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adj,
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names,
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max_jump=360.0,
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angle_step=45,
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top_k=3,
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)
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best_delta = suggest[0]["delta_crossings_est"] if suggest else 0
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candidates.append(
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{
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"fabric_node_id": nid,
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"name": name,
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"short": _short_name(name),
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"x": round(x, 1),
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"y": round(y, 1),
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"degree": deg,
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"in_crossings": cnt,
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"score": round(cnt / max(deg, 1), 3),
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"suggest_xy": suggest[:3],
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"delta_crossings_est": best_delta,
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}
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)
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if len(candidates) >= min(30, limit):
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break
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top5 = top_crossing_nodes(
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pos_xy, links, names=names, adj=adj, top_n=5, participation=hit
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)
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top_e = top_crossing_edges(
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pos_xy, links, names=names, top_n=5, edge_participation=edge_hit
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)
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# Fallback: when no drag_candidates (all hot nodes are high-degree hubs),
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# suggest from edge_clearance.top and crossing top_nodes regardless of degree.
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if not candidates:
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from netx_api.mcp_remote.topology.layout_metrics import compute_edge_clearance
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ec = compute_edge_clearance(pos_xy, links, names=names, top_n=5)
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for eh in (ec.get("top_edge_hits") or [])[:5]:
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nid = str(eh.get("fabric_node_id") or "")
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if not nid or nid not in pos:
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continue
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x, y, name = pos[nid]
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deg = len(adj.get(nid, ()))
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candidates.append({
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"fabric_node_id": nid,
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"name": name,
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"short": _short_name(name),
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"x": round(x, 1),
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"y": round(y, 1),
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"degree": deg,
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"in_crossings": hit.get(nid, 0),
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"score": 0.0,
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"suggest_xy": [],
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"delta_crossings_est": 0,
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"reason": "edge_clearance_hit",
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})
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for tn in top5:
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nid = str(tn.get("fabric_node_id") or "")
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if not nid or nid not in pos:
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continue
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if any(c["fabric_node_id"] == nid for c in candidates):
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continue
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x, y, name = pos[nid]
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deg = len(adj.get(nid, ()))
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candidates.append({
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"fabric_node_id": nid,
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"name": name,
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"short": _short_name(name),
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"x": round(x, 1),
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"y": round(y, 1),
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"degree": deg,
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"in_crossings": tn.get("crossing_hits", 0),
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"score": round(tn.get("crossing_hits", 0) / max(deg, 1), 3),
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"suggest_xy": [],
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"delta_crossings_est": 0,
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"reason": "top_crossing_high_degree",
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})
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return {
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"edge_crossings": total_cross,
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"crossings_listed": len(crosses),
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"crossings": crosses,
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"top_nodes": top5,
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"top_edges": top_e,
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"drag_candidates": candidates,
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"tip": (
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"先看 top_nodes / top_edges（交叉最重前5网元与前5边）；"
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"再动 drag_candidates（低度数可挪点，suggest_xy=orbit_lite）；"
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"完整扫角用 layoutTopologyView action=orbit_sweep(preview,node_id)→pick。"
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"勿本地穷举坐标。"
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),
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}
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def spatial_blocks(
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nodes: list[dict[str, Any]],
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edges: list[dict[str, Any]],
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*,
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cell: float = 600.0,
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top_k: int = 12,
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) -> dict[str, Any]:
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"""Bucket nodes into coarse grid cells; report densest / most-crossing blocks."""
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pos = _pos_map(nodes)
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if not pos or cell <= 1:
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return {"blocks": [], "cell": cell}
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links = [(a, b) for a, b in collapse_links(edges) if a in pos and b in pos]
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def cell_key(x: float, y: float) -> tuple[int, int]:
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return (int(math.floor(x / cell)), int(math.floor(y / cell)))
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buckets: dict[tuple[int, int], list[str]] = {}
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for nid, (x, y, _) in pos.items():
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buckets.setdefault(cell_key(x, y), []).append(nid)
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# Count crossings whose both midpoints fall in a cell (or either endpoint).
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cell_cross: dict[tuple[int, int], int] = {k: 0 for k in buckets}
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for i, (a, b) in enumerate(links):
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p1 = (pos[a][0], pos[a][1])
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p2 = (pos[b][0], pos[b][1])
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for c, d in links[i + 1 :]:
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if len({a, b, c, d}) < 4:
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continue
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p3 = (pos[c][0], pos[c][1])
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p4 = (pos[d][0], pos[d][1])
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if not segments_properly_intersect(p1, p2, p3, p4):
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continue
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mx = (p1[0] + p2[0] + p3[0] + p4[0]) / 4
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my = (p1[1] + p2[1] + p3[1] + p4[1]) / 4
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ck = cell_key(mx, my)
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cell_cross[ck] = cell_cross.get(ck, 0) + 1
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blocks = []
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for (cx, cy), ids in buckets.items():
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xs = [pos[i][0] for i in ids]
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ys = [pos[i][1] for i in ids]
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sample = sorted(ids, key=lambda i: pos[i][2])[:8]
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blocks.append(
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{
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"cell": [cx, cy],
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"bbox": [
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round(min(xs), 1),
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round(min(ys), 1),
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round(max(xs), 1),
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round(max(ys), 1),
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],
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"node_count": len(ids),
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"crossings_near": cell_cross.get((cx, cy), 0),
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"sample_nodes": [
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{
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"fabric_node_id": i,
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"short": _short_name(pos[i][2]),
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"x": round(pos[i][0], 1),
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"y": round(pos[i][1], 1),
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}
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for i in sample
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],
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}
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)
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blocks.sort(key=lambda b: (-int(b["crossings_near"]), -int(b["node_count"])))
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return {
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"cell": cell,
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"blocks": blocks[:top_k],
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"tip": (
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"分块手拖：选 crossings_near 高的 block，"
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"getTopologyView / queryTopologyNeighborhood 看邻接，"
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"再 updateTopologyViewPositions 挪该块内的点。"
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),
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}
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def build_sight(
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nodes: list[dict[str, Any]],
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edges: list[dict[str, Any]],
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*,
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mode: str = "hotspots",
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limit: int = 40,
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cell: float = 600.0,
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) -> dict[str, Any]:
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mode = (mode or "hotspots").strip().lower()
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out: dict[str, Any] = {"mode": mode}
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if mode in {"hotspots", "both", "all"}:
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out["hotspots"] = list_crossings(nodes, edges, limit=limit)
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if mode in {"blocks", "both", "all"}:
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out["blocks"] = spatial_blocks(nodes, edges, cell=cell, top_k=min(16, max(6, limit // 3)))
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return out
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