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>
This commit is contained in:
oliver 2026-10-16 21:52:08 +08:00
parent 918ece86c3
commit 52679a7cfc
150 changed files with 2367 additions and 2812 deletions

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@ -0,0 +1,990 @@
"""MCP-facing layout runner: full layout + local fix/relax (no temp scripts)."""
from __future__ import annotations
from dataclasses import replace
from typing import Any
from netx_api.mcp_remote.topology.layout_ops import (
LayoutParams,
build_state_from_nodes_edges,
positions_for_api,
run_recipe,
score_state,
)
from netx_api.mcp_remote.topology.layout_ops.hotspots import (
fix_overlaps_local,
hotspot_scopes,
overlapping_nodes,
)
from netx_api.mcp_remote.topology.layout_ops.recipe import RECIPES
from netx_api.mcp_remote.topology.layout_ops.state import LayoutState
from netx_api.mcp_remote.topology.layout_ops.transforms import normalize_origin
from netx_api.mcp_remote.topology.layout_ops.channels import (
straighten_channels_greedy,
straighten_params_from_overrides,
)
from netx_api.mcp_remote.topology.layout_ops.dual_units import (
dual_unit_params_from_overrides,
layout_dual_unit,
)
from netx_api.mcp_remote.topology.layout_ops.rigid_units import groups_from_membership
from netx_api.mcp_remote.topology.layout_ops.untangle import (
untangle_crossings,
untangle_params_from_overrides,
)
from netx_api.mcp_remote.topology.layout_ops.press_crossings import (
park_phantom_nodes,
polish_crossings,
press_params_from_overrides,
)
from netx_api.mcp_remote.topology.layout_ops.clear_edge_hits import (
clear_edge_hits,
clear_edge_params_from_overrides,
)
from netx_api.mcp_remote.topology.layout_ops.compact_bbox import (
compact_bbox,
compact_bbox_params_from_overrides,
)
from netx_api.mcp_remote.topology.layout_ops.pull_far_chains import (
pull_far_chains,
pull_far_chains_params_from_overrides,
)
from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import (
apply_orbit_pick,
orbit_params_from_overrides,
orbit_sweep_node,
orbit_sweep_round,
orbit_sweep_until_limit,
)
from netx_api.mcp_remote.topology.layout_ops.level_util import (
apply_level_bands,
level_bands_params_from_overrides,
)
# Public recipe names → internal multipass ids
RECIPE_ALIASES: dict[str, str] = {
"rings": "agg_rings_v1",
"corridor": "smd_corridor_v1",
"compact": "smd_corridor_compact_v1",
"unstick": "smd_corridor_unstick_v1",
"agg_rings_v1": "agg_rings_v1",
"smd_corridor_v1": "smd_corridor_v1",
"smd_corridor_compact_v1": "smd_corridor_compact_v1",
"smd_corridor_unstick_v1": "smd_corridor_unstick_v1",
}
PRESETS: dict[str, dict[str, float]] = {
"loose": {
"target_nn": 170.0,
"scale_cap": 2.8,
"target_util": 0.12,
"pack_min_scale": 0.35,
"pack_iters": 6,
"island_pad_x": 200.0,
"island_pad_y": 180.0,
"lane": 300.0,
"x_gain": 1.8,
"pitch": 220.0,
"side": 200.0,
"an_gap": 560.0,
"width_mul": 2.4,
"height_mul": 1.5,
},
"balanced": {
"target_nn": 155.0,
"scale_cap": 2.2,
"target_util": 0.18,
"pack_min_scale": 0.28,
"pack_iters": 6,
"island_pad_x": 180.0,
"island_pad_y": 160.0,
"lane": 260.0,
"x_gain": 1.8,
"width_mul": 2.0,
"height_mul": 1.35,
},
"dense": {
"target_nn": 145.0,
"scale_cap": 2.0,
"target_util": 0.28,
"pack_min_scale": 0.22,
"pack_iters": 8,
"island_pad_x": 160.0,
"island_pad_y": 160.0,
"lane": 240.0,
"x_gain": 1.8,
"width_mul": 1.7,
"height_mul": 1.2,
},
}
_PARAM_KEYS = (
"target_nn",
"scale_cap",
"target_util",
"pack_min_scale",
"pack_iters",
"pack_nn_floor",
"island_pad_x",
"island_pad_y",
"cluster_gap",
"cluster_thr",
"x_gain",
"lane",
"overlap_iters",
"overlap_step",
"width_mul",
"height_mul",
"pitch",
"side",
"an_gap",
)
ACTIONS = (
"layout",
"fix_overlaps",
"untangle",
"straighten_channels",
"layout_dual_unit",
"polish_crossings",
"clear_edge_hits",
"compact_bbox",
"pull_far_chains",
"align_reference",
"orbit_sweep",
"level_bands",
)
def resolve_recipe(name: str | None) -> str:
key = str(name or "rings").strip().lower() or "rings"
if key not in RECIPE_ALIASES:
raise ValueError(f"unknown_recipe:{key}")
internal = RECIPE_ALIASES[key]
if internal not in RECIPES:
raise ValueError(f"recipe_not_registered:{internal}")
return internal
def build_params(
*,
preset: str = "balanced",
overrides: dict[str, Any] | None = None,
) -> LayoutParams:
preset_key = str(preset or "balanced").strip().lower() or "balanced"
if preset_key not in PRESETS:
raise ValueError(f"unknown_preset:{preset_key}")
base = LayoutParams()
merged = {**PRESETS[preset_key]}
for k, v in (overrides or {}).items():
if k not in _PARAM_KEYS or v is None:
continue
try:
merged[k] = float(v) if k not in {"pack_iters", "overlap_iters"} else int(v)
except (TypeError, ValueError):
continue
return replace(base, **merged)
def _rank_key(fin: dict[str, Any]) -> tuple:
rk = (fin.get("score") or {}).get("rank_key")
if isinstance(rk, list) and rk:
return tuple(rk)
ov = int(fin.get("footprint_overlap_pairs") or 0) + int(
fin.get("label_overlap_pairs") or 0
)
total = float((fin.get("score") or {}).get("total") or 0.0)
cross = int(fin.get("edge_crossings") or 0)
return (ov, -total, cross)
def _tune_grid(base: LayoutParams) -> list[LayoutParams]:
"""Sweep knobs that actually move util/nn/crossings (incl. skeleton scale)."""
out: list[LayoutParams] = []
for tu, pms in (
(0.08, base.pack_min_scale),
(0.14, max(0.45, base.pack_min_scale - 0.05)),
(0.20, max(0.40, base.pack_min_scale - 0.10)),
(0.12, min(0.70, base.pack_min_scale + 0.05)),
):
for wm, hm in ((2.2, 1.4), (2.8, 1.7), (3.5, 2.0)):
for tnn in (base.target_nn, 160.0, 140.0):
out.append(
replace(
base,
target_util=tu,
pack_min_scale=pms,
target_nn=tnn,
width_mul=wm,
height_mul=hm,
)
)
seen: set[tuple] = set()
uniq: list[LayoutParams] = []
for p in out:
key = (
p.target_util,
p.pack_min_scale,
p.target_nn,
p.width_mul,
p.height_mul,
)
if key in seen:
continue
seen.add(key)
uniq.append(p)
return uniq[:12]
def _pack_result(
st: LayoutState,
fin: dict[str, Any],
*,
action: str,
recipe: str | None,
recipe_id: str | None,
preset: str,
params: LayoutParams,
tune: bool,
tried: list[dict[str, Any]] | None,
local: dict[str, Any] | None = None,
) -> dict[str, Any]:
report = dict(fin.get("report") or {})
meta = st.meta or {}
return {
"ok": True,
"action": action,
"recipe": recipe,
"recipe_id": recipe_id,
"preset": preset,
"tune": bool(tune),
"tried": tried,
"local": local,
"rings_mode": meta.get("rings_mode"),
"min_rings": meta.get("min_rings"),
"params_used": {
k: getattr(params, k)
for k in (
"target_nn",
"scale_cap",
"target_util",
"pack_min_scale",
"island_pad_x",
"island_pad_y",
"cluster_gap",
"x_gain",
"lane",
)
},
"node_count": len(st.positions),
"positions": positions_for_api(st),
"verdict": report.get("verdict"),
"size": report.get("size"),
"overlap": report.get("overlap"),
"crossing": report.get("crossing"),
"spacing": report.get("spacing"),
"sparsity": report.get("sparsity"),
"edges": report.get("edges"),
"chains": report.get("chains"),
"rings": report.get("rings"),
"score": report.get("score"),
"summary": fin.get("summary") or {},
"guide": report.get("guide"),
}
def _ensure_zero_overlap(st: LayoutState, params: LayoutParams) -> tuple[LayoutState, dict[str, Any]]:
"""If any overlap remains, surgical local pull-apart (not global scale)."""
before = len(overlapping_nodes(st))
if before == 0:
return st, {"ran": False, "overlaps_before": 0, "overlaps_after": 0}
op = fix_overlaps_local(st, params)
after = len(overlapping_nodes(op.state))
return op.state, {
"ran": True,
"overlaps_before": before,
"overlaps_after": after,
"moved_n": len(op.moved),
"note": op.note,
}
def run_layout_on_graph(
nodes: list[dict[str, Any]],
edges: list[dict[str, Any]],
*,
action: str = "layout",
recipe: str = "rings",
preset: str = "balanced",
params: dict[str, Any] | None = None,
tune: bool = False,
) -> dict[str, Any]:
"""Compute layout or local polish on existing positions. Does not PATCH."""
action_key = str(action or "layout").strip().lower() or "layout"
if action_key not in ACTIONS:
raise ValueError(
f"unknown_action:{action_key}; "
f"allowed={','.join(ACTIONS)}"
)
base_params = build_params(preset=preset, overrides=params)
st0 = build_state_from_nodes_edges(nodes, edges)
park_phantom_nodes(st0)
# Inject rigid/portal groups from staging membership when provided.
if params:
raw = params.get("_rigid_membership") or params.get("rigid_membership")
if isinstance(raw, list):
groups: list[dict[str, Any]] = []
pairs: list[tuple[str, list[str]]] = []
any_pivots = False
for row in raw:
if not isinstance(row, dict):
continue
key = str(row.get("key") or "").strip()
ids = [str(x) for x in (row.get("node_ids") or []) if str(x)]
pivots = [str(x) for x in (row.get("pivots") or []) if str(x)]
if not key or len(ids) < 2:
continue
if pivots:
any_pivots = True
groups.append({"key": key, "node_ids": ids, "pivots": pivots})
pairs.append((key, ids))
if groups:
if not any_pivots:
groups = groups_from_membership(pairs)
st0 = st0.copy()
st0.meta = dict(st0.meta or {})
st0.meta["compose_views"] = {
**(st0.meta.get("compose_views") or {}),
"rigid_groups": groups,
}
# --- local actions: keep current coordinates, do not rebuild skeleton ---
if action_key == "fix_overlaps":
op = fix_overlaps_local(st0, base_params)
st, fix_meta = _ensure_zero_overlap(op.state, base_params)
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={"op": op.params, "ensure": fix_meta, "hotspots": len(hotspot_scopes(st0))},
)
if action_key == "untangle":
knobs = untangle_params_from_overrides(params)
op = untangle_crossings(st0, base_params, **knobs)
# Origin shift only — soft_nn_scale can inflate an already-good human
# layout; local jumps are capped inside untangle_crossings.
st = normalize_origin(op.state, base_params).state
# Overlap crush must not erase untangle gains.
from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings as _cx
x_before_fix = _cx(st.positions, st.links)
st2, fix_meta = _ensure_zero_overlap(st, base_params)
x_after_fix = _cx(st2.positions, st2.links)
if x_after_fix <= x_before_fix + 2:
st = st2
else:
fix_meta = {
**fix_meta,
"reverted": True,
"crossings_before": x_before_fix,
"crossings_after": x_after_fix,
"reason": "overlap_fix_raised_crossings",
}
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={"op": op.params, "note": op.note, "meta": st.meta.get("untangle"), "ensure": fix_meta},
)
if action_key == "straighten_channels":
knobs = straighten_params_from_overrides(params)
op = straighten_channels_greedy(st0, base_params, **knobs)
st, fix_meta = _ensure_zero_overlap(op.state, base_params)
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get("straighten_channels"),
"ensure": fix_meta,
},
)
if action_key == "layout_dual_unit":
knobs = dual_unit_params_from_overrides(params)
op = layout_dual_unit(
st0,
base_params,
unit_id=knobs.get("unit_id"),
portal_a=knobs.get("portal_a"),
portal_b=knobs.get("portal_b"),
require_zero_cross=bool(knobs.get("require_zero_cross", False)),
)
accepted = bool(op.params.get("accepted", False))
# Always return best-effort dual geometry (even if accepted=False).
# Do NOT run global overlap crush — it reintroduces crossings.
st = op.state
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"accepted": accepted,
"meta": st.meta.get("layout_dual_unit"),
"ensure": {
"ran": False,
"reason": "dual_unit_skips_global_overlap_crush",
},
},
)
if action_key == "orbit_sweep":
knobs = orbit_params_from_overrides(params)
do_round = bool(knobs.get("round"))
do_until = bool(knobs.get("until_limit"))
node_id = str(knobs.get("node_id") or "").strip()
frozen = knobs.get("frozen_ids")
protect = knobs.get("protect_rigid", "off")
if do_until:
# until_limit wins over round/node_id (single-point loop to stall).
op = orbit_sweep_until_limit(
st0,
params=base_params,
max_degree=int(knobs.get("max_degree") or 14),
max_jump=knobs.get("max_jump"),
angle_step=knobs.get("angle_step"),
nn_floor=float(knobs.get("nn_floor") or 36.0),
min_angle_sep=float(knobs.get("min_angle_sep") or 35.0),
protect_rigid=protect,
frozen_ids=frozen,
freeze_layers=knobs.get("freeze_layers"),
freeze_levels=knobs.get("freeze_levels"),
y_min=knobs.get("y_min"),
y_max=knobs.get("y_max"),
objective=str(knobs.get("objective") or "crossing"),
max_moves=int(knobs.get("max_moves") or 40),
stall_limit=int(knobs.get("stall_limit") or 12),
max_stretch=float(knobs.get("max_stretch") or 32.0),
min_delta=int(knobs.get("min_delta") or 1),
scan_cap=int(knobs.get("scan_cap") or 32),
top_k=int(knobs.get("top_k") or 8),
prefer_low_degree=bool(knobs.get("prefer_low_degree", True)),
cand_cap=int(knobs.get("cand_cap") or 360),
bundle=bool(knobs.get("bundle", True)),
bundle_max=int(knobs.get("bundle_max") or 10),
)
st = normalize_origin(op.state, base_params).state
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get("orbit_sweep"),
"until_limit": True,
},
)
if do_round:
op = orbit_sweep_round(
st0,
params=base_params,
top_n=int(knobs.get("top_n") or 12),
max_degree=int(knobs.get("max_degree") or 9),
max_jump=knobs.get("max_jump"),
angle_step=knobs.get("angle_step"),
nn_floor=float(knobs.get("nn_floor") or 36.0),
min_angle_sep=float(knobs.get("min_angle_sep") or 35.0),
protect_rigid=protect,
frozen_ids=frozen,
freeze_layers=knobs.get("freeze_layers"),
freeze_levels=knobs.get("freeze_levels"),
focus_ids=knobs.get("focus_ids"),
y_min=knobs.get("y_min"),
y_max=knobs.get("y_max"),
objective=str(knobs.get("objective") or "crossing"),
)
st = normalize_origin(op.state, base_params).state
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get("orbit_sweep"),
"round": True,
},
)
if not node_id:
raise ValueError(
"orbit_sweep_requires_node_id_or_round_or_until_limit:"
"params.node_id=… or params.round=true or params.until_limit=true"
)
sweep = orbit_sweep_node(
st0,
node_id,
params=base_params,
max_jump=knobs.get("max_jump"),
angle_step=knobs.get("angle_step"),
nn_floor=float(knobs.get("nn_floor") or 36.0),
min_angle_sep=float(knobs.get("min_angle_sep") or 35.0),
cand_cap=int(knobs.get("cand_cap") or 280),
protect_rigid=protect,
frozen_ids=frozen,
freeze_layers=knobs.get("freeze_layers"),
freeze_levels=knobs.get("freeze_levels"),
top_k=int(knobs.get("top_k") or 3),
y_min=knobs.get("y_min"),
y_max=knobs.get("y_max"),
objective=knobs.get("objective", "crossing"),
)
if not sweep.get("ok"):
fin = score_state(st0)
return _pack_result(
st0,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={"op": sweep, "note": f"orbit_sweep:{sweep.get('error')}"},
)
# Suggest-only keeps coords. Apply path sets params.pick (HTTP injects
# pick=1 on mode=apply) so the chosen candidate is in positions.
apply_pick = knobs.get("pick")
if apply_pick is not None:
op = apply_orbit_pick(st0, sweep, pick=int(apply_pick or 1))
st = normalize_origin(op.state, base_params).state
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"sweep": sweep,
"pick": int(apply_pick or 1),
},
)
fin = score_state(st0)
return _pack_result(
st0,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": {
"node_id": sweep.get("node_id"),
"crossings_before": sweep.get("crossings_before"),
"candidates": sweep.get("candidates"),
"sampled": sweep.get("sampled"),
"improving_n": sweep.get("improving_n"),
},
"note": (
f"orbit_sweep top3 improving={sweep.get('improving_n')} "
f"sampled={sweep.get('sampled')}"
),
"sweep": sweep,
"hint": sweep.get("hint"),
},
)
if action_key == "clear_edge_hits":
knobs = clear_edge_params_from_overrides(params)
preserve = bool(knobs.get("preserve_axis"))
op = clear_edge_hits(
st0,
base_params,
top_n=int(knobs.get("top_n") or 12),
thr=float(knobs.get("thr") or 40.0),
margin=float(knobs.get("margin") or 20.0),
max_moves=int(knobs.get("max_moves") or 24),
preserve_axis=preserve,
pitch=knobs.get("pitch"),
side=knobs.get("side"),
rounds=int(knobs.get("rounds") or (6 if preserve else 1)),
frozen_ids=knobs.get("frozen_ids") or set(),
max_eject_degree=int(knobs.get("max_eject_degree") or 5),
)
st = normalize_origin(op.state, base_params).state
from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings as _cx
x0 = _cx(st.positions, st.links)
st2, fix_meta = _ensure_zero_overlap(st, base_params)
x1 = _cx(st2.positions, st2.links)
if x1 <= x0 + 2:
st = st2
else:
fix_meta = {"ran": False, "reason": "overlap_fix_would_raise_crossings"}
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get("clear_edge_hits"),
"ensure": fix_meta,
},
)
if action_key == "compact_bbox":
knobs = compact_bbox_params_from_overrides(params)
op = compact_bbox(
st0,
base_params,
frozen_ids=knobs.get("frozen_ids") or set(),
portal_ids=knobs.get("portal_ids") or [],
min_scale=float(knobs.get("min_scale") or 0.72),
step=float(knobs.get("step") or 0.03),
max_clearance_slack=int(knobs.get("max_clearance_slack") or 80),
outlier_only=bool(knobs.get("outlier_only", True)),
)
st = normalize_origin(op.state, base_params).state
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={"op": op.params, "note": op.note, "meta": st.meta.get("compact_bbox")},
)
if action_key == "pull_far_chains":
knobs = pull_far_chains_params_from_overrides(params)
op = pull_far_chains(
st0,
base_params,
frozen_ids=knobs.get("frozen_ids") or set(),
portal_ids=knobs.get("portal_ids") or [],
max_chains=int(knobs.get("max_chains") or 16),
min_tip_radius=float(knobs.get("min_tip_radius") or 1800.0),
scales=knobs.get("scales") or (0.92, 0.88, 0.84, 0.80, 0.75),
max_clearance_slack=int(knobs.get("max_clearance_slack") or 40),
pull_isolates=bool(knobs.get("pull_isolates", True)),
)
st = normalize_origin(op.state, base_params).state
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get("pull_far_chains"),
},
)
if action_key == "level_bands":
knobs = level_bands_params_from_overrides(params)
op = apply_level_bands(st0, base_params, **knobs)
# Soft unstick only — hard crush fights band geometry and inflates crossings.
st = op.state
ov = overlapping_nodes(st)
fix_meta: dict[str, Any] = {"ran": False, "overlaps_before": len(ov)}
if ov:
st2 = fix_overlaps_local(st, base_params).state
st = st2
fix_meta = {
"ran": True,
"overlaps_before": len(ov),
"overlaps_after": len(overlapping_nodes(st)),
"mode": "local_only",
}
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get("level_bands"),
"ensure": fix_meta,
},
)
if action_key == "polish_crossings":
knobs = press_params_from_overrides(params)
# Omit knobs so polish can auto-scale budgets on large E (MCP timeout).
# Internal polish still calls straighten / press_hot_edges /
# press_crossers / untangle.
op = polish_crossings(
st0,
base_params,
portal_ids=knobs.get("portal_ids"),
straighten=knobs.get("straighten"),
preserve_dual_eye=knobs.get("preserve_dual_eye"),
max_degree=int(knobs.get("max_degree") or 9),
untangle_rounds=knobs.get("untangle_rounds"),
top_n=knobs.get("top_n"),
max_moves=knobs.get("max_moves"),
max_sweeps=knobs.get("max_sweeps"),
)
st = normalize_origin(op.state, base_params).state
# Overlap crush must not erase crossing gains.
from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings as _cx
x0 = _cx(st.positions, st.links)
st2, fix_meta = _ensure_zero_overlap(st, base_params)
x1 = _cx(st2.positions, st2.links)
if x1 <= x0 + 2:
st = st2
else:
fix_meta = {
**fix_meta,
"reverted": True,
"reason": "overlap_fix_raised_crossings",
}
fin = score_state(st)
return _pack_result(
st,
fin,
action=action_key,
recipe=None,
recipe_id=None,
preset=preset,
params=base_params,
tune=False,
tried=None,
local={
"op": op.params,
"note": op.note,
"meta": st.meta.get(action_key),
"ensure": fix_meta,
},
)
# --- full layout recipe ---
recipe_id = resolve_recipe(recipe)
tried: list[dict[str, Any]] = []
best_st = None
best_fin: dict[str, Any] | None = None
best_params = base_params
combos = _tune_grid(base_params) if tune else [base_params]
for i, p in enumerate(combos):
st, _trace, fin = run_recipe(st0, recipe_id, p)
st, fix_meta = _ensure_zero_overlap(st, p)
fin = score_state(st)
row = {
"i": i,
"total": (fin.get("score") or {}).get("total"),
"overlaps": fin.get("footprint_overlap_pairs"),
"crossings": fin.get("edge_crossings"),
"nn_p50": fin.get("nn_p50"),
"util": fin.get("space_utilization"),
"fix": fix_meta,
"headline": ((fin.get("report") or {}).get("verdict") or {}).get("headline"),
}
tried.append(row)
if best_fin is None or _rank_key(fin) < _rank_key(best_fin):
best_st, best_fin, best_params = st, fin, p
assert best_st is not None and best_fin is not None
return _pack_result(
best_st,
best_fin,
action=action_key,
recipe=recipe if recipe in RECIPE_ALIASES else recipe_id,
recipe_id=recipe_id,
preset=preset,
params=best_params,
tune=bool(tune),
tried=tried if tune else None,
local={"auto_fix_overlaps": True},
)
def list_layout_catalog() -> dict[str, Any]:
from netx_api.mcp_remote.topology import NETX_MCP_REV, __version__
return {
"version": __version__,
"rev": NETX_MCP_REV,
"actions": {
"layout": "全图配方(骨架);块内 pack,结束时局部解叠",
"fix_overlaps": "只拉开当前重叠点(+1 跳邻居),不重排全图",
"straighten_channels": (
"拉直 deg≤2 通道(弦/横/纵);仅全局交叉下降才接受该通道"
"(params: step/min_len)"
),
"layout_dual_unit": (
"双门户单元美化(多走廊→平行 H/V 道;链→拉直);"
"默认允许残留交叉;params.require_zero_cross=true 时要求为 0(params: unit_id)"
),
"untangle": (
"贪心挪低度数点降交叉;默认 protect_rigid=portals "
"只冻共享门户,走廊/触手可动(all=全冻,off=不冻);"
"可用 focus_ids / source_view_ids"
),
"polish_crossings": (
"一键压交叉:straighten→press_hot_edges→press_crossers→"
"untangle(portals);优先用 source_view_ids 冻门户 "
"(勿写临时 py)"
),
"clear_edge_hits": (
"把贴在非关联边上的网元沿垂直方向弹开(直角偏好 H/V);"
"门控:不增交叉、不增重叠(preserve_axis 亦不放宽交叉)。"
"眼 sink 须 portal_ids;params: top_n/thr/margin/max_moves/max_eject_degree"
),
"compact_bbox": (
"眼图安全收 bbox:相对门户中心;默认 farthest-K(outlier_only);"
"门控:交叉不升、overlaps=0、贴边不可大幅恶化。"
"params: portal_ids/min_scale/step"
),
"pull_far_chains": (
"眼图安全收远场:deg≤2 走廊/孤立点/远叶相对门户中点缩放;"
"门控:交叉不升、overlaps=0、贴边松弛有限。"
"params: portal_ids/max_chains/min_tip_radius/scales"
),
"align_reference": (
"【非主路径】仅同网同成员画布 A/B 调试:"
"把参考画布几何映射到当前画布(共享 fabric_node_id)。"
"日常无范本、跨网人工图 → 禁止当交付手段。"
"params: reference_view_id|source_view_id, portal_ids, mode=similarity|adopt"
),
"level_bands": (
"按 fabric level/layer 水平分层(external→core→agg→access);"
"params: y0/band_gap/preserve_x/pitch;分层场景先于 polish"
),
"orbit_sweep": (
"压交叉:以网元为圆心不定长扫角;"
"preview+node_id / apply+pick;"
"until_limit=true 单点循环到 stall(默认冻 portals、objective=crossing|total);"
"round=true 一批 top_n 自动 pick#1(眼 sink 禁用);"
"单点/round 默认 protect_rigid=off;bundle 默认开"
),
"job_status": (
"轮询后台 job:params.job_id;返回 progress.phase/pct、elapsed_ms、"
"heartbeat_age_ms、stale(软警告,不改状态)"
),
"job_cancel": (
"协作式取消后台 job:params.job_id;下一检查点退出;"
"若已 PATCH 则保留写入并回报 applied"
),
"move_nodes": (
"成员迁移(双向):params.fabric_node_ids 从 source_view_id→view_id;"
"默认 remove_from_source;对调两 view 即回迁;"
"copy_positions|park;mode=preview|apply"
),
},
"recipes": {
"rings": "环+链花瓣;core_bar 时梁优先(多 CN 不塞进 AN 列)",
"corridor": "Tutte 走廊骨架,交叉常更少,易偏空——稳基线",
"compact": "走廊 + 分块 pack(禁止全局压扁)——稳基线",
"unstick": "corridor 后再强解重叠",
},
"presets": {
"loose": "偏疏、交叉友好",
"balanced": "默认折中",
"dense": "偏紧、抬 util",
},
"modes": {
"preview": "只算分+坐标,不写库",
"apply": "PATCH 到 view_id(有残留重叠则拒绝落笔)",
},
"workflow": (
"主路径:analyze(structure) → sinkTopologyDualUnits(max_units=1) → "
"suggestSinkHubs/move_nodes(park) → "
"orbit_sweep(until_limit crossing→total) → "
"clear_edge_hits → pull_far_chains → compact_bbox → 手拖。"
"默认无范本;align_reference 仅同网调试,禁止当跨网交付。"
"眼 sink 禁 polish/fix_overlaps/untangle/round。"
),
"eye_polish_plateau": (
"算法到头:overlaps=0 + until_limit stall + "
"pull/compact/clear moved≈0 → 手拖或改初布;勿指望金标对齐。"
),
}