feat(ledger): classify_shapes + list_shapes MCP tools; get_snippet carries the consumer map (#2789, milestone 294 step 3)
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The judgment write path. classify_shapes applies a batch of classifications
to a project's live ledger rows — all-or-nothing (#2709's lesson: the whole
batch is validated, write-ACL'd, and every snippet target proven readable
before any row is touched); rows match by exact (path, symbol), kind narrows,
and shapes no live row matches come back as 'unmatched' rather than errors.
variant/exempt REQUIRE the reason — the why is the record (note 2786) — and
'unclassified' deliberately withdraws a judgment back to the todo. The 'via'
channel is caller-restricted to agent|audit|import; hook and mechanical stay
server-internal so a caller can't launder judgment as machinery.

list_shapes is the todo query (status=unclassified) with composable filters:
path is exact-or-under like recorded locations, snippet_id reads a consumer
map, include_vanished reads history; paged with the true total.

get_snippet now attaches  and  — the structured consumer
map, filtered to projects the CALLER can read so a shared snippet never side-
channels another project's file layout; attached only when non-empty (#2483).

Integration tests pin the batch atomicity, ACL gates, filter composition, the
consumer map on the MCP pull, and the SET NULL companion: a judgment whose
snippet was purged rejoins the todo on the next sync.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
2026-08-19 19:55:47 -04:00
co-authored by Claude Fable 5
parent 9d92df2825
commit 942edd1eb5
6 changed files with 601 additions and 2 deletions
+3 -2
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@@ -5,8 +5,8 @@ to a FastMCP instance. `register_all(mcp)` is the single entry point called
from `mcp.server.build_mcp_server`.
"""
from scribe.mcp.tools import (
design_systems, milestones, notes, processes, projects, recent, repos, rulebooks, search, snippets,
systems, tags, tasks, trash,
design_systems, milestones, notes, processes, projects, recent, repos, rulebooks, search, shapes,
snippets, systems, tags, tasks, trash,
)
@@ -24,5 +24,6 @@ def register_all(mcp) -> None:
repos.register(mcp)
processes.register(mcp)
snippets.register(mcp)
shapes.register(mcp)
rulebooks.register(mcp)
trash.register(mcp)
+95
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@@ -0,0 +1,95 @@
"""Shape-ledger MCP tools — the classification write/read surface (#2789).
The accounting model (note 2786): the snippet library records CANON (small);
the ledger accounts for EVERY extracted shape (total). These tools are how
agents move shapes out of `unclassified` — the todo state — and how they read
what still needs judgment. The ledger rows themselves are fed by the coverage
refresh; these tools only ever judge what the sync has seen.
"""
from __future__ import annotations
from scribe.mcp._context import current_user_id
from scribe.services import shape_ledger as shape_ledger_svc
async def classify_shapes(
project_id: int, classifications: list[dict], via: str = "agent"
) -> dict:
"""Record judgments for a project's code shapes — in batch, as rows.
EVERY shape in a bound repo should end up classified (note 2786):
- `instance` of snippet N — it conforms to recorded canon (family-level
canon in another project counts; that fully accounts for the shape).
- `variant` of snippet N — a deliberate, named departure. `reason`
(the why) is REQUIRED; it is the record.
- `exempt` — judged genuinely one-off. `reason` REQUIRED.
- `canonical` of snippet N — this row IS the snippet's reference
(rarely set by hand; the coverage sync stamps these mechanically).
- `unclassified` — withdraw a judgment; the shape rejoins the todo.
Consumer maps belong HERE, not in prose: when an audit enumerates call
sites of a canonical helper, each call site's defining shape is an
`instance` row — a sentence in a verification detail cannot be sorted,
queried, or diffed.
Args:
project_id: The project whose ledger is being judged.
classifications: Objects of {path, symbol, status, kind?, snippet_id?,
reason?}. path+symbol name the shape exactly as list_shapes shows
it; kind ("sym"/"css") narrows when one file defines both.
snippet_id is required for canonical/instance/variant; reason is
required for variant/exempt.
via: Who is judging — "agent" (default), "audit" (a sweep), or
"import" (carrying maps recorded elsewhere).
All-or-nothing: a structural error, a missing snippet target, or no write
access applies NOTHING. Returns {"classified": N, "unmatched": [...]} —
unmatched names shapes no live ledger row matches (the tree may have
moved since you listed; re-run the project's coverage refresh to re-sync).
"""
uid = current_user_id()
return await shape_ledger_svc.classify_shapes(
uid, project_id, classifications, via=via
)
async def list_shapes(
project_id: int,
status: str = "",
path: str = "",
snippet_id: int = 0,
include_vanished: bool = False,
limit: int = 100,
offset: int = 0,
) -> dict:
"""Read a project's shape ledger — `status="unclassified"` IS the todo.
Every extracted definition in the project's bound repos has a row here
(fed by the coverage refresh). Filters compose:
Args:
status: canonical | instance | variant | exempt | unclassified.
path: exact file, or a directory — matches everything beneath it
(the coverage line's "largest" dirs go straight in here).
snippet_id: rows classified against this snippet — a consumer map.
include_vanished: include shapes no longer in the tree (history).
limit/offset: page through big ledgers (limit caps at 500).
Returns {"shapes": [...], "total": N} — total counts every match, not
just this page. Classify what you can judge with classify_shapes; a
repeating shape with NO recorded canon is a derive-one-first moment
(consolidate onto a reference, create_snippet it, then classify the
rest against it), never N loose classifications.
"""
uid = current_user_id()
rows, total = await shape_ledger_svc.list_project_shapes(
uid, project_id,
status=status, path=path, snippet_id=snippet_id,
include_vanished=include_vanished, limit=limit, offset=offset,
)
return {"shapes": [r.to_dict() for r in rows], "total": total}
def register(mcp) -> None:
for fn in (classify_shapes, list_shapes):
mcp.tool(name=fn.__name__)(fn)
+16
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@@ -207,6 +207,12 @@ async def get_snippet(snippet_id: int) -> dict:
the source moved on — trust the location over the cached body and
consider verify_snippet after you look.
When the shape ledger has judgments against this snippet, the response
carries `instances` (shapes classified as conforming to it — the
structured consumer map) and/or `variants` (named departures, each with
its why). Consult them before changing the snippet's contract: they are
the call sites your change lands on (classify_shapes maintains them).
If the record belongs to someone else it carries `shared: true` with the
`owner` and your `permission`. Read that as ONE PERSON'S SUGGESTION, not as
established practice here: judge it on its merits, say whose it is when you
@@ -229,6 +235,16 @@ async def get_snippet(snippet_id: int) -> dict:
await systems_tools.attach_systems(
uid, note.user_id, data, note.id, note.project_id
)
# The structured consumer map (#2789): ledger rows judged against this
# snippet. Attached only when non-empty (#2483) — and never for projects
# the caller can't read.
from scribe.services import shape_ledger as shape_ledger_svc
consumers = await shape_ledger_svc.snippet_consumers(uid, int(note.id))
if consumers["instances"]:
data["instances"] = consumers["instances"]
if consumers["variants"]:
data["variants"] = consumers["variants"]
return data
+234
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@@ -174,3 +174,237 @@ async def live_rows(project_id: int) -> list[CodeShape]:
)
).scalars().all()
)
# --- classification (#2789): the judgment write path --------------------------
# Statuses an explicit classification may set. All five: setting a row back to
# `unclassified` is how a judgment is deliberately withdrawn.
_SETTABLE = ("canonical", "instance", "variant", "exempt", "unclassified")
# Who may appear as the classifier on this path. `hook` and `mechanical` are
# server-internal feeds (steps 5-6) — a caller claiming them would launder a
# judgment as machinery.
_CALLER_VIAS = ("agent", "audit", "import")
def validate_classifications(items: list[dict]) -> str | None:
"""The structural error a classification batch would earn, or None.
Pure and checked BEFORE anything is touched: a batch either applies or
errors whole — the StrictArgs lesson (#2709), a caller must never learn
later that half a batch silently happened.
"""
if not items:
return "classifications is empty — nothing to apply"
for i, item in enumerate(items):
if not isinstance(item, dict):
return f"classifications[{i}] is not an object"
path = (item.get("path") or "").strip()
symbol = (item.get("symbol") or "").strip()
if not path or not symbol:
return f"classifications[{i}] needs both path and symbol"
status = item.get("status") or ""
if status not in _SETTABLE:
return (
f"classifications[{i}] has unknown status {status!r} "
f"(one of: {', '.join(_SETTABLE)})"
)
snippet_id = item.get("snippet_id") or 0
if status in _NEEDS_TARGET and not snippet_id:
return (
f"classifications[{i}]: status {status!r} needs snippet_id — "
"the snippet this shape is (or departs from)"
)
if status in ("variant", "exempt") and not (item.get("reason") or "").strip():
return (
f"classifications[{i}]: status {status!r} needs a reason — "
"the WHY is the record (note 2786)"
)
return None
async def classify_shapes(
user_id: int,
project_id: int,
classifications: list[dict],
*,
via: str = "agent",
) -> dict:
"""Apply a batch of judgments to a project's live ledger rows.
All-or-nothing on errors: the whole batch is validated (structure, write
access, every snippet target readable by the caller) before any row is
touched. Rows are matched by exact (path, symbol) — plus kind when the
item carries one — and a target no live row matches is reported in
``unmatched``, not an error: the tree may simply have moved since the
caller listed. Idempotent by construction.
"""
from scribe.services import access
from scribe.services import snippets as snippets_svc
if via not in _CALLER_VIAS:
raise ValueError(f"via must be one of: {', '.join(_CALLER_VIAS)}")
error = validate_classifications(classifications)
if error:
raise ValueError(error)
if not await access.can_write_project(user_id, project_id):
raise ValueError(f"project {project_id} not found or no write access")
# Snippet targets resolve through the caller's own read access — a
# family-canon snippet in another project counts (note 2786), a snippet
# the caller cannot read does not exist for them.
target_ids = {
int(item["snippet_id"])
for item in classifications
if item.get("status") in _NEEDS_TARGET
}
for sid in sorted(target_ids):
if await snippets_svc.get_snippet(user_id, sid) is None:
raise ValueError(f"snippet {sid} not found (or not readable)")
now = datetime.now(timezone.utc)
classified = 0
unmatched: list[dict] = []
async with async_session() as session:
rows = (
await session.execute(
select(CodeShape).where(
CodeShape.project_id == project_id,
CodeShape.vanished_at.is_(None),
)
)
).scalars().all()
by_key: dict[tuple[str, str], list[CodeShape]] = {}
for row in rows:
by_key.setdefault((row.path, row.symbol), []).append(row)
for item in classifications:
matches = by_key.get(
((item.get("path") or "").strip(), (item.get("symbol") or "").strip())
) or []
kind = (item.get("kind") or "").strip()
if kind:
matches = [r for r in matches if r.kind == kind]
if not matches:
unmatched.append({
"path": item.get("path"), "symbol": item.get("symbol"),
})
continue
status = item["status"]
for row in matches:
row.status = status
if status == "unclassified":
row.snippet_id = None
row.reason = None
row.classified_by = None
row.classified_at = None
else:
row.snippet_id = (
int(item["snippet_id"]) if status in _NEEDS_TARGET else None
)
row.reason = (item.get("reason") or "").strip() or None
row.classified_by = via
row.classified_at = now
classified += 1
await session.commit()
return {"classified": classified, "unmatched": unmatched}
async def list_project_shapes(
user_id: int,
project_id: int,
*,
status: str = "",
path: str = "",
snippet_id: int = 0,
include_vanished: bool = False,
limit: int = 100,
offset: int = 0,
) -> tuple[list[CodeShape], int]:
"""A filtered page of a project's ledger, with the unfiltered-match total.
([], 0) when the caller can't read the project — the same silence every
other project list gives. ``path`` matches the exact file or anything
beneath it, mirroring recorded-location semantics.
"""
from sqlalchemy import func, or_
from scribe.services import access
if not await access.can_read_project(user_id, project_id):
return [], 0
conds = [CodeShape.project_id == project_id]
if not include_vanished:
conds.append(CodeShape.vanished_at.is_(None))
if status:
conds.append(CodeShape.status == status)
if path:
clean = path.strip().strip("/")
conds.append(or_(
CodeShape.path == clean, CodeShape.path.like(clean + "/%")
))
if snippet_id:
conds.append(CodeShape.snippet_id == snippet_id)
async with async_session() as session:
total = (
await session.execute(
select(func.count()).select_from(CodeShape).where(*conds)
)
).scalar_one()
rows = (
await session.execute(
select(CodeShape).where(*conds)
.order_by(CodeShape.path, CodeShape.symbol, CodeShape.kind)
.limit(max(1, min(limit, 500))).offset(max(0, offset))
)
).scalars().all()
return list(rows), int(total)
def _consumer_dict(row: CodeShape) -> dict:
"""The compact shape a snippet's consumer map carries — enough to open
the file, none of the ledger bookkeeping."""
out = {
"project_id": row.project_id,
"repo": row.repo_key,
"path": row.path,
"symbol": row.symbol,
"kind": row.kind,
"classified_by": row.classified_by,
}
if row.reason:
out["reason"] = row.reason
return out
async def snippet_consumers(user_id: int, note_id: int) -> dict:
"""The structured consumer map for one snippet (#2789): its `instances`
(rows judged to conform) and `variants` (named departures, each carrying
its why). Rows are filtered to projects the CALLER can read — a shared
snippet must not become a side channel into someone else's project
layout. Empty lists mean "attach nothing" (#2483)."""
from scribe.services import access
async with async_session() as session:
rows = (
await session.execute(
select(CodeShape).where(
CodeShape.snippet_id == note_id,
CodeShape.status.in_(("instance", "variant")),
CodeShape.vanished_at.is_(None),
)
)
).scalars().all()
readable: dict[int, bool] = {}
out: dict[str, list[dict]] = {"instances": [], "variants": []}
for row in rows:
if row.project_id not in readable:
readable[row.project_id] = await access.can_read_project(
user_id, row.project_id
)
if not readable[row.project_id]:
continue
out["instances" if row.status == "instance" else "variants"].append(
_consumer_dict(row)
)
return out