feat(rag): agent document tools — list/read tools with env-tuned budgets, SSE tool events + "calling tool" UI

Grounded chat turns now run the agent loop (app/rag/agent.py) instead
of a bare chat_stream: while the per-turn budgets last
(BOR_AGENT_LIST_CALLS / BOR_AGENT_READ_CALLS, default 1 each) the model
gets list_documents (the indexed catalog, /api/docs order) and
read_document (full text, never truncated — A7-revised contract); once
both budgets are spent the tools key is dropped from the request and
the model must answer. Rejected calls (unknown tool, unknown/missing
path, document already in context, spent budget) consume no budget.
Budgets 0/0 make exactly one tools=None request — byte-identical to
the pre-phase path (budgets-as-kill-switch). Deflected turns keep the
direct chat_stream (A8 unchanged; the LOW prompt never carries the
<tools> section).

SSE contract gains {"type":"tool","name":...,"argument":
"source/path"|null} frames ahead of the answer deltas (PLAN §4
extension, owner permission 2026-08-26); done.sources, query_log.sources
and the per-turn log line (gains tool_calls=N) report the retrieval
docs + read docs, deduped. The UI shows a "calling tool"
button/label state and one visible .tool-call line per call above the
answer; the lines persist with the chat record and re-render on
reload. chat_stream passes tools through and accumulates streaming
tool_calls deltas into ToolCallPiece (tools=None stays byte-identical).

E2E: deterministic mock tool flow ("use your tools" + <tools> marker:
list -> read first catalog line -> quoted answer) plus the story suite
(marker flow, reload re-render, plain/deflected no-tool regressions).
Docs: .env.example + README (the two tools, the budgets, the SSE tool
frame, the "calling tool" UI state).

probe: turbo tool_calls=supported 2026-08-26 (uv run python -m
scripts.llm_probe --tools — non-streaming + streaming
finish_reason=tool_calls, indexed delta.tool_calls partials)

tests/unit/test_sse_events.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 import json
 
 from app.api.chat import sse_event
-from app.schemas import ChatErrorEvent, ChatThinkingEvent
+from app.schemas import ChatErrorEvent, ChatThinkingEvent, ChatToolEvent
 
 
 def _payload(frame: str) -> dict:
@@ -76,3 +76,27 @@ def test_thinking_event_shape_is_type_and_text_only() -> None:
     dumped = ChatThinkingEvent(text="hmm").model_dump()
     assert set(dumped.keys()) == {"type", "text"}
     assert dumped["type"] == "thinking"  # default — call sites never spell it out
+
+
+def test_tool_frame_serializes_exactly() -> None:
+    """Phase 37 (PLAN §4 extension): the ``tool`` frame is exactly
+    ``{type: "tool", name: str, argument: str | null}`` — one per
+    model-requested document tool call, streamed ahead of the ``delta``
+    frames of the answer."""
+    frame = sse_event(ChatToolEvent(name="read_document", argument="S/p.md").model_dump())
+    assert frame == 'data: {"type": "tool", "name": "read_document", "argument": "S/p.md"}\n\n'
+    assert _payload(frame) == {"type": "tool", "name": "read_document", "argument": "S/p.md"}
+
+
+def test_tool_frame_argument_is_null_for_parameterless_tools() -> None:
+    """``list_documents`` takes no parameters, so its frame's ``argument``
+    serializes as JSON null (the client renders the name alone)."""
+    dumped = ChatToolEvent(name="list_documents").model_dump()
+    assert dumped == {"type": "tool", "name": "list_documents", "argument": None}
+    assert _payload(sse_event(dumped))["argument"] is None
+
+
+def test_tool_event_shape_is_type_name_argument_only() -> None:
+    dumped = ChatToolEvent(name="read_document", argument="S/p.md").model_dump()
+    assert set(dumped.keys()) == {"type", "name", "argument"}
+    assert dumped["type"] == "tool"  # default — call sites never spell it out