IGXED5

The ACP surface (this task's focus) is a pleasure to watch on the wire, and the raw output captures prove it. Cleanest split of channels in the cohort: stdout carries only the answer (out1 = A1b9ln1h2m, a bare line; --output-format json emits exactly one object), while every progress and diagnostic line goes to stderr in a tight, consistent vocabulary — [mcp] fake: 2 tools, 1 prompt, [turn 1] calling model, [tool] read -> ok (14 chars), [acp] session … in <cwd> (cc-1t2j2ahd/stderr, cc-87exmste/stderr). The task's contract is met and visible: a session/new mcpServers entry is started and greeted exactly like a .mcp.json one (mcp.log shows initialize, notifications/initialized, tools/list in order), its env is set (env=E87exmste logged by the server harness, proving the {name,value} list was applied), and its tools are advertised to the model as mcp__fake__echo/mcp__fake__fail with a system-prompt section a person can parse at a glance (# MCP servers … - fake: tools … commands /mcp__fake__greet … resources are read with @fake:<uri>), so tool names, prompts and resource mentions are all guessable without reading code. Errors behave like a grown-up tool: a failing MCP tool is an error result to the model, not a crash (mcp__fake__missing -> error (22 chars) and the loop continues, req.3), a server that won't start is reported and skipped via mcp_server_failed, an unknown model prints ERROR: unknown provider 'ghost' in model 'ghost/xo9rzuq2l' (declared: a) with no stack trace, exit codes tell the truth (0/1/2/130), and serve_acp redirects stdout to stderr so a stray print can never corrupt the protocol stream. Shutdown closes each session's servers with the agent (mcp.log ends eof). Deductions: the interactive TUI — prompt, status line, keys — was never captured in any probe, so that half of the experience is unproven; AGENTS.md still documents only part one (no word on --acp or client-supplied servers beyond docstrings and --help), and no completion note was delivered. What was delivered, though, is consistently legible, honest and quiet — a tool that explains itself at every failure and never lies about where output went.

Governance splits sharply. Strengths: zero third-party dependencies (pure stdlib — nothing to pin, nothing gratuitous), documented run commands that mostly match reality, and genuinely strong decision documentation in acp.py's module and function docstrings (wire protocol, threading model, 600s client timeout with rationale, Connection's write-serialisation and reply-matching). Weaknesses: (1) the task commit b0e8ef6 contains no source changes at all — only .ololo harness artifacts — because the whole editor-files feature (client_file_tools, ClientCapabilities.readTextFile/writeTextFile) was already identical in the earlier 'Initialize' commit dc2a3cc; the per-task feat commits are empty of code, so the history tells the story of test runs, not the work. (2) AGENTS.md declares 'test: python3 -m unittest discover -s tests -v' but tests/ holds only the fake_mcp_server.py fixture — zero tests run; the layout section is stale (names hcagent/tools.py, never mentions --acp). (3) No formatter, linter, or type-checker is configured anywhere, despite # noqa: BLE001 comments implying uncommitted local ruff use. The unexplained edit-fallback (disk read when the editor can't read) is the one decision a successor must guess. Decisions written down: yes, in code; conventions enforced: no; reproducible: yes; honest history: no.

Judged from the tool's own captured output: the committed harness run for this task (.ololo/tmp/cc-5cwk69eq/{out,stderr,model.sh} + ws/.agent/sessions/a3b096083c96419a81930265e2a3bc5a.json), earlier run captures (cc-bqttd2yx, cc-65tn2o7w, cc-lylwxeue, cc-7rji1tqk), and the ACP/CLI/permission sources those runs exercised (hcagent/acp.py, cli.py, permissions.py, loop.py).

CLARITY (9.5). Stream discipline is textbook. In ACP mode the protocol transcript (.ololo/tmp/cc-5cwk69eq/out) is one JSON object per line, nothing else: pending tool_call with title/kind/rawInput, then the session/request_permission request carrying the toolCall and the two options (allow/allow_once, reject/reject_once), then in_progress, then completed/failed with content and rawOutput. An editor can tell at a glance what is happening and why the turn is blocked. Logs stay on stderr ('[acp] client ololo-check 1', '[turn 1] calling model', '[tool] bash: touch madeA5cwk69eq.txt') and never touch the wire. Headless mode is equally clean: AGENTS.md promises 'stdout carries only the answer' and cli.py does exactly that. The denial is legible inside the failed tool_call_update ('permission denied: denied by the editor', is_error true) with the turn continuing to a normal end_turn — matching the task spec exactly.

ERRORS (9.0). Failures are results, not crashes. A rejected permission becomes a failed tool result ('permission denied: denied by the editor') and the loop moves on (call-2 in cc-5cwk69eq); a cancelled outcome denies politely ('the turn was cancelled while asking permission'); an editor that never answers hits a 600 s timeout and yields a denial, not a hang or an exception on the wire (ClientPolicy.ask in acp.py). A dying model is retried with visible, specific progress ('[turn 1] model failed on attempt 1: model command exited 1: ; retrying', cc-lylwxeue) before 'ERROR: model failed after 3 attempts' and exit 1. A bad flag prints argparse's message plus usage on stderr with exit 2 (cc-bqttd2yx). Stack traces never reach the user: internal bugs become JSON-RPC INTERNAL_ERROR with the traceback logged to stderr. One nit: on session/cancel during a pending permission, the denial text says 'the editor did not answer...' when 'cancelled' would be truer. Also, an older snapshot (cc-7rji1tqk) shows a raw 'ModuleNotFoundError: No module named hcagent.cli' traceback when launched from another cwd; the current launcher (agent.py inserting its own dir on sys.path) fixes this, and no current capture reproduces it.

ERGONOMICS (9.0). AGENTS.md is a genuinely useful one-page doc (usage line, flags, provider setup examples, layout map). Flags are guessable: -C, -p, --yes, --model provider/model, --max-turns, --output-format json, --acp, --resume/--continue, --version. Sane defaults (read/glob/grep run, bash/write/edit ask) and, best of all, a denial that tells you what to do next: 'bash(touch ...) is not allowed by default; add "bash(touch *)" to permissions.allow in .agent/settings.json, or run with --yes' (permissions.py suggest_rule) — the exact rule to paste. The TUI documents its own keys in /help ('y/n answer a permission question', Esc interrupts) and its status line states 'waiting for y/n'. Caveat: the interactive TUI experience is evidenced here by the help text and code, not a terminal recording — no recording probe was deliverable this session; the graded ACP surface, however, is fully evidenced by verbatim transcripts. Overall a pleasant, honest tool: it says what it is doing, asks cleanly, and recovers without drama.

The tool-call reporting feature is implemented cleanly and verifiably works: the loop gained optional tool_call_start/tool_call_end events with pass-through defaults (loop.py _run_tools, Events), and acp.py's SessionEvents translates them into session/update notifications — tool_call with status pending, toolCallId minted per-session under a lock (call-1, call-2...), title from describe_tool (the raw command for bash, kind 'execute' via the named TOOL_KINDS table), an in_progress update fired from ClientPolicy.check only after permission clears, and a final tool_call_update with completed/failed plus both a text content block and rawOutput. The recorded transcripts confirm the exact wire sequence for the happy path (cc-1m5f5au8/out) and for denied and cancelled calls (cc-5cwk69eq, cc-az1x9vbe). The genuinely tricky part — model tool_use ids that repeat across turns and across subagent loops — is handled by a shared, lock-guarded id counter and call map with an explanatory docstring, and entries are popped on completion so nothing leaks. Code quality is high: precise naming, named constants (PERMISSION_OPTIONS, error codes, timeouts), short functions with flat nesting, defensive handling at the real boundaries (ClientError -> denial decision, type-checked replies, swallowed write failures with a comment), and no meaningful duplication; jsonrpc.py is the MCP transport, not dead code. Nits: REJECT_OPTION is an unused constant, and the shared-root initialization in SessionEvents.init is dense for a newcomer though documented. Note the task commit itself adds only harness artifacts; the source was committed earlier in the session and is identical at the task commit.

No tests were written for this task or anywhere in the session. tests/ holds only a fixture (tests/fake_mcp_server.py, a scripted MCP server with no assertions); there is no test_*.py at the task commit c4c66ec or at the session-start snapshot f317525, and the task commit's diff adds only .ololo check transcripts. The documented 'test: python3 -m unittest discover -s tests -v' therefore discovers zero tests, despite AGENTS.md claiming a unittest suite. None of the cancel task's scenarios are covered by a committed test: kill of a running tool (cancel.py watch/kill_process), no further model call after cancel (loop check()), the pending session/prompt answered {stopReason:'cancelled'} promptly, cancel between model calls, and a later prompt starting a fresh turn (reset()). The only verification is external — the ololo harness's own transcript (.ololo/tmp/cc-az1x9vbe/out, session 0ac93da4..., matching the prior-task answer) plus ad-hoc scratchpad runs — which is manual/one-shot evidence, not a repeatable suite; verification is manual-only, capping at 3.0, and with zero assertions and zero test modules the criterion sits near the floor. The cancellation logic itself (context-var plumbing, process-group kill, race where a proc registers after cancel) is exactly the kind of code that needs unit and integration tests, and it ships with none. A regression here would be caught by nothing in the repo.

The cancel path is genuinely event-driven, not polling: Cancellation.cancel() sets a threading.Event and immediately SIGKILLs every registered process group (os.killpg with start_new_session=True, so sh -c subtrees die too — the probe's sleeper=gone confirms). No lock is held across I/O — watching() takes _lock only to add/discard a Popen — and the reader thread answers session/cancel inline (acp.py: cancel()), so a queued notification is never serialized behind the running turn. Latency sources are bounded and few: the loop checks cancel.check() at the top of every model attempt and after every tool; a killed bash tool reports is_cancelled() without waiting on the 120s timeout; in-flight permission/fs requests are woken by conn.abandon(session_id) rather than the 600s CLIENT_TIMEOUT_SECONDS. Pending prompts are answered stopReason: "cancelled" from the turn thread on Interrupted, and the recorded transcript shows tool failed → cancelled answer → fresh second turn, exactly as asked.

Deductions, each pointable: (1) Cancel is unbounded for the HTTP providers. post_json (hcagent/providers/http.py) is a plain urllib.request.urlopen with no cancellation wiring, and AnthropicProvider/OpenAIProvider register nothing with watch() — the Cancellation machinery only kills subprocesses. Cancel during an in-flight Anthropic/OpenAI call surfaces only at the next cancel.check() between attempts, i.e. up to the 600s provider timeout later. The graded probe used a command provider where killpg makes it instant, so the observed behavior passed, but two of three providers miss the "answered promptly" contract. (2) A small race: _run_prompt sets session.turn and then calls cancel.reset() outside any protocol with the reader thread's cancel(); a cancel landing in that window sees busy false (or is wiped by reset()) and the turn runs to completion. Milliseconds wide, but this task is precisely about that edge. (3) The 1s time.sleep(retry_delay) between model attempts is not interruptible — a bounded (≤1s) delay in the cancelled answer. (4) No measurement anywhere in the repo: tests/ contains only fake_mcp_server.py; no timing harness, no cancel-latency test, no before/after note. Promptness is attested solely by the grader's single probe. Memory discipline is clean — proc set, call-id map, and pending-request table all popped in finally — and concurrent sessions prompt on independent threads, so no cross-client serialization.

No automated tests exist. The repo's only tests/ content is tests/fake_mcp_server.py, a scripted-MCP helper with no test cases, so the AGENTS.md 'test:' command (unittest discover) collects nothing. The task's behavior was verified solely by a manual probe: .ololo/tmp/cc-5cwk69eq/{model.sh,out} records a scripted-model end-to-end ACP run showing session/request_permission with allow/reject options, allow -> tool completed (madeA file present), reject -> failed with 'permission denied: denied by the editor' and is_error=true. That probe covers both branches of the task's scenario well and matches the harness transcript, but it is a one-off artifact with no assertions and cannot be re-run to catch regressions. Nothing exercises edge cases the task implies: a cancelled outcome, a client that never answers (the 600s CLIENT_TIMEOUT path), denial text content, or the options payload shape — all only in the implementation (ClientPolicy.ask in hcagent/acp.py), untested. Per the rubric, manual-only verification caps this at 3.0; the probe's genuine end-to-end breadth justifies the top of that band rather than lower. A minimal fix would be pipe-driven integration tests over serve_acp asserting the request_permission transcript, plus unit tests of the decision mapping (selected/allow -> run, selected/reject and cancelled -> permission denied).

Well-proportioned ports-and-adapters architecture. The core loop (hcagent/loop.py:AgentLoop) is decoupled from all presentation through an Events port; the task's concern — ACP tool-call reporting — is implemented as an adapter (hcagent/acp.py:SessionEvents) that translates loop events into session/update notifications, with the ACP view (title/kind/locations) isolated in the pure describe_tool() helper and the in_progress transition owned by ClientPolicy.check(). The same Events port feeds three frontends (stderr CLI, ACP, TUI), which proves the boundary is real. Dependency direction is clean: loop depends on abstractions (Events, Policy, Provider), frontends depend on the core, no cycles; threading model (per-prompt threads, serialized writes, reply matching in Connection) is explicit and documented. Units are small and single-purpose, dependencies are injectable, and AGENTS.md sketches the layout. Deductions: acp.py is a ~700-line multi-role file (wire + event translation + client file tools + session registry) with labeled but broad sections; two separate JSON-RPC implementations (jsonrpc.StdioPeer vs acp.Connection) duplicate pending-request bookkeeping; acp reaches back into the loop via the current_tool_use() contextvar; StderrEvents, a reusable base class, lives in cli.py; and AGENTS.md drifts from the tree (hcagent/tools.py vs a tools/ package; claims a tests/ suite but only tests/fake_mcp_server.py is committed).

The ACP layer (hcagent/acp.py, present at task commit 4060224, written in commit dc2a3cc) is exceptionally clean, disciplined code. Cleanliness: every constant that matters is named (PROTOCOL_VERSION, the JSON-RPC error-code block, CLIENT_TIMEOUT_SECONDS, SHUTDOWN_GRACE_SECONDS, PERMISSION_OPTIONS, TOOL_KINDS map), so there are no magic values in the protocol logic. There is no copy-paste: Connection, SessionEvents, ClientPolicy, client_file_tools, AcpSession and AcpServer each own one concern, and _with_run exists precisely so the client-backed read/write/edit tools don't repeat Tool-construction. Naming says what things are (prompt_text, describe_tool, mcp_configs, AcpSession.busy). Only two tiny dead symbols: REJECT_OPTION is bound alongside ALLOW_OPTION but never used, and ToolRegistry is imported but unused. Maintainability: a newcomer could follow this safely — nesting stays shallow, the longest functions (new_session, _run_prompt, ClientPolicy.ask, run_edit) are well under a screen, module and method docstrings explain both the what and the why (threading model in the module docstring, 'a stray print anywhere must never corrupt the protocol stream', why tool call ids are remapped to call-1/call-2), and error handling sits exactly at the real boundaries: malformed JSON → parse error reply, unknown session / bad cwd / busy session → REQUEST_FAILED or INVALID_PARAMS, ClientError on editor replies → denial or ToolError, and a catch-all in handle/_run_prompt that logs a traceback and answers INTERNAL_ERROR instead of killing the server. The task behavior itself is correct and verified by the recorded probe (initialized/session/prompt/end_turn, cwd_seen, prompt_seen, calls=1, exit_on_eof — 20/20): session/new validates and normalizes cwd, threads it through Settings, Session, SessionEvents, MCP and the system prompt's 'Working directory:' line, and session/prompt answers {stopReason: end_turn}. Deductions keeping this from 10: the two dead symbols above, a few ~650-line single module (banner-comment sectioning mitigates it), and the committed tests/ directory contains only a fake MCP server with no unit tests for the ACP or loop logic, so safety of future changes rests entirely on manual probing. No duplication concerns worth a probe — reading the code shows none.

The task's hot path — model text_delta -> session/update — is implemented with the right algorithmic shape and no cost traps. CommandProvider._read_lines reads the model process stdout line-by-line (for raw in stdout) and hands each text_delta to on_text the moment it arrives, so a delta never waits for the reply to finish; parse_reply then needs only the last line, with the full line list bounded by reply size. SessionEvents.text_delta -> chunk -> Connection.notify is O(1) per delta: no re-scan, no re-parse, no accumulation. I/O discipline is sound: one write+flush per notification (exactly what a line-oriented streaming protocol requires — chunks must be visible immediately, so batching would trade latency for syscalls against the spec), stderr drained on its own thread so the pipe can't deadlock, feeder/drainer/timer all bounded. Concurrency is the strong part: session/prompt runs on its own thread per AcpServer.handle, so a streaming turn on one session doesn't block another; Connection._write_lock is held only across the single write+flush, never across the wait in request() (the _Pending event pattern releases the lock before waiting), so a client answering a permission request while another session streams is not serialized. The probe (cc-vm3hbuod) confirms the required order: two agent_message_chunk lines before the id-103 response, matching the non-streaming fallback in AnthropicProvider/OpenAIProvider (single on_text(reply.text) when no streaming) and the compaction guard in SessionEvents (summary deltas suppressed, _compacting reset in model_end). Deductions: no measurement evidence anywhere — the probe is a functional line-order check, not a timing run, and there is no benchmark or recorded before/after to back the per-delta-unbatched-write and per-delta json.dumps choices; those look right for the job but are unmeasured. Lines list in _read_lines and stderr_lines accumulate for the whole model call (fine at reply scale, unbounded in principle). Lock-free dict growth: _calls is popped on tool_call_end so no leak. Solid, honest fit between cost and job; missing only evidence.

The --acp feature is added as a single new module, hcagent/acp.py (657 lines), plus minimal, backward-compatible touches on cli.py and loop.py — proportionate to the task.

Strengths: Internal structure is cleanly layered with section banners: Connection is a pure wire layer (thread-safe line framing, request/reply matching via _Pending, abandon(tag) for cancellation); AcpServer owns dispatch and lifecycle; SessionEvents is an adapter translating loop Events into session/update notifications; ClientPolicy wraps the existing Policy as a decorator that defers unsettled calls to session/request_permission; client_file_tools manufactures standard Tool objects so the loop cannot tell them from local tools. Dependencies flow one way from the protocol adapter into the core (loop, providers, sessions, settings, permissions, mcp); the loop itself stays protocol-agnostic — the new tool_call_start/tool_call_end hooks are generic front-end extension points with default implementations (loop.py:66-73), and current_tool_use() is documented generic plumbing. serve_acp accepts injectable stdin/stdout/stderr and defensively redirects sys.stdout to stderr so no stray print can corrupt the stream (acp.py:649-657), matching the 'nothing else on stdout' requirement. The prior-result captures (.ololo/tmp/cc-*/out) confirm the wired behavior: initialize reply with protocolVersion 1 and agentCapabilities, session/new, streaming chunks, and exit on EOF.

Deductions: (1) acp.py imports StderrEvents from cli.py while cli.main lazily imports serve_acp from acp (cli.py:178-180) — a bidirectional module relationship only defused by the deferred import; the base events class belongs in its own module. (2) Notable duplication with jsonrpc.py: a second _Pending class, METHOD_NOT_FOUND = -32601, and near-identical pending-request locking machinery, rather than a shared wire module (the two directions differ enough to justify divergence, but the constants and bookkeeping are copy-class-level twins). (3) At 657 lines acp.py bundles transport + events + policy + tools + server; each unit is single-purpose and clearly delimited, but the transport and policy pieces could stand alone. (4) No tests for the new module (tests/ holds only fake_mcp_server.py), though the design is testable via stream injection. Overall: a well-proportioned, clearly bounded protocol layer with two identifiable coupling blemishes.