Attachable Debug AI: Orchestrating gdb/lldb via MCP for Deterministic Bug Repro and Fixes

An attachable “debug AI” that can hook into live processes, drive breakpoints, inspect memory, and propose code fixes sounds futuristic—until you realize the ingredients already exist. gdb and lldb are deterministic, scriptable, and battle-tested. The Model Context Protocol (MCP) gives you a clean, standardized way to expose these capabilities to an AI agent, IDEs, and tooling without leaking your source code to external services. Combine that with modern record/replay (e.g., rr), containerized environments, and strict locality policies, and you have a pragmatic path to autonomous debugging that stays safe, auditable, and effective.

This article lays out a reference architecture, concrete APIs, and wiring patterns for building an attachable debug AI using gdb/lldb—focused on determinism, privacy, and developer experience.

Executive Summary

• Goal: Build an AI that can attach to a running process, set breakpoints, step, read memory/registers, pull backtraces, correlate that telemetry with local source, propose patches, and validate fixes—all while keeping code and logs local to the developer or organization.
• How: Wrap gdb/lldb control inside an MCP server that exposes a small, safe set of tools (attach, break, step, eval, memory-read, backtrace, rr replay, patch-propose, test-run). The AI calls these tools via MCP; IDEs can also integrate via MCP or bridge to DAP.
• Determinism: Prefer reproducible containers, rr-based time-travel debugging for Linux, controlled clocks, seeded RNG, and hermetic test harnesses. Avoid non-deterministic breakpoints in production unless you use non-stop mode or sampling/tracing.
• Safety: Attaching can pause processes; production requires guardrails (read-only modes, eBPF/uprobes, CPU quotas, on-call approval). Keep source/logs local; redact PII; enforce policy.
• Results: Deterministic bug reproduction, higher-confidence patches, lower MTTR, and institutionalized debugging knowledge.

Why an Attachable Debug AI Now?

• Tooling is ready: gdb’s MI interface and lldb’s SB API are scriptable and stable. rr enables time-travel debugging. eBPF/USDT tracing allows low-impact telemetry.
• Protocols are ready: MCP provides a standardized, structured way for LLMs and agents to call local tools with audited, typed inputs and outputs. It integrates cleanly with modern IDEs and orchestrators.
• Privacy and compliance demand local-first: Many orgs can’t ship source or production logs outside their network. An MCP-based local toolchain solves this.
• Determinism is achievable: Containers, pinned toolchains, hermetic builds, and rr make reproduction practical for a wide class of issues, including many concurrency bugs.

What Is MCP (Model Context Protocol)?

MCP is an open protocol for exposing tools and data sources to AI systems in a way that is structured, discoverable, and secure. An MCP server registers tools with JSON schemas, runs locally (or in your network), and processes tool invocations from an AI client. It’s similar in spirit to LSP for code or DAP for debugging, but generalized for AI tool use. Crucially:

• Tools are explicitly declared with typed input/output schemas.
• Transport can be stdio, sockets, or other channels, allowing host control.
• Servers can enforce policy and locality (e.g., never send source code out).
• Multiple tools can be composed behind a single server (e.g., gdb, lldb, rr, git, test runners), giving the AI a single, coherent interface.

Architecture Overview

A concrete, production-ready stack looks like this:

1. MCP Server (local/network):

   • Registers tools: dbg.attach, dbg.breakpoint.set, dbg.step, dbg.backtrace, dbg.eval, dbg.memory.read, rr.record/replay, patch.generate, tests.run, etc.
   • Controls gdb via MI or lldb via SB API.
   • Normalizes debugger differences into a single schema where possible.
   • Enforces policy: PID allowlists, read-only modes, rate limits, audit logs, redaction.

2. Debugger Adapters:

   • gdb Adapter (MI2): launches gdb with --interpreter=mi2, parses output, sends commands.
   • lldb Adapter (Python SB API or lldb-mi): attaches to process, performs operations.

3. Determinism Layer:

   • rr record/replay for Linux C/C++ binaries.
   • Hermetic container images with pinned toolchains and debug symbols.
   • Controlled time and RNG (faketime/clock mocks), network stubs (Toxiproxy), seeded data fixtures.

4. IDE Wiring:

   • VS Code or JetBrains plugin that speaks MCP.
   • Optional bridge to DAP to unify UI controls (breakpoints, stepping) while delegating the logic to MCP tools.

5. Policy and Observability:

   • Locality: source code and logs never leave the machine/network.
   • Role-based access controls for attaching to specific processes.
   • Audit trails of tool invocations and derived artifacts.

6. AI Orchestration:

   • The AI agent calls MCP tools to gather state (backtraces, locals, memory snapshots), correlates with local source, proposes diffs, and asks the test runner to validate.

Required Capabilities and APIs

At minimum, your MCP server should expose these tool categories:

• Process Management:
  • list_processes, attach(pid, debugger), detach, kill (optional and dangerous)
• Debugging Controls:
  • breakpoint.set, breakpoint.clear, step_in/over/out, continue, pause, thread.select
  • backtrace, frames, list_threads, registers.read, disassemble
  • eval(expression, frame/thread), memory.read(addr, length)
• Deterministic Repro:
  • rr.record(cmd), rr.replay(session), rr.step(time-travel), snapshot.create/load
• Source and Build Context:
  • symbols.locate, source.read(file, range), build.info, container.run(cmd)
• Patch and Test Loop:
  • patch.generate(context), patch.apply, tests.run(selector), tests.report, bisect.run

The trick is designing schemas that are cross-debugger and version-tolerant. For example, memory.read should return a typed structure with hex and base64 forms; breakpoint.set should accept by-file:line, by-function, or by-address, and return canonical IDs.

Example MCP Tool Schemas

Below is a minimal subset. In a production server, include descriptions, examples, and stricter validation.

json
{
  "tools": [
    {
      "name": "dbg.attach",
      "description": "Attach to a running process using gdb or lldb",
      "input_schema": {
        "type": "object",
        "properties": {
          "pid": {"type": "integer"},
          "debugger": {"type": "string", "enum": ["gdb", "lldb"]},
          "non_stop": {"type": "boolean", "default": true},
          "read_only": {"type": "boolean", "default": false}
        },
        "required": ["pid", "debugger"]
      },
      "output_schema": {
        "type": "object",
        "properties": {
          "session_id": {"type": "string"},
          "target": {"type": "string"},
          "threads": {"type": "array", "items": {"type": "object"}}
        }
      }
    },
    {
      "name": "dbg.breakpoint.set",
      "description": "Set a breakpoint by file:line, function, or address",
      "input_schema": {
        "type": "object",
        "properties": {
          "session_id": {"type": "string"},
          "file": {"type": "string"},
          "line": {"type": "integer"},
          "function": {"type": "string"},
          "address": {"type": "string"},
          "condition": {"type": "string"},
          "ignore_count": {"type": "integer", "default": 0}
        }
      },
      "output_schema": {
        "type": "object",
        "properties": {
          "breakpoint_id": {"type": "string"},
          "resolved": {"type": "boolean"},
          "location": {"type": "string"}
        }
      }
    },
    {
      "name": "dbg.step",
      "description": "Step control",
      "input_schema": {
        "type": "object",
        "properties": {
          "session_id": {"type": "string"},
          "mode": {"type": "string", "enum": ["in", "over", "out", "continue", "pause"]},
          "thread_id": {"type": "integer"}
        },
        "required": ["session_id", "mode"]
      },
      "output_schema": {"type": "object"}
    },
    {
      "name": "dbg.backtrace",
      "description": "Get a backtrace for the selected thread",
      "input_schema": {
        "type": "object",
        "properties": {
          "session_id": {"type": "string"},
          "thread_id": {"type": "integer"},
          "max_frames": {"type": "integer", "default": 64}
        },
        "required": ["session_id"]
      },
      "output_schema": {
        "type": "object",
        "properties": {
          "frames": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "index": {"type": "integer"},
                "function": {"type": "string"},
                "file": {"type": "string"},
                "line": {"type": "integer"}
              }
            }
          }
        }
      }
    },
    {
      "name": "dbg.memory.read",
      "description": "Read a range of memory from the process",
      "input_schema": {
        "type": "object",
        "properties": {
          "session_id": {"type": "string"},
          "address": {"type": "string"},
          "length": {"type": "integer", "minimum": 1, "maximum": 65536}
        },
        "required": ["session_id", "address", "length"]
      },
      "output_schema": {
        "type": "object",
        "properties": {
          "address": {"type": "string"},
          "bytes_hex": {"type": "string"},
          "bytes_b64": {"type": "string"}
        }
      }
    },
    {
      "name": "rr.replay",
      "description": "Replay an rr recording with time-travel stepping",
      "input_schema": {
        "type": "object",
        "properties": {
          "trace_dir": {"type": "string"},
          "commands": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["trace_dir"]
      },
      "output_schema": {"type": "object"}
    },
    {
      "name": "patch.generate",
      "description": "Propose a code patch for the identified root cause",
      "input_schema": {
        "type": "object",
        "properties": {
          "files": {
            "type": "array",
            "items": {"type": "string"}
          },
          "analysis": {"type": "string"},
          "tests": {
            "type": "array",
            "items": {"type": "string"}
          }
        }
      },
      "output_schema": {
        "type": "object",
        "properties": {
          "diff": {"type": "string"},
          "commit_message": {"type": "string"}
        }
      }
    }
  ]
}

Implementing the MCP Server

You can implement the server using any language, but Python is pragmatic given lldb’s Python API and solid gdb MI libraries.

gdb via MI2 (Machine Interface)

The MI2 interface is a machine-friendly text protocol. The pygdbmi package simplifies parsing.

python
# mcp_server_gdb.py
from pygdbmi.gdbcontroller import GdbController
from typing import Dict, Any
import json, threading

class GDBSession:
    def __init__(self, pid: int, non_stop: bool = True):
        self.gdb = GdbController(command=['gdb', '--interpreter=mi2'])
        self.lock = threading.Lock()
        self._write("-gdb-set auto-load safe-path /")
        if non_stop:
            self._write("-gdb-set non-stop on")
        self._write(f"-target-attach {pid}")
        # Optional: disable pagination
        self._write("-gdb-set pagination off")

    def _write(self, cmd: str):
        with self.lock:
            return self.gdb.write(cmd, timeout_sec=2)

    def set_breakpoint(self, file=None, line=None, function=None, address=None, condition=None, ignore_count=0):
        if file and line:
            spec = f"{file}:{line}"
            cmd = f"-break-insert {spec}"
        elif function:
            cmd = f"-break-insert {function}"
        elif address:
            cmd = f"-break-insert *{address}"
        else:
            raise ValueError("Invalid breakpoint spec")
        if condition:
            cmd += f" if {condition}"
        if ignore_count:
            cmd += f" -i {ignore_count}"
        return self._write(cmd)

    def step(self, mode: str):
        mapping = {
            'in': '-exec-step',
            'over': '-exec-next',
            'out': '-exec-finish',
            'continue': '-exec-continue',
            'pause': '-exec-interrupt'
        }
        return self._write(mapping[mode])

    def backtrace(self, max_frames=64):
        return self._write(f"-stack-list-frames 0 {max_frames}")

    def eval(self, expr: str):
        return self._write(f"-data-evaluate-expression {json.dumps(expr)}")

    def read_memory(self, addr: str, length: int):
        # results contain hex bytes under payload memory
        return self._write(f"-data-read-memory-bytes {addr} {length}")

lldb via SB API

lldb’s Python API is more structured. Example attach and memory read:

python
# mcp_server_lldb.py
import lldb

class LLDBSession:
    def __init__(self, pid: int):
        self.debugger = lldb.SBDebugger.Create()
        self.debugger.SetAsync(True)
        self.target = self.debugger.CreateTarget(None)
        self.listener = lldb.SBListener()
        error = lldb.SBError()
        self.process = self.target.AttachToProcessWithID(self.listener, pid, error)
        if not error.Success():
            raise RuntimeError(error.GetCString())

    def set_breakpoint(self, file=None, line=None, function=None, address=None, condition=None, ignore_count=0):
        if file and line:
            bp = self.target.BreakpointCreateByLocation(file, line)
        elif function:
            bp = self.target.BreakpointCreateByName(function)
        elif address:
            bp = self.target.BreakpointCreateByAddress(int(address, 16))
        else:
            raise ValueError("Invalid breakpoint spec")
        if condition:
            bp.SetCondition(condition)
        if ignore_count:
            bp.SetIgnoreCount(ignore_count)
        return bp

    def step(self, mode: str):
        thread = self.process.GetSelectedThread()
        if mode == 'in':
            thread.StepInto()
        elif mode == 'over':
            thread.StepOver()
        elif mode == 'out':
            thread.StepOut()
        elif mode == 'continue':
            self.process.Continue()
        elif mode == 'pause':
            self.process.Stop()

    def backtrace(self, max_frames=64):
        thread = self.process.GetSelectedThread()
        frames = []
        for i in range(min(thread.GetNumFrames(), max_frames)):
            f = thread.GetFrameAtIndex(i)
            frames.append({
                'index': i,
                'function': f.GetFunctionName(),
                'file': f.GetLineEntry().GetFileSpec().GetFilename(),
                'line': f.GetLineEntry().GetLine()
            })
        return frames

    def read_memory(self, addr: str, length: int):
        error = lldb.SBError()
        data = self.process.ReadMemory(int(addr, 16), length, error)
        if not error.Success():
            raise RuntimeError(error.GetCString())
        return data

Binding to MCP

An MCP server wraps these sessions and exposes the tool methods. A minimal server skeleton:

python
# mcp_server.py
import uuid
from typing import Dict
from fastapi import FastAPI
from pydantic import BaseModel
from mcp_types import ToolRequest, ToolResponse  # assume your own MCP types
from mcp_transport import MCPServer             # your transport impl (stdio/unix socket)
from mcp_server_gdb import GDBSession
from mcp_server_lldb import LLDBSession

app = FastAPI()
sessions: Dict[str, object] = {}

class AttachInput(BaseModel):
    pid: int
    debugger: str
    non_stop: bool = True
    read_only: bool = False

@app.post("/tools/dbg.attach")
def dbg_attach(inp: AttachInput):
    sid = str(uuid.uuid4())
    if inp.debugger == 'gdb':
        sess = GDBSession(inp.pid, non_stop=inp.non_stop)
    elif inp.debugger == 'lldb':
        sess = LLDBSession(inp.pid)
    else:
        raise ValueError("Unsupported debugger")
    sess.read_only = inp.read_only
    sessions[sid] = sess
    return {"session_id": sid, "target": inp.pid}

# Implement dbg.breakpoint.set, dbg.step, dbg.backtrace, dbg.memory.read, etc.

server = MCPServer(app)  # expose over stdio/socket per MCP spec
server.run()

This sketch omits transport details, discovery, and policy checks, but illustrates the layering. MCP clients (AI or IDE) hit the tool endpoints with typed JSON.

Deterministic Bug Reproduction

Attaching to a live process can be high-friction and non-deterministic. A “deterministic pipeline” ensures your AI can reproduce, diagnose, and verify fixes reliably.

1. Capture a Reproducible Snapshot:

   • Linux: use rr to record failing runs. It captures syscalls, nondeterministic inputs, and thread interleavings.
   • Use container images with pinned libc, compiler flags, and debug symbols.
   • Persist symbol files (DWARF/dSYM) alongside the build (BuildID/UUID indexed). Consider a local debuginfod server.

2. Time-travel Debugging with rr:

   • Record: rr record ./app --arg1 --arg2 or attach to a running process by launching under rr.
   • Replay: rr replay and attach gdb automatically with rr’s gdb plugin. From MCP, you can drive rr by spawning replay and issuing gdb commands.

3. Control the Environment:

   • Clock: use libfaketime or time abstraction to freeze time.
   • RNG: inject deterministic seeds (env var, init call).
   • Network: Toxiproxy or local mocks to make flaky I/O predictable.
   • Filesystem: hermetic test fixtures, temporary directories with stable layout.

4. Test Harness:

   • Encode the failing behavior into an integration or property-based test that reproduces under rr/container.
   • Your MCP server can expose tests.run to let the AI validate patches and verify the failure flips to pass.

5. Concurrency Bugs:

   • rr excels here; the AI can use rr’s “reverse-continue” and “reverse-step” to walk back from a crash to the last write of a corrupt memory location.
   • gdb commands: reverse-continue, reverse-step, watch *addr and reverse-continue to writer.

6. When You Can’t rr:

   • On macOS, rr isn’t available. Prefer lldb plus core dumps, or sampling-based eBPF/ktrace for low-overhead data.
   • Use process snapshots (core) with gcore (Linux) or procdump (macOS, via lldb) to avoid perturbing the process.

Driving Breakpoints, Memory, and Expressions

The AI’s primary loop is: observe -> hypothesize -> probe -> validate. Make these primitives fast and predictable.

• Breakpoints:

  • Always allow conditional breakpoints to limit perturbation: e.g., req_id == 1234 && counter > 1000.
  • For production attaches, use non-stop mode (gdb) so only the thread of interest halts.
  • Hardware watchpoints are precise but scarce; on x86 you have 4. Use sparingly.

• Expression Evaluation:

  • Evaluate in a specific frame/thread; expose frame/thread selectors in APIs.
  • Normalize C++ name mangling differences between gdb and lldb if the AI reasons about symbols.

• Memory Reads:

  • Return both hex and base64; include an endian hint and pointer size from target to help decoding.
  • Guard length (e.g., max 64KB) and rate limit in production.

• Backtraces:

  • Include inline frame info and symbol fallbacks (addresses if symbols unavailable).
  • Encourage symbolization by ensuring debug symbols are present. Provide a tool to map BuildID -> symbol file path.

Patch Proposal and Validation Loop

The AI should never blindly change code. A disciplined loop:

1. Synthesize a Hypothesis:

   • From backtraces, locals, memory invariants, and error logs, propose a concrete root cause.
   • Keep the analysis local; if a remote LLM is used, ship only derived features (variable names redacted, no full source) per policy.

2. Generate a Minimal Patch:

   • Produce a unified diff with context lines and a descriptive commit message.
   • If the fix affects concurrency, add fences, atomics, or lock ordering comments; prefer tested idioms.

3. Run Tests:

   • Fast unit tests first, then targeted integration tests that reproduce the original failure.
   • Optionally run rr replay of the failing trace with the patch to confirm the error path is eliminated.

4. Produce Evidence:

   • Before/after traces, stack diffs, invariant checks, perf delta if relevant.
   • Only then propose a PR with the diff and the evidence attached.

Minimal diff format example:

diff
--- a/src/cache.c
+++ b/src/cache.c
@@ -210,7 +210,12 @@ int cache_put(Cache* c, const char* k, const char* v) {
-    node->value = strdup(v);
+    if (!v) {
+        return ERR_NULL_VALUE;
+    }
+    char* tmp = strdup(v);
+    if (!tmp) return ERR_OOM;
+    node->value = tmp;
     node->ts = now();
     return 0;
 }

IDE Wiring: VS Code and JetBrains

There are two good patterns:

• MCP-Native View:

  • Build an IDE extension that speaks MCP directly. Render a “Debug AI” panel with backtraces, locals, memory hex view, and patch proposals. Tool invocations are logged and clickable.

• DAP Bridge:

  • Implement a Debug Adapter Protocol (DAP) shim that forwards DAP requests to MCP tools. This reuses built-in UI for breakpoints, stepping, and variables. For specialized operations (rr reverse-step, patch.generate), add custom commands in the extension that call MCP directly.

Key tips:

• Use problem matchers to surface AI-generated diagnostics inline in source.
• Use CodeLens to insert “Reproduce with rr” or “Propose patch” commands at the relevant frames/lines.
• Cache symbol lookup results in the extension to speed up symbolized views.

Security, Privacy, and Policy

• Locality First:

  • The MCP server runs on the developer’s machine or inside the organization’s network. It never exfiltrates source or raw logs. If an external LLM is used, the server enforces a redaction/aggregation policy.

• RBAC and Safelists:

  • Only allow attaching to PIDs owned by the user or to safelisted services.
  • Require elevated approvals to attach to production processes; log every attach/detach.

• Rate Limits and Quotas:

  • Limit frequency and duration of pauses. For production, use read-only mode (no breakpoints), rely on sampling/eBPF, or attach with non-stop mode and conditional breakpoints.

• Redaction:

  • Strip memory reads of obvious PII (emails, tokens) before letting the AI see them. Provide a tool to compute structured summaries (e.g., counts, histograms) without sending raw buffers.

• Auditability:

  • Log every tool call, inputs (minus sensitive fields), outputs, and user overrides. Make it easy to answer “what did the AI do?”

Safety Trade-offs of Live Attaches

Attaching a debugger changes process behavior.

• Stop-the-world vs non-stop:

  • Default ptrace attach stops all threads. In production this can trip timeouts. Prefer non-stop mode (gdb) or a shadow process with rr.

• Overheads:

  • Breakpoints replace instruction bytes with traps; hitting them incurs context switches.
  • Watchpoints use debug registers; can degrade performance or be imprecise if software-emulated.

• Signals and Handlers:

  • The debugger may intercept signals (SIGSEGV, SIGALRM). Understand how your production app uses signals to avoid unexpected behavior.

• macOS SIP and Entitlements:

  • lldb attach may require disabling SIP for system processes or granting task_for_pid entitlement (dev-only). Do not attempt privileged attaches in production macOS without a security review.

• Linux ptrace_scope:

  • On hardened systems, set kernel.yama.ptrace_scope=0 for unrestricted ptrace (dev only). In production, prefer core dumps, eBPF, or rr in staging.

• Alternatives in Production:

  • eBPF uprobes for read-only function probes.
  • Core dumps on crash (ulimit -c, systemd-coredump). Analyze offline.

Cross-Platform and Symbols

• Symbols:

  • Linux: separate DWARF packages; use BuildID symlinks and debuginfod.
  • macOS: dSYMs via dsymutil; ensure Spotlight index or symbol cache for lldb.
  • Containers: mount symbol volumes; store symbols in an artifact store keyed by build.

• ABI and Arch:

  • Expose word size, endianness, and arch in dbg.attach output so the AI decodes pointers and registers correctly.

• Windows (Future Work):

  • Use cdb/windbg or DbgEng. Expose equivalent MCP tools. Symbol server integration (PDB) is essential.

Example Session: From Crash to Patch

1. Developer sees a sporadic segfault in a C++ service on Linux.
2. They reproduce under rr: rr record ./svc --seed 4242 --case edge-17.
3. The AI is invoked from the IDE. It calls rr.replay with a script: reverse-continue to crash, print backtrace, watch last-writer to a null pointer.
4. MCP dbg.backtrace returns frames pointing to cache.cc:413. dbg.eval shows a missing null check; dbg.memory.read verifies a previously freed pointer is dereferenced under rare interleavings.
5. The AI generates a patch adding a null check and fixes a race by switching to std::atomic for a shared flag.
6. tests.run executes unit and integration tests plus rr replay. All pass.
7. The AI opens a PR with the diff and an analysis note linking the rr session.

Productionizing: Packaging and Deployment

• Package the MCP server as a container with:

  • gdb, rr (Linux), lldb (macOS), symbol tools, pygdbmi, Python 3.11+.
  • A thin configuration layer for policy (YAML): allowed PIDs, resource limits, redaction rules.

• Permissions:

  • Linux: CAP_SYS_PTRACE or ptrace_scope=0 in dev; run as root only if necessary. Prefer user namespaces where possible.
  • macOS: run as the same user as the target process; for system processes, consider core dumps instead.

• Observability:

  • Expose Prometheus metrics: attach_count, breakpoint_hits, memory_reads_total, rr_replays, patch_proposals.
  • Store audit logs locally with rotation.

Example: rr Integration

A small helper in your server to drive rr replay with gdb MI:

python
import subprocess, os, tempfile
from pygdbmi.gdbcontroller import GdbController

def rr_replay(trace_dir: str, commands: list[str]):
    env = os.environ.copy()
    env["RR_TRACE_DIR"] = trace_dir
    # rr will spawn gdb; use the gdb bridge
    with tempfile.NamedTemporaryFile("w", delete=False) as f:
        for c in commands:
            f.write(c + "\n")
        script = f.name
    # rr replay -x <script> will run commands via gdb
    p = subprocess.run(["rr", "replay", "-x", script], env=env, capture_output=True, text=True)
    return {"stdout": p.stdout, "stderr": p.stderr, "returncode": p.returncode}

Commands might include:

reverse-continue
bt 20
watch *0x7ffff7fdf000
reverse-continue
info registers

Your MCP rr.replay tool can wrap this and parse the output into structured JSON for the AI.

Keeping Source and Logs Local

• Source Access:

  • The AI requests file slices through an MCP source.read tool. The server returns only the requested ranges, never entire repos by default.
  • Enforce glob allowlists (e.g., src/**/*.cc) and exclude secrets.

• Logs:

  • Provide logs.query that returns structured summaries (counts, regex-matched windows) rather than raw logs.
  • For deep dives, require user confirmation and redact known PII patterns.

• Model Choices:

  • If you must use a remote LLM, ship only structured context (stack signatures, symbol names, code snippets under a line threshold) and codify this in policy that the server enforces.
  • For fully local, pair MCP with a local model (e.g., llama.cpp, vLLM) and ensure no egress.

Common Pitfalls and How to Avoid Them

• Missing Symbols:

  • Symptom: backtraces show addresses. Fix: install debug symbol packages and configure debuginfod; for macOS, generate dSYMs.

• Heisenbugs Due to Attach:

  • Symptom: bug disappears when attaching. Fix: prefer rr replay or core dumps; use non-stop mode; reduce breakpoints; use conditional breaks.

• Timeouts and SLAs:

  • Symptom: attaching stalls prod requests. Fix: read-only sampling via eBPF/uprobes; attach only in off-peak; set time budgets; always ask for confirmation in IDE.

• Cross-Container Mismatches:

  • Symptom: symbols don’t match running binary. Fix: ensure container’s BuildID matches symbol store; expose build.info tool to verify.

• Over-aggressive Memory Reads:

  • Symptom: large memory copies cause latency. Fix: cap reads, summarize structures, and provide schema-aware decoders for common types.

Opinionated Guidance

• Use rr wherever possible for C/C++ on Linux. Time travel turns many “mostly impossible” bugs into straightforward archaeology.
• Normalize all debugger interactions through your MCP server. Do not let the AI shell out to arbitrary commands.
• Make the AI earn trust: require it to produce a clear hypothesis, a minimal diff, and green tests before suggesting merge.
• Treat production attaches as exceptional. Prefer sampling, tracing, and crash dumps for prod; use live attaches only with guardrails.
• Invest in symbols and hermetic builds. Debugging without symbols is a tax you pay over and over.

Roadmap and Extensions

• Time-Travel for macOS:

  • Investigate persistent snapshots and deterministic schedulers; consider QEMU-based record/replay for select services.

• Language-Specific Decoders:

  • Add pretty-printers for STL/boost/Abseil; Rust pretty-printers via gdb/lldb scripts; schema-aware memory readers exposed via MCP.

• Knowledge Capture:

  • Persist “debug narratives” (tool call sequences, findings, diffs) as playbooks. Next time a similar crash signature appears, the AI can propose a known fix faster.

• Windows Support:

  • MCP tools for cdb/windbg with PDB symbol servers; a DAP bridge for windbg would unlock many enterprise services.

Conclusion

An attachable debug AI is no longer speculative. By wrapping gdb/lldb and rr behind an MCP server, you can give an AI agent safe, audited access to the same deterministic debugging primitives expert engineers use—without letting source code or logs escape. The result is a system that can reproduce failures, inspect real process state, and propose patches with evidence, all integrated into your IDE.

Build it once with strong policies and solid APIs, and your team will spend less time wading through flaky logs and more time delivering robust fixes—with auditable, deterministic confidence.