{"slug": "trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop", "title": "Trace Agent Tool Calls on a Free Server: A 10M-Token Debug Loop", "summary": "A developer detailed a debugging workflow that uses tool-call traces to diagnose failures in LLM agents, leveraging MonkeyCode's free server tier and 10 million free tokens. The approach involves wrapping every tool call with a decorator to log timestamps, arguments, results, and diffs, then analyzing the traces to identify suspicious calls. The developer shared code examples and a seven-step loop to catch common failure patterns.", "body_md": "At 2 AM, my agent rewrote a config file. Tests passed locally. The deployment failed silently.\n\nThe logs showed no error. The agent called `read_file`\n\nand `write_file`\n\n. The diff looked correct. But the service crashed.\n\nI needed tool-call traces. Every input, output, and diff. Not just metrics.\n\nMonkeyCode is an open-source project. It offers a free server tier and 10 million free tokens for LLM calls. That's enough to run a trace-analysis pipeline for a real debugging session. Disclosure: This article was prepared as part of MonkeyCode's product outreach.\n\nHere is the workflow I use.\n\nLLM agents hide their reasoning. You see the final patch. You do not see the bad assumption.\n\nTool calls are the ground truth. They show what the model actually did. Which file it read. Which command it ran. Which value it wrote.\n\nDiffs show the change. Without them, you cannot tell if the agent edited the right lines.\n\nWrap every tool in a small decorator. Record the timestamp, tool name, arguments, return value, and token cost.\n\n``` python\n# trace_tools.py (example)\nimport json, time\nfrom functools import wraps\n\nTRACE_LOG = 'traces.jsonl'\n\ndef traced(name):\n    def decorator(fn):\n        @wraps(fn)\n        def wrapper(*args, **kwargs):\n            start = time.time()\n            result = fn(*args, **kwargs)\n            entry = {\n                'time': start,\n                'tool': name,\n                'args': kwargs,\n                'result': result,\n                'cost_tokens': kwargs.get('max_tokens', 0)\n            }\n            with open(TRACE_LOG, 'a') as f:\n                f.write(json.dumps(entry) + chr(10))\n            return result\n        return wrapper\n    return decorator\n```\n\nThis gives you a JSONL file. One line per call. Easy to parse.\n\nMonkeyCode's free server hosts a small parser. Upload the log file first.\n\n```\nscp traces.jsonl user@your-free-server:~/agent-runs/\n```\n\nThen run the analysis script.\n\n```\npython analyze_trace.py traces.jsonl\n```\n\nCapture the file state before and after each call. Then use `difflib.unified_diff`\n\n.\n\n``` python\nimport difflib\n\ndef compute_diff(before, after):\n    before_lines = before.splitlines()\n    after_lines = after.splitlines()\n    return chr(10).join(difflib.unified_diff(\n        before_lines, after_lines, lineterm=''\n    ))\n```\n\nStore the `before`\n\nsnapshot in each trace entry. Add it to your decorator.\n\nHere is the core logic. It uses MonkeyCode's free model to label each call.\n\n``` python\n# analyze_trace.py (pseudocode)\nimport json, sys\n\ndef analyze(path):\n    with open(path) as f:\n        traces = [json.loads(line) for line in f]\n    for t in traces:\n        diff = compute_diff(t['before'], t['result'])\n        # MonkeyCode free model call (pseudocode)\n        label = monkeycode.complete(\n            prompt='Did this diff preserve intent?',\n            trace=t,\n            diff=diff,\n            model='free'\n        )\n        if label == 'suspicious':\n            print(t['time'], t['tool'])\n\nanalyze(sys.argv[1])\n```\n\nThis script answers one question. “Which tool call likely caused the failure?”\n\nMy loop has seven steps.\n\nRepeat until no call gets flagged.\n\nHere is the table I use for every trace.\n\n| Field | Why it matters |\n|---|---|\n| timestamp | call order |\n| tool name | action taken |\n| arguments | model's belief |\n| result | actual outcome |\n| diff | code change |\n| token cost | budget leak |\n\nThe combination of diff and arguments catches most failures.\n\nI see three patterns again and again.\n\n| Symptom | Likely cause | Check |\n|---|---|---|\n| wrong file changed | bad tool argument | diff |\n| empty output | truncated context | result |\n| repeated call | misread error | timestamp |\n\nThe debug loop catches all three. It just needs a few good traces.\n\nLast week, an agent ran a rename operation. It called `move_file(src, dst)`\n\n. The diff showed old content overwritten.\n\nThe trace revealed a third argument. The tool schema changed. The agent used an outdated description.\n\nThe debug loop caught it in minutes. No paid telemetry needed.\n\nThe 10M token pool is finite. Plan how far it goes.\n\nAssume one analysis costs 200 tokens. That gives 50,000 analyses. A few failing runs produce hundreds of traces. The free tier lasts a long time.\n\nThe math changes if you call the model per tool call. Batch multiple calls into one prompt. You save tokens.\n\nThis approach needs a wrappable agent. Some agents use black-box functions.\n\nThe free server may not handle high concurrency. Do not run production telemetry there.\n\nThe 10M token allowance is generous. But it is not for high-frequency online summarization. Batch your traces.\n\nMy capture script is example code. Adjust it to your own agent framework.\n\nIf you need real-time alerting, choose a hosted observability service.\n\nIf your agent spawns many parallel tools, the free server might drop requests.\n\nIf you store sensitive data, do not upload traces to a remote server. Run a local parser instead.\n\nTool-call traces bridge logs and outcomes. You do not need a big budget to start.\n\nMonkeyCode's free tier let me test this pipeline. You can try it too. Start with one failing run. Trace it. Fix it. Repeat.\n\nThe next time your agent breaks at 2 AM, you will know exactly which call to blame.", "url": "https://wpnews.pro/news/trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop", "canonical_source": "https://dev.to/apprs_6334/trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop-549i", "published_at": "2026-08-29 10:06:51+00:00", "updated_at": "2026-08-29 10:49:36.150793+00:00", "lang": "en", "topics": ["ai-agents", "developer-tools", "mlops"], "entities": ["MonkeyCode"], "alternates": {"html": "https://wpnews.pro/news/trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop", "markdown": "https://wpnews.pro/news/trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop.md", "text": "https://wpnews.pro/news/trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop.txt", "jsonld": "https://wpnews.pro/news/trace-agent-tool-calls-on-a-free-server-a-10m-token-debug-loop.jsonld"}}