┌──────────────────────────────────────────────────────────────────┐
│  MCP Client                                                       │
│  (Inspector UI · Claude Desktop · custom ClientSession · agent)   │
└───────────────────────────────┬──────────────────────────────────┘
                                │  MCP over stdio
                                │  tools/list · tools/call
                                ▼
┌──────────────────────────────────────────────────────────────────┐
│  server.py                                                        │
│  FastMCP("Local Intelligence Server")                             │
│                                                                   │
│  @mcp.tool()                                                      │
│  def analyze_data(raw_text, instruction) -> str                   │
│        │                                                          │
│        ├─ system: "You are a precise data analysis assistant."    │
│        ├─ user:   f"{instruction}\n\n{raw_text}"                  │
│        │                                                          │
│        └─ ollama.chat(                                            │
│             model="qwen3.5:4b",                                   │
│             options={"temperature": 0},                           │
│             think=False,                                          │
│           )                                                       │
│        → response["message"]["content"]  (or error string)        │
└───────────────────────────────┬──────────────────────────────────┘
                                │  HTTP POST /api/chat
                                │  127.0.0.1:11434 only
                                ▼
                     ┌─────────────────────┐
                     │  Ollama daemon       │
                     │  qwen3.5:4b weights  │
                     └─────────────────────┘

except Exception as e:
    return f"Error calling local LLM: {e}"

User → Host LLM → (sees full logs in prompt) → answer

User → Host LLM → tools/call analyze_data(raw_text, instruction)
                      → Local LLM analyzes
                      → compact string returns
                 → Host LLM uses summary for the user

Query → embed query → vector search over chunks → (optional rerank)
      → stuff top-k chunks into prompt → generate answer

1. Import FastMCP from official SDK
2. Instantiate mcp = FastMCP("Local Intelligence Server")
3. Decorate analyze_data with @mcp.tool()  → schema + description auto-derived
4. On call:
     a. try:
     b.   ollama.chat(model, messages, options, think=False)
     c.   read message.content
     d.   if empty → error string
     e.   else return content
     f. except → error string
5. mcp CLI loads module, finds global `mcp`, runs server.run()

for each Loghub 40-line slice:
    analyze_data(raw, instruction)     # direct Python call
    write artifacts/<id>_direct.txt

MCP ClientSession over stdio:
    list_tools → expect ["analyze_data"]
    call_tool(apache case)
    write artifacts/mcp_stdio_apache.txt

write artifacts/run_summary.json + full_outputs.json
assert quality gate (non-empty, no errors)

.
├── server.py                 # MCP server + analyze_data
├── run_demo.py               # Reproducible real-run harness
├── main.py                   # uv init stub (unused by MCP path)
├── pyproject.toml            # project metadata + deps
├── uv.lock                   # locked graph
├── .python-version           # 3.13.13
├── LICENSE                   # MIT (code)
├── README.md                 # this file
├── data/
│   ├── SOURCE.md             # Loghub provenance
│   ├── apache_access_sample.log
│   ├── linux_syslog_sample.log
│   ├── hdfs_sample.log
│   ├── apache_40.log         # analysis slice
│   ├── linux_40.log
│   └── hdfs_40.log
└── artifacts/                # outputs from real runs
    ├── run_summary.json
    ├── full_outputs.json
    ├── apache_mod_jk_errors_direct.txt
    ├── linux_ssh_auth_failures_direct.txt
    ├── hdfs_block_activity_direct.txt
    └── mcp_stdio_apache.txt

ollama list | grep 'qwen3.5:4b'
# expect a line with qwen3.5:4b

# From the project root
uv python install 3.13.13    # if needed; fallback policy is 3.12.10 only if 3.13.13 is unresolvable
uv venv --python 3.13.13
uv python pin 3.13.13
uv sync                      # restores mcp[cli] + ollama from uv.lock

uv run python -c "
import importlib.metadata as m
from mcp.server.fastmcp import FastMCP
print('mcp', m.version('mcp'))
print('ollama', m.version('ollama'))
print('FastMCP OK', FastMCP)
"

uv run python run_demo.py

uv run mcp dev server.py

Proxy server listening on localhost:6277
MCP Inspector is up and running at:
  http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=...

uv run mcp run server.py

uv run python -c "
from pathlib import Path
from server import analyze_data
raw = Path('data/apache_40.log').read_text()
print(analyze_data(raw, 'List unique error messages and approximate counts.'))
"

[Sun Dec 04 04:47:44 2005] [notice] workerEnv.init() ok /etc/httpd/conf/workers2.properties
[Sun Dec 04 04:47:44 2005] [error] mod_jk child workerEnv in error state 6
[Sun Dec 04 04:51:08 2005] [notice] jk2_init() Found child 6725 in scoreboard slot 10
...
[Sun Dec 04 04:51:18 2005] [error] mod_jk child workerEnv in error state 6

### 1. Dominant Failure Pattern
The dominant failure pattern is the continuous cycling of Apache JK (mod_jk) backend workers into an **unrecoverable "error" state** despite repeated initialization attempts by `workerEnv.init()`. While new child processes are successfully spawned and registered in the scoreboard, they consistently fail to transition from a healthy or transient error state 6 directly to a stable operational state.

### 2. Unique Error Messages and Approximate Counts
Based on the provided log snippet:

*   **`[error] mod_jk child workerEnv in error state 6`** (Approximately 10 occurrences)
    *   This is the most frequent specific error, appearing repeatedly across different timestamps (e.g., at 04:51:18 and 04:52:15). It indicates a persistent failure condition where the worker cannot recover.
*   **`[error] mod_jk child workerEnv in error state 7`** (Approximately 3 occurrences)
    *   This appears as an escalation or alternative manifestation of the same underlying issue, seen specifically at timestamp `04:52:15`.

*(Note: The logs also contain numerous `[notice] jk2_init() Found child...` and `[notice] workerEnv.init() ok ...` messages indicating successful spawning and initial configuration checks.)*

### 3. Most Likely Root Cause
The root cause is likely a **configuration mismatch or resource exhaustion** preventing the JK workers from completing their initialization sequence, as evidenced by `workerEnv.init()` returning "ok" while the subsequent state check immediately reports an error in states 6 and 7. This suggests that although the Apache process starts successfully, the backend application (Java/Tomcat) running within these children fails to bind or start due to missing ports, insufficient memory, or a deadlock during startup logic.

Jun 14 15:16:01 combo sshd(pam_unix)[19939]: authentication failure; ... rhost=218.188.2.4
Jun 15 02:04:59 combo sshd(pam_unix)[20882]: authentication failure; ... rhost=220-135-151-1.hinet-ip.hinet.net  user=root

### 1) Security-Relevant Events Identified
*   **SSH Authentication Failures:** Multiple repeated failures detected via `sshd(pam_unix)` indicating brute-force or credential stuffing attempts against the root user (`user=root`).
    *   Source: Jun 15 02:04:59 (IP: 220-135-151-1.hinet-ip.hinet.net) – **7 instances**.
    *   Source: Jun 15 12:12:34 & 12:13:19 (IP: 218.188.2.4) – **Multiple instances** (~16 total).
*   **Successful Privilege Escalation:** Two `su` sessions opened by root (`uid=0`) for non-root users, indicating successful compromise or authorized admin activity.
    *   User: `cyrus` (Jun 15 04:06:18–20).
    *   User: `news` (Jun 15 04:12:42–43).

### 2) Distinct Remote Hosts/IPs Involved in Auth Failures
*   **220-135-151-1.hinet-ip.hinet.net** (IPv6 mapped notation for `220.135.151.1`) – Associated with high-volume root login attempts at 02:04 UTC on Jun 15.
*   **218.188.2.4** (IPv4) – Associated with repeated authentication failures starting Jun 14 and continuing through the morning of Jun 15.

### 3) Non-Auth System Events
*   **Service Activity:** Successful `su` sessions for users `cyrus` and `news`.
*   **Log Rotation Alert:** `logrotate: ALERT exited abnormally with [1]` at Jun 15 04:06:20. This indicates a configuration or disk space issue preventing log rotation, which may be causing syslog buffer issues given the high volume of auth logs generated during the attack window.

### 4) Ops Triage Recommendation
*   **Immediate Action:** Block both remote IPs (`218.188.2.4` and `220-135-151-1.hinet-ip.hinet.net`) at the firewall level immediately to stop active credential stuffing attacks on root accounts.
*   **Investigation Priority 1 (Compromise):** Verify if users `cyrus` or `news` were authorized by an admin who used their credentials for `su`. If unauthorized, assume a successful breach and initiate incident response procedures (reset passwords, audit user activity).
*   **Log Management:** Investigate the `logrotate` failure. With such high log volume from SSH attacks, disk space exhaustion is likely; ensure logs are being archived or rotated to prevent system instability during future attack waves.

Based on the provided HDFS NameNode/DataNode style logs, here is the analysis:

### 1. Summary of Block/Packet Operations
The log snippet depicts a standard **block replication and storage lifecycle** within an HDFS cluster. The operations observed include:
*   **Block Allocation:** New blocks are being allocated for tasks (specifically `/user/root/rand/_temporary/...`), indicated by `NameSystem.allocateBlock`. This suggests active data processing or job execution where new files need to be stored immediately upon creation.
*   **Data Reception & Replication:** Multiple DataNodes (`dfs.DataNode$PacketResponder`) are receiving blocks from various remote peers (e.g., `/10.251.x.x`). The consistent block size of `67108864` bytes indicates standard 64MB replication chunks.
*   **Storage Registration:** As data is received, the NameNode (`dfs.FSNamesystem`) updates its in-memory map to track these blocks as "stored," linking specific DataNodes (e.g., `10.251.73.220:50010`) to their respective block IDs via `NameSystem.addStoredBlock`.
*   **Verification:** A single instance of successful verification (`dfs.DataBlockScanner`), confirming data integrity for a specific block ID before it was likely committed or moved in the broader context (though this log only shows success).

### 2. Unique Components and Thread Names
The logs contain explicit thread/component identifiers following the standard Hadoop logging format `Component$Subcomponent`. The unique components identified are:
*   **dfs.DataNode$PacketResponder**: Handles incoming block data packets for specific blocks or general termination of responder threads.
*   **dfs.FSNamesystem**: Manages high-level filesystem operations, including adding stored blocks to the map and allocating new blocks.
*   **dfs.DataNode$DataXceiver**: Specifically handles the network reception stream (`Receiving block ... src: ... dest: ...`).
*   **dfs.DataBlockScanner**: Responsible for verifying data integrity (checksums) of specific blocks.

*(Note: While timestamps like `081109` and thread IDs in packet responders exist, they are not unique component names).*

### 3. Health Status Assessment
**The snippet looks healthy.**

*   **Evidence:** All log entries are prefixed with the level `INFO`. There are no lines containing `WARN`, `ERROR`, or any other severity indicators indicating a failure or warning condition. The operations (allocation, reception, verification) completed successfully without interruption in this timeframe.

# Ollama up?
curl -s http://127.0.0.1:11434/api/tags | head

# Tool path only
uv run python -c "from server import analyze_data; print(analyze_data('a=1\nb=2','Sum the numbers'))"

# MCP path only
uv run python run_demo.py

@mcp.tool()
def count_error_lines(raw_text: str) -> str:
    """Deterministic count of lines containing ERROR/Error/error."""
    n = sum(1 for line in raw_text.splitlines() if "error" in line.lower())
    return f"error_line_count={n}"

index_logs(directory) → chunks + embeddings + store
search_logs(query)    → top-k passages
analyze_data(passages + instruction) → still local generation

{
  "mcpServers": {
    "local-intelligence": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/project", "mcp", "run", "server.py"]
    }
  }
}

# install
uv sync

# prove the stack
uv run python run_demo.py

# interactive MCP
uv run mcp dev server.py

# headless MCP server
uv run mcp run server.py