Provides network troubleshooting for Cisco Packet Tracer labs by analyzing symptoms, topology context, and Cisco IOS show command outputs to identify root causes, recommend verification commands, and construct safe remediation plans.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@NetSage AI Diagnose why hosts in VLAN 10 can't ping VLAN 20 and recommend fixes."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Network troubleshooting copilot for Cisco Packet Tracer labs. Built with a hard rule: it can never touch your devices.
Junior network engineers can recite IOS commands in their sleep. What trips them up is the gap between "PC can't reach the web server in VLAN 30" and figuring out which of the twelve things that could cause that is actually broken.
NetSage reads your show command output, cross-references it against a rule engine and an LLM, and tells you where to look next. It does not fix anything. It cannot. There's no write access to any control plane — by design, not by accident.
Every diagnosis it produces requires a human to explicitly approve, edit, or reject it before it goes anywhere. That's not a feature checkbox. It's the entire point.
Related MCP server: packet-tracer-mcp
Before anything else: here's what the system will never do, regardless of what you ask it.
Issue configure terminal, write memory, reload, or any other write command
Invent facts that aren't in the show output you gave it
Let a remediation step execute without a named reviewer signing off
Suggest "read-only next steps" that are actually disruptive
Rejected and edited suggestions get logged to data/responsible_ai_log.csv with the reason. That log is how you catch the model hallucinating and improve prompts over time.
Symptom + show outputs
│
▼
Deterministic rule check (regex, no LLM)
│
▼
Multi-tier LLM diagnosis
├─ Tier 1: Hugging Face (Llama-3.2-3B-Instruct)
├─ Tier 2: Local Ollama (air-gapped fallback)
└─ Tier 3: CiscoRuleChecker (zero deps, always works)
│
▼
NIST SP 800-53 / CIS benchmark security check
│
▼
Human reviewer: Accept / Edit / Reject
│
▼
Approved verification steps onlyThe deterministic checker runs first, every time. If it catches the problem (gateway mismatch, missing OSPF network statement, etc.), you get an answer immediately. The LLM is for the cases that don't pattern-match cleanly.
Requirements: Python 3.10+. Ollama and a HF token are optional — the deterministic fallback works without either.
git clone https://github.com/your-org/netsage-ai.git
cd netsage-ai
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
# Edit .env — set HF_API_TOKEN if you want cloud inference
# Set AI_BACKEND=fallback if you just want the rule engine
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000Open http://localhost:8000. That's it.
.env options:
# AI_BACKEND: auto | huggingface | ollama | fallback
AI_BACKEND=auto
HF_API_TOKEN=hf_...
HF_MODEL=meta-llama/Llama-3.2-3B-Instruct
OLLAMA_BASE_URL=http://127.0.0.1:11434
OLLAMA_MODEL=llama3.2:latest
ALLOWED_ORIGINS=http://localhost:800032 Packet Tracer lab cases in data/cases.csv. Hand-curated, ground-truth validated, covering OSI Layers 1–7. Not generated.
Domain | Cases | What's actually in there |
VLAN & Trunking | 5 | Native VLAN mismatch, missing access VLAN, VTP domain errors |
Gateway & Subnetting | 4 | /24 vs /25 masks, SVI/gateway mismatches, HSRP misconfig |
Routing Protocols | 5 | Bad static routes, OSPF network statements, passive interface bugs |
ACL & Security | 4 | Implicit deny, wrong ACL direction, port security violations |
DHCP | 4 | Pool exhaustion, missing exclusions, relay agent missing |
DNS | 2 | Typo in server IP, unreachable server route |
NAT / PAT | 3 | Missing |
Wireless & Isolation | 3 | Guest leakage, SSID-to-VLAN mapping, WPA2 PSK mismatch |
Physical / Interface | 2 | Admin shutdown, line protocol down |
Run the checker headless against all 32:
python checker.py
# CASE-001: missing_route (High)
# CASE-002: acl_blocks_traffic (High)
# ...These are documented, not buried. This is the responsible AI log in action:
Case | What the AI said | What was actually wrong | Outcome |
CASE-002 | Missing route | ACL blocking traffic at Layer 4 — routing table was fine | Edited |
CASE-006 | DHCP server down | Client had a valid lease; gateway was wrong ( | Edited |
CASE-012 | DNS daemon crashed | DHCP was handing out a typo'd DNS IP ( | Edited |
CASE-020 | Add broad | That would have exposed internal subnets to guest Wi-Fi | Rejected |
CASE-030 | OSPF routing failure | MTU mismatch — small pings worked, jumbo pings dropped | Edited |
CASE-020 is the important one. The AI proposed permit ip any any as a fix. A reviewer caught it, rejected it, and logged why. That's the loop working correctly.
NetSage runs a native MCP 2.x Streamable HTTP server at /mcp. Works with Claude Desktop, Cursor, or any MCP-compatible agent.
Tool exposed:
{
"name": "validate_network_evidence",
"description": "Deterministic inspection of Cisco show outputs for misconfigurations.",
"inputSchema": {
"required": ["case_id", "symptom", "show_outputs"]
}
}Claude Desktop config:
{
"mcpServers": {
"netsage-ai": {
"url": "http://localhost:8000/mcp"
}
}
}Method | Endpoint | What it does |
GET |
| Health check |
GET |
| All 32 lab cases |
POST |
| Run AI diagnosis on a case |
GET |
| Deterministic rule check only |
POST |
| Submit Accept / Edit / Reject |
POST |
| Conversational assistant (ticket-aware) |
GET |
| Which backend is active right now |
GET |
| Summary stats across all cases |
GET |
| NIST + CIS compliance check |
pytest -v
pytest --cov=app tests/ # with coverageFour test suites: API contracts, deterministic rule engine, AI fallback behavior, chat assistant.
netsage-ai/
├── app/
│ ├── api/routes.py # REST endpoints
│ ├── application/
│ │ ├── rules.py # CiscoRuleChecker — the always-on fallback
│ │ ├── service.py # Orchestration
│ │ ├── prompt_library.py # Prompts + few-shot examples
│ │ ├── security_service.py # NIST/CIS bridge
│ │ └── fallback_chat.py # Deterministic chat path
│ ├── domain/models.py # Case, Review, Diagnosis types
│ ├── infrastructure/
│ │ ├── live_providers.py # HF + Ollama adapters
│ │ ├── fallback_provider.py # Rule-based diagnosis
│ │ ├── llm_client.py # HTTP client for both LLM backends
│ │ ├── file_repositories.py # CSV / JSON persistence
│ │ └── security_validator_mcp.py # Compliance validator
│ ├── mcp_server.py # MCP 2.x server
│ └── main.py # App factory + lifespan
├── data/
│ ├── cases.csv # 32 benchmark cases
│ ├── diagnosis_comparison.csv # Model vs ground truth
│ ├── responsible_ai_log.csv # Every rejection and edit, with reasons
│ └── reviews.json # Full audit trail
├── prompts/
│ ├── diagnose_prompt.md # Strict JSON diagnostic prompt, 3 worked examples
│ └── chat_prompt.md # Conversational tutor prompt
├── scripts/
│ └── generate_diagnosis_comparison.py
├── static/ # SPA frontend
├── tests/
├── checker.py # Headless CLI rule runner
└── requirements.txtMIT. All scenarios are for educational use in Packet Tracer and virtual lab environments — not for production networks.
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
MCP server for secureFlows: token-free URL builders and integration-linting tools for AI agents.
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
MEOK MCP Hardening MCP — automated security red-team for any MCP server. Maps OWASP LLM Top 10
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Gyanprakash136/NextsageAI'
If you have feedback or need assistance with the MCP directory API, please join our Discord server