MCP Vertica

The MCP Vertica server provides a local NLP-to-SQL interface for Vertica databases, running entirely locally with no authentication required. Key capabilities include:

• Execute SQL queries directly against Vertica with streaming results and batching options • Natural language to SQL conversion using a local LLM (Ollama) for both data queries and DDL operations like table creation • Data loading via bulk COPY operations into Vertica tables • Database introspection to retrieve table structures, columns, types, constraints, indexes, and views • Similar incident analysis to find incidents based on ID or text description • Multiple access methods: REST API at http://127.0.0.1:8001/api/, terminal commands via uvx mcp-vertica nlp, and SSE MCP server on port 8000 • Dry-run capabilities to preview generated SQL without execution • Local deployment using Docker and Ollama for the complete stack

mcp-vertica — Local NLP + REST for Vertica (no auth)

This runs entirely on your laptop: Vertica CE via Docker, a local REST API, and a terminal NLP→SQL command powered by a local LLM (Ollama). No auth, bound on 0.0.0.0 for convenience.

⚠️ Security is intentionally disabled for local demos. Do not expose to the public internet.

Prerequisites

Docker Desktop
Python 3.12+
uv (recommended) or pip
Ollama (for local LLM)
- Mac: brew install ollama → ollama serve & → ollama pull llama3.1:8b
- Windows: install Ollama app → run “Ollama” → in PowerShell: ollama pull llama3.1:8b
(Optional) A Vertica instance; we provide Docker.

1) Start Vertica locally

docker compose up -d
# Wait until healthy (30–60s)
docker ps

Defaults:

Host: localhost

Port: 5433

Database: VMart

User: dbadmin

Password: (empty)

2) Install & configure mcp-vertica

# Mac/Linux (uv)
uv sync
# Or pip:
# python -m venv .venv && source .venv/bin/activate
# pip install -e .

Set env (Mac/Linux bash or zsh):

export VERTICA_HOST=127.0.0.1
export VERTICA_PORT=5433
export VERTICA_DATABASE=VMart
export VERTICA_USER=dbadmin
export VERTICA_PASSWORD=""
export VERTICA_CONNECTION_LIMIT=10

Windows (PowerShell):

$env:VERTICA_HOST="127.0.0.1"
$env:VERTICA_PORT="5433"
$env:VERTICA_DATABASE="VMart"
$env:VERTICA_USER="dbadmin"
$env:VERTICA_PASSWORD=""
$env:VERTICA_CONNECTION_LIMIT="10"

3) Seed ITSM/CMDB sample data

python scripts/seed_itsm.py
# Creates schemas itsm/cmdb and loads ~2k incidents + CIs/changes/relations

4) REST API (no auth)

uvx mcp-vertica serve-rest --host 0.0.0.0 --port 8001

Test:

curl http://127.0.0.1:8001/api/health
curl -X POST http://127.0.0.1:8001/api/query \
  -H 'Content-Type: application/json' \
  -d '{"sql":"SELECT COUNT(*) AS n FROM itsm.incident;"}'

NLP endpoint:

curl -X POST http://127.0.0.1:8001/api/nlp \
  -H 'Content-Type: application/json' \
  -d '{"question":"Top 5 incident categories this month", "execute": true}'

5) NLP from terminal

Start Ollama in background (if not already):

ollama serve &
ollama pull llama3.1:8b

Examples:

# Ask anything; will generate Vertica SQL and run it
uvx mcp-vertica nlp ask "Top 5 incident categories this month by count"

# Create a table (mutations allowed)
uvx mcp-vertica nlp ask "Create table staging.high_prio_incidents as P1 incidents last 7 days"

# Dry-run (just show SQL)
uvx mcp-vertica nlp ask --dry-run "List incidents joined to CI class and change window overlap"

# Similar incidents
uvx mcp-vertica nlp similar --incident-id INC000123
uvx mcp-vertica nlp similar --text "database timeout in payment service" --top-k 10

6) SSE MCP server (unchanged)

uvx mcp-vertica --port 8000  # runs SSE (0.0.0.0)

Troubleshooting

If MCP client can’t connect: uv cache clean and retry.

If Vertica not ready: docker logs vertica-ce and re-run after healthy.

If Ollama fails: ensure ollama serve is running and you pulled a model.

License

MIT