pip install simba-mcp

uvx simba-mcp

{
  "mcpServers": {
    "simba": {
      "command": "uvx",
      "args": ["simba-mcp"],
      "env": {
        "SIMBA_API_URL": "https://demo.simba-mmm.com",
        "SIMBA_API_KEY": "simba_sk_..."
      }
    }
  }
}

{
  "mcpServers": {
    "simba": {
      "command": "uvx",
      "args": ["simba-mcp"],
      "env": {
        "SIMBA_API_URL": "https://demo.simba-mmm.com",
        "SIMBA_API_KEY": "simba_sk_..."
      }
    }
  }
}

import anthropic

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "List my Simba models"}],
    mcp_servers=[
        {
            "type": "url",
            "url": "https://demo.simba-mmm.com/mcp",
            "name": "simba",
            "authorization_token": "simba_sk_...",
        }
    ],
    tools=[{"type": "mcp_toolset", "mcp_server_name": "simba"}],
    betas=["mcp-client-2025-11-20"],
)

// Wrong
"period_cpm": {"TV": 10}

// Correct
"period_cpm": {"TV": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]}

import math
for row in scenario_data:
    for key, val in row.items():
        if val is None or (isinstance(val, float) and math.isnan(val)):
            row[key] = 0

import requests, time

BASE = "https://demo.simba-mmm.com"
HEADERS = {"Authorization": "Bearer simba_sk_..."}

# Upload data
with open("marketing_data.csv", "rb") as f:
    r = requests.post(f"{BASE}/api/v1/ingest",
                      headers={**HEADERS, "Content-Type": "text/csv"},
                      data=f.read(), params={"name": "q1_data"})
file_id = r.json()["id"]

# Create model
r = requests.post(f"{BASE}/api/v1/models", headers=HEADERS, json={
    "data_source": {"uploaded_file_id": file_id},
    "date_column": "date",
    "kpi_column": "revenue",
    "hierarchy_column": "brand",
    "channels": [
        {"name": "TV", "activity_column": "tv_grps", "spend_column": "tv_spend"},
        {"name": "Search", "activity_column": "search_impressions", "spend_column": "search_spend"},
    ],
    "total_media_effect": "Retail",
})
model_hash = r.json()["model_hash"]

# Poll until complete
while True:
    status = requests.get(f"{BASE}/api/v1/models/{model_hash}/status",
                          headers=HEADERS).json()
    if status["status"] in ("complete", "failed"):
        break
    print(f"Fitting... {status.get('progress', '?')}%")
    time.sleep(10)

# Get results
results = requests.get(f"{BASE}/api/v1/models/{model_hash}/results",
                       headers=HEADERS,
                       params={"sections": "channel_summary,model_stats"}).json()
for ch in results["results"]["channel_summary"]:
    print(f"{ch['Channel']}: ROI {ch['ROI']:.1f}")

API_KEY="simba_sk_..."
BASE="https://demo.simba-mmm.com"

# Upload data
curl -X POST "$BASE/api/v1/ingest?name=q1_data" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: text/csv" \
  --data-binary @marketing_data.csv

# Create model (replace uploaded_file_id with id from upload)
curl -X POST "$BASE/api/v1/models" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"data_source": {"uploaded_file_id": 1}, "date_column": "date", "kpi_column": "revenue", "hierarchy_column": "brand", "channels": [{"name": "TV", "activity_column": "tv_grps", "spend_column": "tv_spend"}]}'

# Poll status (replace MODEL_HASH)
curl "$BASE/api/v1/models/MODEL_HASH/status" -H "Authorization: Bearer $API_KEY"

# Get results
curl "$BASE/api/v1/models/MODEL_HASH/results?sections=channel_summary,model_stats" \
  -H "Authorization: Bearer $API_KEY"

# stdio (default) — for Cursor, Claude Code
simba-mcp

# Streamable HTTP — for remote deployment
simba-mcp --transport streamable-http --port 8100

# SSE — legacy transport
simba-mcp --transport sse --port 8100

# Or via uvicorn directly
uvicorn simba_mcp.server:app --host 0.0.0.0 --port 8100