Simba MCP Server

Simba is a Bayesian Marketing Mix Modeling (MMM) platform. This Marketing Mix Modeling MCP server lets AI assistants interact with your models directly — upload data, build models, check results, and run budget optimizations through natural language in Claude, Cursor, or Claude Code.

Installation

pip install simba-mcp

Or run directly without installing:

uvx simba-mcp

Quick Start

Cursor IDE

Add to your Cursor MCP settings (.cursor/mcp.json in the workspace or global settings):

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

Claude Code

Add to your Claude Code MCP config:

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

Claude API (MCP Connector)

Use the remote Streamable HTTP transport with the Anthropic MCP connector:

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"],
)

Available Tools

Tool	Description
get_data_schema	Get the canonical CSV schema for MMM input files
upload_data	Upload a CSV dataset to Simba
list_models	List all models with their status
create_model	Configure and start fitting a new MMM model
get_model_status	Poll fitting progress for a model
get_model_results	Get results (ROI, contributions, response curves, diagnostics, and more)
run_optimizer	Run budget optimization on a completed model
get_optimizer_results	Get optimizer status and results
get_scenario_template	Generate a forward-period template for scenario planning
run_scenario	Run a "what-if" scenario prediction
get_scenario_results	Get scenario prediction results

Example Prompts

Try these with any connected AI assistant:

Explore your models:

"List my Simba models and show me the channel ROI summary for the most recent complete model."

Build a model:

"Upload this CSV data to Simba and create a new MMM model with TV, Search, and Social as media channels. Use 'revenue' as the KPI and 'date' as the date column."

Check progress:

"What's the fitting status of model a1b2c3d4?"

Get results:

"Show me the model diagnostics and channel contributions for model a1b2c3d4."

Optimize budget:

"Run a budget optimization on model a1b2c3d4 with $1M total budget over 12 months. Set TV bounds to 5-40% and Search to 10-50%. Use uniform laydown weights."

Response curves:

"Show me the response curves for model a1b2c3d4. At what spend level does TV hit diminishing returns?"

Scenario planning:

"Get a scenario template for model a1b2c3d4 for the next 12 weeks. Then run a scenario where I increase TV by 20% and cut Search by 10%. What happens to revenue?"

Full workflow:

"I have marketing data I want to analyze. First get the schema so I know what format is needed, then upload my data, create a model, and once it's done show me the ROI by channel."

Gotchas & Tips

Things that commonly trip up both AI agents and humans:

Channel names are exact-match

Model results are keyed by the channel's activity column name (e.g. "search_activity", "TV_impressions"), not by the channels[].name you passed to create_model. Keys can contain spaces and matching is case-sensitive and space-sensitive — the optimizer and scenario tools use them as dictionary keys.

Always call get_model_results with sections="channel_summary" first to see exact channel keys, then use those verbatim in optimizer/scenario payloads.

Results sections

get_model_results serves these sections (request only what you need via sections=): channel_summary, contributions (KPI/unit space — multiplier not applied), coefficients (per-period per-channel revenue table), params, decay_curves, response_curves, marginal_curves, saturation, mroi_summary (marginal ROI at current spend with 94% HDI; post-#591 fits add the allperiods_unweighted / spendweighted_active convention scalars, and post-#629 fits add a *_mean beside every *_median — the median is displayed, the mean is what reconciles with the marginal-revenue curve), mroi_periods (opt-in only — the per-period marginal ROI series; never in the default payload, request it by name), model_stats, actual_vs_model, long_run_rollup, optimizer, predictions, posterior, financials, model_config. The response's sections_available field is authoritative if the server is newer than these docs.

Models are identified by model_hash

All model endpoints use the string model_hash (e.g. "f835671a25") returned by create_model and list_models.

API-key management is deliberately not exposed

The /api/v1/keys endpoints (create/list/revoke API keys) are session-auth only and have no MCP tools by design: a server holding one key must not be able to mint or revoke keys. Manage keys in the Simba UI (Profile → API Keys).

Optimizer arrays, not scalars

laydown_weights and period_cpm must be objects of arrays, each array having exactly num_periods elements:

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

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

The same channel keys must appear in bounds, laydown_weights, and period_cpm. Bounds values are percentages (0-100) of total_budget, not currency amounts.

Clean NaN from scenario templates

The template from get_scenario_template may contain NaN/null for channels without historical data. Replace them with 0 before passing to run_scenario:

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

Three endpoints are async

These return 202 and require polling:

Action	Start	Poll
Fit model	create_model	get_model_status
Optimize	run_optimizer	get_optimizer_results
Scenario	run_scenario	get_scenario_results

Poll every 5-10 seconds. Check the status field for "complete" or "failed".

Data upload requirements

• CSV only (not Excel). Maximum 10 MB (API-enforced).
• Row minimum: check get_data_schema → x-simba-constraints.min_rows; the upload response's warnings field is authoritative. More rows = tighter posteriors (104+ weekly rows recommended).
• Media columns: {channel}_activity and {channel}_spend per channel.
• Use 0 for inactive periods, not blank or NA.
• Large file? Pass csv_path (a local file path) instead of csv_content — the server reads it directly instead of the CSV going through the conversation. Local (stdio) servers only; disabled on HTTP/SSE deployments unless SIMBA_MCP_ALLOW_LOCAL_FILES=1.

Common Errors

Error	Cause	Fix
Authentication required	No API key or expired key	Check SIMBA_API_KEY env var
API key missing required scope: <scope>	Key doesn't have the needed scope	Create a key with all scopes
Missing required fields: [...]	Payload missing required keys	Check the tool's parameter list
Model status is '<status>'. Optimization requires a 'complete' model.	Model still fitting or failed	Poll get_model_status until complete
laydown_weights['TV'] must be an array of length 12	Scalar instead of array, or wrong length	Use arrays matching num_periods
period_cpm['TV'] values must all be positive	Zero or negative CPM	All CPM values must be > 0
Channels in bounds missing from period_cpm: [...]	Mismatched channel names	Same keys in bounds, laydown_weights, and period_cpm
Columns not found in data: [...]	Column name typo	Check CSV headers match exactly
File exceeds 10 MB limit	CSV too large	Reduce file size or aggregate data

Direct API Access

The MCP server wraps the Simba REST API. For scripting, CI/CD, or environments without MCP, you can call the API directly.

When to use MCP vs direct API

	MCP (via AI assistant)	Direct API (curl / Python)
Best for	Exploratory analysis, conversational workflows	Automated pipelines, scheduled jobs, scripts
Async polling	Assistant handles it automatically	You implement poll-until-complete logic
Data cleaning	Assistant cleans NaN/null, builds payloads	You write the data prep code
Reproducibility	Conversational	Scriptable, version-controlled

Both use the same API keys with the same scopes.

Quick start (Python)

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}")

Quick start (curl)

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"

API Key Setup

The MCP server authenticates with the same API keys used by the Simba REST API. Create a key with the required scopes:

1. Go to Profile > API Keys in the Simba UI
2. Click Create Key
3. Set scopes: ingest, read:models, read:results, create:models, optimize, scenario
4. Copy the key (shown only once)

Set the key as the SIMBA_API_KEY environment variable in your MCP config.

Configuration

Environment Variable	Description	Default
SIMBA_API_URL	Simba API base URL	http://localhost:5005
SIMBA_API_KEY	Your Simba API key	(required)

Transport Modes

The server supports all MCP transport modes:

# 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

License

MIT