sablier-mcp 0.12.2

Sablier MCP — plug Wall Street-grade portfolio analytics, stress-testing, and SEC filing intelligence into any AI assistant. What takes quants & analysts weeks, done in 2 minutes.

Sablier MCP Server

An MCP (Model Context Protocol) server that lets AI assistants analyze portfolios, stress-test scenarios, generate synthetic market paths, and scan SEC filings — in under 2 minutes.

What This Does

Connect Sablier to Claude, ChatGPT, or any MCP-compatible AI assistant. The agent gets ~70 tools to:

Scan SEC filings & earnings calls — AI reads every company's 10-K, 10-Q filings and earnings call transcripts, then scores how exposed each holding is to any theme you ask about (0-100 scale with evidence). "How exposed is my portfolio to China supply chain risk?" — scored, evidenced, and ranked in seconds.

Compute factor exposures — Measures how each stock responds to market drivers (interest rates, VIX, dollar index, oil, credit spreads, etc.). Factor betas are estimated on a rolling window of recent data so they reflect current market conditions. Supports both linear and nonlinear (GAM) factor models.

Stress-test with scenarios — "What if VIX hits 40?" or "What if the Fed raises rates to 6%?" Run Monte Carlo simulations to get per-asset expected returns, Value-at-Risk, Expected Shortfall, and full return distributions.

Generate synthetic market paths — Train a generative flow model on the joint distribution of assets and factors. Generate hundreds of realistic future trajectories, optionally constrained ("paths where gold stays above $3000"). Forward-test strategies across many scenarios.

Real-time market intelligence — Get a Bloomberg-terminal-grade briefing with 50+ indicators, z-scores, regime signals, and cross-asset analysis in one call.

Manage portfolios — Create and track portfolios with live prices, performance analytics (Sharpe ratio, max drawdown, volatility), optimization, and efficient frontier computation.

Speed

Under 2 minutes, end-to-end. Portfolio creation -> model training -> factor betas -> stress scenarios -> SEC filing analysis. All in a single conversation.

The same workflow — gathering filings, building factor models, running simulations, writing risk memos — takes a team of analysts and quants days to weeks. Sablier compresses it into one chat.

Quick Start

Option A: Claude Desktop / Claude.ai (recommended — zero install)

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "sablier": {
      "type": "url",
      "url": "https://sablier-mcp-215397666394.us-central1.run.app/mcp/"
    }
  }
}

That's it. On first use, Claude opens a browser window — sign up or log in with your Sablier account. No API keys needed.

Option B: ChatGPT (Developer Mode)

1. Go to Settings -> Developer Mode and enable it
2. Go to Connectors -> Add connector
3. Enter:
   • Name: Sablier
   • Server URL: https://sablier-mcp-215397666394.us-central1.run.app/mcp/
4. In a new chat, activate via "+" -> More -> Developer Mode

Option C: Claude Code (local, stdio)

# Clone and install
git clone https://github.com/sablier-it/sablier-mcp.git
cd sablier-mcp
uv sync

# Register with Claude Code
claude mcp add sablier -- uv --directory /path/to/sablier-mcp run sablier-mcp

# Set your API key (get one from sablier-ai.com)
export SABLIER_API_KEY=sk_live_your_key_here

Tools (~70)

Market Intelligence

Tool	Description
market_radar	Bloomberg-grade briefing: 50+ indicators, z-scores, regime signals, cross-asset analysis
search_features	Search for tickers (stocks, ETFs, futures) and market indicators (VIX, DXY, rates)

Portfolio Management

Tool	Description
create_portfolio	Create a portfolio from tickers and weights (must sum to 1.0)
list_portfolios	List your portfolios with names, assets, and status
get_portfolio	Get full details for a specific portfolio
update_portfolio	Update name, description, weights, or capital
delete_portfolio	Permanently delete a portfolio
get_portfolio_value	Live portfolio value: total, P&L, per-position breakdown
get_portfolio_analytics	Sharpe ratio, volatility, max drawdown, beta (1W-5Y timeframes)
get_asset_profiles	Sector, industry, country, and exchange for each holding
optimize_portfolio	Find optimal weights: max Sharpe, min variance, or max return
get_efficient_frontier	Compute the efficient frontier curve for portfolio assets

Qualitative Analysis (SEC Filings & Earnings Calls)

Tool	Description
analyze_qualitative	Score company exposure to any theme (0-100) using 10-K, 10-Q, and earnings transcripts
list_themes	Browse the built-in theme library (AI risk, rate sensitivity, China exposure, etc.)
list_grain_analyses	List past qualitative analyses
get_grain_analysis	Load a saved analysis with full scores and evidence passages
delete_grain_analysis	Delete a saved qualitative analysis

Quantitative Analysis (Factor Models)

Tool	Description
analyze_quantitative	One-shot: builds factor models, trains, computes factor exposures (linear + nonlinear)
list_model_groups	List existing analyses with training and simulation status
list_feature_set_templates	Browse pre-built market driver sets (rates, volatility, commodities, credit, etc.)
create_feature_set	Create a custom set of market drivers for analysis
list_feature_sets	List all accessible feature sets
get_feature_set	Get details of a specific feature set
delete_feature_set	Delete a custom feature set
compute_betas	Compute per-asset factor betas from a trained model group
compute_returns	Sample forward asset returns under user-specified factor levels (Monte Carlo)
get_residual_correlation	Cross-asset residual correlation matrix (post-factor)
list_simulations	List all simulations for a model group
optimize_portfolio	Find optimal weights from a trained MOMENT batch (analytical objectives)
get_optimization_history	Browse past optimization runs for a portfolio
delete_model_group	Delete a model group and all associated data

Stress Testing & Scenarios

Tool	Description
compute_returns	Monte Carlo what-if: per-asset VaR, ES, expected return under custom factor levels
create_scenario	Save a named what-if scenario (fixed value, percentile, or shock)
list_scenarios	List saved scenarios
get_scenario	Get scenario details
update_scenario	Update a scenario's factors or description
delete_scenario	Delete a saved scenario

Generative Simulation (Flow)

Tool	Description
train_flow_model	Train a conditional flow-matching model on a model group
generate_flow_paths	Generate baseline (unconstrained) synthetic price paths
simulate_flow_scenario	Generate constrained paths (e.g., "gold above $3000 and VIX below 20")
check_flow_job	Poll status of a queued/running flow job
get_flow_results	Pull paths + summary stats from a completed generation job
download_flow_paths	Download raw paths as JSON or CSV
list_flow_baselines	List baseline path generations for a model group
list_flow_scenarios	List scenario-conditioned generations for a model group
delete_flow_job	Delete a flow job and its outputs
test_flow_risk	Compute portfolio risk metrics across generated paths
flow_validate	Validate flow model quality against historical data (calibration, dependence, tails)

Feature Catalog

Tool	Description
search_features	Search the catalog for available tickers / macro series
add_feature	Add a ticker to the catalog (Yahoo Finance or FRED)
refresh_feature_data	Fetch/update historical data for tickers

Trading Rules

Tool	Description
create_rule	Create a trigger-action rule on a portfolio (VIX spike, drawdown, regime, etc.)
list_rules	List rules attached to a portfolio
toggle_rule	Activate or deactivate a rule
delete_rule	Permanently delete a rule
evaluate_rules	Check which rules would fire on the latest market data
backtest_rules	Historical backtest of rules against real prices
forward_test_rules	Forward-test rules against generated FLOW paths

Derivatives

Tool	Description
analyze_derivatives	Greeks aggregation + scenario P&L for portfolio options positions
price_option_tool	Price a single equity / futures option (Black-76) with Greeks

Market Data

Tool	Description
market_radar	60+ regime indicators (VIX, yield curve, credit, sector rotation, RORO)
screen_universe	Cross-sectional screening across the equity catalog

Account & Billing

Tool	Description
whoami	Current user, tier, and feature flags
get_credits	Current credit balance and tier monthly allocation
list_credit_packs	Browse one-off credit packs available for purchase
buy_credit_pack	Purchase a credit pack via Stripe Checkout
get_billing_info	View current subscription tier and limits
get_billing_usage	View usage across metered buckets
toggle_overage	Enable/disable per-call overage spending
subscribe	Subscribe or upgrade via Stripe Checkout
manage_subscription	Open Stripe Customer Portal

Example Conversations

1. Full risk analysis in one conversation

You:   Create a portfolio with AAPL 40%, MSFT 30%, NVDA 30%.
       Then stress-test it for a recession — VIX at 35, 10Y at 5.5%, SPY at 380.

Agent: 1. create_portfolio("Tech Portfolio", ["AAPL", "MSFT", "NVDA"], [0.4, 0.3, 0.3])
       2. list_feature_set_templates()  →  picks "Macro + Volatility" set
       3. analyze_quantitative(portfolio_id, conditioning_set_id)
          →  trains models, computes factor betas per asset
       4. compute_returns(sim_batch_id, {"VIX": 35, "US 10Y": 5.5, "SPY": 380})
          →  per-asset expected returns, VaR (95%), Expected Shortfall

       Result: Portfolio expected return = -8.2%, VaR(95%) = -14.5%
       NVDA most exposed (-12.1%), MSFT most defensive (-4.8%)

2. SEC filing analysis for thematic risk

You:   How exposed are the Magnificent 7 to AI regulation risk?

Agent: 1. analyze_qualitative(
            tickers=["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"],
            themes=["AI regulation risk"]
       )
       2. Returns per-ticker scores (0-100) with evidence from 10-K filings:
          - META: 82/100 (HIGH) — "government regulation of AI... could limit our ability
            to deploy AI features across our family of apps"
          - GOOGL: 78/100 — "new AI regulations could require us to restrict or modify
            products and services"
          - NVDA: 71/100 — "export controls... restrictions on AI chip sales to China"
          - TSLA: 31/100 (LOW) — limited AI regulation mentions in filings

3. Generative scenario analysis

You:   I want to see how a gold + bonds portfolio performs over the next quarter
       in scenarios where inflation stays high.

Agent: 1. create_portfolio("Inflation Hedge", ["GLD", "TLT", "IAU"], [0.4, 0.4, 0.2])
       2. train_flow_model(model_group_id, horizon=60)
          →  dispatches GPU training job (~5-15 min async)
       3. check_flow_job(job_id) until completed; flow_validate(model_group_id)
          →  confirms model quality before generating paths
       4. generate_flow_paths(model_group_id, n_paths=500)
          →  generates 500 joint baseline price paths
       5. simulate_flow_scenario(model_group_id, constraints=[
            {"feature_name": "CPI", "type": "level", "lower": 3.5, "t_start": 0, "t_end": 60}
          ])
          →  generates paths conditioned on CPI > 3.5%
       6. test_flow_risk(portfolio_id, job_id)
          →  distribution of Sharpe, max drawdown, total return across constrained paths

       Result: Median return = +4.2%, 5th percentile = -6.8%
       Gold outperforms bonds in 72% of high-inflation paths

4. Market briefing and portfolio checkup

You:   What's happening in markets today and how is my portfolio positioned?

Agent: 1. market_radar()
          →  50+ indicators: equities, rates, credit, FX, commodities, volatility
          →  regime signals: Risk-Off score = 0.7, yield curve inverted, VIX elevated
       2. list_portfolios()  →  finds your "Tech Portfolio"
       3. get_portfolio_value(portfolio_id)
          →  current value, daily P&L, per-position breakdown
       4. get_portfolio_analytics(portfolio_id, timeframe="1M")
          →  1-month Sharpe, volatility, max drawdown

       Result: Markets are risk-off (VIX +15% this week, credit spreads widening).
       Your tech portfolio is down -2.3% today, concentrated in high-beta names.
       Consider: stress-test with compute_returns to quantify downside risk.

Authentication

• Remote mode (Claude Desktop, Claude.ai, ChatGPT): OAuth 2.0 browser-based login — no API keys to manage. Sablier authenticates via Google OAuth, then issues an API key for the MCP session.
• Local mode (Claude Code, stdio): Set SABLIER_API_KEY environment variable.

Pricing

Tier	Price	Included
Free	$0	10 market radar, 5 factor models, 2 GRAIN analyses/mo
Pro	$79/mo	100 market radar, 50 factor models, 20 GRAIN, 10 Flow sims/mo
Enterprise	$399/mo/seat	Unlimited everything, priority support

Portfolio management, read operations, and scenario management are always free. Overages billed per-call beyond included limits.

Architecture

sablier-mcp/
├── src/sablier_mcp/
│   ├── server.py      # 53 MCP tool definitions (FastMCP)
│   ├── client.py      # Async HTTP client for Sablier API
│   ├── auth.py        # OAuth 2.0 provider (remote mode)
│   └── widgets.py     # Rich HTML cards for Claude Desktop
├── pyproject.toml
├── Dockerfile
└── README.md

• Remote mode (Claude Desktop, Claude.ai, ChatGPT): OAuth 2.0 browser login — no API keys to manage
• Local mode (Claude Code, stdio): API key from environment variable
• Widgets: Tools return rich HTML cards (beta heatmaps, score cards, portfolio overviews) alongside text for visual output in Claude Desktop

Development

# Run the server locally (stdio transport)
uv run sablier-mcp

# Test with MCP inspector
npx @modelcontextprotocol/inspector uv --directory . run sablier-mcp

# Run as remote server (streamable-http with OAuth)
MCP_TRANSPORT=streamable-http uv run sablier-mcp

Privacy Policy

Sablier processes portfolio data and market queries to provide analytics. Full details:

• Data collected: Portfolio holdings, factor model parameters, and query metadata for analytics computation
• Usage & storage: Data is processed on Sablier's servers (GCP, US) and stored for your account's analytics history. Portfolios and models persist until you delete them.
• Third-party sharing: Sablier does not sell or share your data. Market data is sourced from public feeds. SEC filings are public records.
• Retention: Account data is retained while your account is active. Deleted portfolios and models are purged within 30 days.
• Contact: [e-mail]

Full privacy policy: sablier-ai.com/privacy | Terms of service: sablier-ai.com/terms

Support

• Email: [e-mail]
• Issues: github.com/sablier-it/sablier-mcp/issues

Links

• Sablier Platform: sablier-ai.com
• MCP Protocol: modelcontextprotocol.io