📊 Toolstem — Financial Data MCP for AI Agents | Stock Analysis & DCF

./LICENSE

Curated financial data MCP for AI agents — equity research in one call.

Toolstem is the financial data MCP built for AI stock analysis, equity research, and agent-driven investment workflows. Real-time stock data, company fundamentals, DCF valuations, financial metrics, and the ability to compare companies side-by-side — all returned as flat, agent-friendly JSON with derived signals already computed.

Works natively with Claude, OpenAI Agents SDK, and LangChain. Pay-per-call pricing, no subscription. More finance MCP servers (SEC filings, insider transactions, institutional holdings) are on the way.

Unlike passthrough wrappers that just expose a vendor's REST API, every Toolstem tool combines multiple data sources, derives signals, and pre-computes the math an agent would otherwise have to do itself.

One call. One agent-friendly JSON response. No nested arrays to parse, no cross-endpoint stitching, no null-checking boilerplate.

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Try It Now (30 seconds)

Apify Console — click Run with default input. AAPL stock snapshot returns in ~3 seconds, no charge:

https://apify.com/toolstem/toolstem-mcp-server

Claude Desktop — drop into your claude_desktop_config.json:

{
  "mcpServers": {
    "toolstem": {
      "command": "npx",
      "args": ["-y", "toolstem-mcp-server"],
      "env": { "FMP_API_KEY": "your-key-from-financialmodelingprep.com" }
    }
  }
}

Restart Claude. Ask: "Use Toolstem to get a snapshot of NVDA."

npm — npx toolstem-mcp-server runs the server locally over stdio.

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Why Toolstem?

Most financial MCP servers expose one tool per API endpoint — forcing your agent to make 4–5 sequential calls, write glue code, and reason about raw data shapes. Toolstem is built differently:

• Parallel data fetching — every tool fans out to multiple sources concurrently.
• Derived signals — human-readable recommendations like UNDERVALUED, STRONG, ACCELERATING computed from raw numbers.
• Pre-computed math — CAGRs, YoY growth, margin trends, distance from 52-week high/low, FCF yield, and more are already in the response.
• Flat, predictable schema — no deeply nested vendor quirks leaking into agent prompts.
• Graceful degradation — if one upstream endpoint fails, the rest of the response still comes through with nulls in place.

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Tools

get_stock_snapshot

Comprehensive stock overview combining quote, profile, DCF valuation, and rating into a single response.

Input:

{
  "symbol": "AAPL"
}

Example output (truncated):

{
  "symbol": "AAPL",
  "company_name": "Apple Inc.",
  "sector": "Technology",
  "industry": "Consumer Electronics",
  "exchange": "NASDAQ",
  "price": {
    "current": 178.52,
    "change": 2.34,
    "change_percent": 1.33,
    "day_high": 179.80,
    "day_low": 175.10,
    "year_high": 199.62,
    "year_low": 130.20,
    "distance_from_52w_high_percent": -10.57,
    "distance_from_52w_low_percent": 37.11
  },
  "valuation": {
    "market_cap": 2780000000000,
    "market_cap_readable": "$2.78T",
    "pe_ratio": 29.5,
    "dcf_value": 195.20,
    "dcf_upside_percent": 9.35,
    "dcf_signal": "FAIRLY VALUED"
  },
  "rating": {
    "score": 4,
    "recommendation": "Buy",
    "dcf_score": 5,
    "roe_score": 4,
    "roa_score": 4,
    "de_score": 5,
    "pe_score": 3
  },
  "fundamentals_summary": {
    "beta": 1.28,
    "avg_volume": 55000000,
    "employees": 164000,
    "ipo_date": "1980-12-12",
    "description": "Apple Inc. designs, manufactures..."
  },
  "meta": {
    "source": "Toolstem via Financial Modeling Prep",
    "timestamp": "2026-04-17T18:30:00Z",
    "data_delay": "End of day"
  }
}

Derived fields (not in raw APIs):

• dcf_signal — UNDERVALUED if DCF upside > 10%, OVERVALUED if < -10%, else FAIRLY VALUED.
• market_cap_readable — human-friendly $2.78T, $450.2B, $12.5M format.
• distance_from_52w_high_percent / distance_from_52w_low_percent — pre-computed range position.

---

get_company_metrics

Deep fundamentals analysis — profitability, financial health, cash flow, growth, and per-share metrics — synthesized from 5 financial statements endpoints.

Input:

{
  "symbol": "AAPL",
  "period": "annual"
}

period accepts annual (default) or quarter.

Example output (truncated):

{
  "symbol": "AAPL",
  "period": "annual",
  "latest_period_date": "2025-09-30",
  "profitability": {
    "revenue": 394328000000,
    "revenue_readable": "$394.3B",
    "revenue_growth_yoy": 7.8,
    "net_income": 96995000000,
    "net_income_readable": "$97.0B",
    "gross_margin": 46.2,
    "operating_margin": 31.5,
    "net_margin": 24.6,
    "roe": 160.5,
    "roa": 28.3,
    "roic": 56.2,
    "margin_trend": "EXPANDING"
  },
  "financial_health": {
    "total_debt": 111000000000,
    "total_cash": 65000000000,
    "net_debt": 46000000000,
    "debt_to_equity": 1.87,
    "current_ratio": 1.07,
    "interest_coverage": 41.2,
    "health_signal": "STRONG"
  },
  "cash_flow": {
    "operating_cash_flow": 118000000000,
    "free_cash_flow": 104000000000,
    "free_cash_flow_readable": "$104.0B",
    "fcf_margin": 26.4,
    "capex": 14000000000,
    "dividends_paid": 15000000000,
    "buybacks": 89000000000,
    "fcf_yield": 3.7
  },
  "growth_3yr": {
    "revenue_cagr": 8.2,
    "net_income_cagr": 10.1,
    "fcf_cagr": 9.5,
    "growth_signal": "ACCELERATING"
  },
  "per_share": {
    "eps": 6.42,
    "book_value_per_share": 3.99,
    "fcf_per_share": 6.89,
    "dividend_per_share": 0.96,
    "payout_ratio": 14.9
  },
  "meta": {
    "source": "Toolstem via Financial Modeling Prep",
    "timestamp": "2026-04-17T18:30:00Z",
    "periods_analyzed": 3,
    "data_delay": "End of day"
  }
}

Derived fields:

• margin_trend — EXPANDING, STABLE, or CONTRACTING based on net margin series direction.
• health_signal — STRONG, ADEQUATE, or WEAK from debt-to-equity, current ratio, and interest coverage.
• growth_signal — ACCELERATING, STEADY, or DECELERATING based on YoY growth trajectory.
• revenue_cagr, net_income_cagr, fcf_cagr — compound annual growth rates over the analyzed window.
• fcf_margin, fcf_yield — pre-computed from cash flow + revenue + market cap.

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compare_companies

Side-by-side comparison of 2–5 companies across price, valuation, profitability, financial health, growth, dividends, and analyst ratings.

Input:

{
  "symbols": ["AAPL", "MSFT", "GOOGL"]
}

Example output (truncated):

{
  "symbols_compared": ["AAPL", "MSFT", "GOOGL"],
  "comparison_date": "2026-04-20T18:30:00Z",
  "companies": [
    {
      "symbol": "AAPL",
      "company_name": "Apple Inc.",
      "sector": "Technology",
      "price": { "current": 178.52, "change_percent": 1.33 },
      "valuation": { "pe_ratio": 29.5, "dcf_upside_percent": 9.35 },
      "profitability": { "net_margin": 24.6, "roe": 160.5, "roic": 56.2 },
      "financial_health": { "debt_to_equity": 1.87, "current_ratio": 1.07 },
      "growth": { "revenue_growth_yoy": 7.8, "earnings_growth_yoy": 10.1 },
      "dividend": { "dividend_yield": 0.5, "payout_ratio": 14.9 },
      "rating": { "score": 4, "recommendation": "Buy" }
    }
  ],
  "rankings": {
    "lowest_pe": "GOOGL",
    "highest_margin": "AAPL",
    "strongest_balance_sheet": "GOOGL",
    "best_growth": "MSFT",
    "most_undervalued": "GOOGL",
    "highest_rated": "MSFT"
  },
  "meta": {
    "source": "Toolstem via Financial Modeling Prep",
    "timestamp": "2026-04-20T18:30:00Z",
    "data_delay": "Real-time during market hours",
    "api_calls_made": 19
  }
}

Derived fields:

• rankings — automatically computed: lowest_pe, highest_margin, strongest_balance_sheet, best_growth, most_undervalued, highest_rated.
• All valuation, profitability, health, and growth metrics pre-computed per company.
• Uses batch quote for efficient multi-symbol price retrieval.

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Installation

npm

$npm install -g toolstem-mcp-server

Run as stdio server:

$FMP_API_KEY=your_key_here toolstem-mcp-server

Run as HTTP (Streamable HTTP transport) server:

$FMP_API_KEY=your_key_here PORT=3000 toolstem-mcp-server --http

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "toolstem": {
      "command": "npx",
      "args": ["-y", "toolstem-mcp-server"],
      "env": {
        "FMP_API_KEY": "your_fmp_api_key"
      }
    }
  }
}

Apify

Available on the Apify Store as the toolstem-financial-data Actor. Call it from your Apify workflow with input:

{
  "tool": "get_stock_snapshot",
  "symbol": "AAPL"
}

or

{
  "tool": "compare_companies",
  "symbols": ["AAPL", "MSFT", "GOOGL"]
}

Results are pushed to the default dataset. The actor monetizes per tool call via Apify's Pay-Per-Event model.

Self-hosting (Cloudflare Workers / any Node runtime)

Build and run the HTTP transport:

npm install
npm run build
FMP_API_KEY=your_key npm run start:http

Your MCP client can then connect to POST http://your-host:3000/mcp.

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Environment Variables

Variable	Required	Description
FMP_API_KEY	Yes	Financial Modeling Prep API key. Get one at financialmodelingprep.com.
PORT	No	Port for HTTP transport. Defaults to 3000.

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Development

npm install
npm run dev           # stdio, hot reload via tsx
npm run build         # TypeScript -> dist/
npm start             # run built stdio server
npm run start:http    # run built HTTP server

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Architecture

src/
├── index.ts          # MCP server entry (stdio + Streamable HTTP)
├── actor.ts          # Apify Actor entry
├── services/
│   └── fmp.ts        # Financial Modeling Prep API client
├── tools/
│   ├── get-stock-snapshot.ts
│   ├── get-company-metrics.ts
│   └── compare-companies.ts
└── utils/
    └── formatting.ts # Market cap formatting, CAGR, trend signals

All FMP endpoints are wrapped in a single FmpClient class. Tool implementations fan out to multiple client methods in parallel via Promise.all, then synthesize the merged result.

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License

MIT — see ./LICENSE.

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Toolstem — curated financial intelligence for the agent-native economy.