Toolstem Mcp Server

by toolstem

270 downloads

Not rated

GitHub

Other
AI

About

Agent-ready financial intelligence tools for AI agents. Derives signals like UNDERVALUED, STRONG, and ACCELERATING — one call, one agent-friendly response.

Details

Author: toolstem
Downloads: 270
Categories: Other, AI

- Parallel data fetching from multiple sources concurrently
- Human-readable derived signals (e.g. UNDERVALUED, STRONG, ACCELERATING)
- Pre-computed CAGR, YoY growth, margin trends, and FCF yield
- Flat, predictable response schemas — no nested vendor quirks
- Graceful degradation — partial nulls if one upstream endpoint fails
- Four tools: stock snapshot, company metrics, stock screener, company comparison

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Toolstem Mcp Server

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

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

JSON

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

JSON

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

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

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

JSON

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

JSON

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

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

From the repository

Install globally via npm (npm install -g toolstem-mcp-server) and run with the required FMP_API_KEY environment variable. Supports stdio mode (default) and Streamable HTTP transport (--http flag, optional PORT). Can also be added to Claude Desktop via claude_desktop_config.json, run as an Apify Actor, or self-hosted on any Node runtime.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

{
    "toolstem": {
        "command": "npx",
        "args": [
            "-y",
            "toolstem-mcp-server"
        ]
    }
}

Toolstem MCP Server

Agent-ready financial intelligence tools — curated, not raw. Toolstem is an MCP (Model Context Protocol) server that turns raw financial market data into curated, synthesized intelligence for AI agents. 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. ---

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. ---

Tools

`get_stock_snapshot`

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

json
{
  "symbol": "AAPL"
}



Example output (truncated):

json
{
  "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:

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

period accepts annual (default) or quarter

.

Example output (truncated):

json
{
  "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.

---

screen_stocks



Screen and filter stocks by sector, market cap, price range, beta, volume, dividend yield, exchange, and country. Returns derived category signals for every match.

Input:

json
{
  "sector": "Technology",
  "market_cap_min": 10000000000,
  "exchange": "NASDAQ",
  "volume_min": 500000,
  "limit": 20
}



All parameters are optional — omit any filter to leave that dimension open.

Example output (truncated):

json
{
  "query_summary": "20 stocks matching: sector=Technology, mktCap≥$10.0B, exchange=NASDAQ, volume≥500,000",
  "total_results": 20,
  "stocks": [
    {
      "symbol": "AAPL",
      "company_name": "Apple Inc.",
      "sector": "Technology",
      "industry": "Consumer Electronics",
      "exchange": "NASDAQ",
      "country": "US",
      "price": 178.52,
      "market_cap": 2780000000000,
      "market_cap_readable": "$2.78T",
      "beta": 1.28,
      "volume": 55000000,
      "last_annual_dividend": 0.96,
      "cap_category": "MEGA",
      "volatility_category": "MODERATE",
      "liquidity_category": "HIGH"
    }
  ],
  "meta": {
    "source": "Toolstem via Financial Modeling Prep",
    "timestamp": "2026-04-20T18:30:00Z",
    "data_delay": "Real-time during market hours",
    "filters_applied": ["sector: Technology", "market_cap_min: 10000000000", "exchange: NASDAQ", "volume_min: 500000", "limit: 20"]
  }
}



Derived fields:

-

cap_category — MEGA (>$200B), LARGE ($10B–$200B), MID ($2B–$10B), SMALL ($300M–$2B), MICRO ($50M–$300M), NANO

 (<$50M).
-

volatility_category — LOW (beta < 0.8), MODERATE (0.8–1.3), HIGH

 (> 1.3).
-

liquidity_category — HIGH (volume > 1M), MODERATE (100K–1M), LOW

 (< 100K).

---

compare_companies



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

Input:

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



Example output (truncated):

json
{
  "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.

---

Installation


npm

bash
npm install -g toolstem-mcp-server



Run as stdio server:

bash
FMP_API_KEY=your_key_here toolstem-mcp-server



Run as HTTP (Streamable HTTP transport) server:

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



Claude Desktop


Add to your

claude_desktop_config.json

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:

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

or

json
{
  "tool": "screen_stocks",
  "sector": "Technology",
  "market_cap_min": 10000000000,
  "limit": 20
}

or

json
{
  "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:

bash
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

.

---

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

. |

---

Development

bash
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



---

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
│   ├── screen-stocks.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. ---

License

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

No reviews yet — be the first

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Toolstem MCP Server

Why Toolstem?

Tools

get_stock_snapshot

get_company_metrics

screen_stocks

compare_companies

Installation

npm

Claude Desktop

Apify

Self-hosting (Cloudflare Workers / any Node runtime)

Environment Variables

Development

Architecture

License

Install

More like this

Sequential Thinking

Filesystem

EverArt

Google Drive

Context 7

Reactive Resume

`get_stock_snapshot`