Toolstem MCP Server

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

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.

---

Quickstart — hosted endpoint (recommended)

Point your MCP client or agent at the hosted endpoint. No API key, no infra, no setup. Billing is per-call via x402 — the agent's wallet pays directly in USDC on Base mainnet.

https://mcp.toolstem.com/mcp/finance

• No FMP API key required — you do not bring or manage any upstream data key.
• No infrastructure — nothing to install, host, or keep running.
• No setup — connect an MCP client and call a tool.
• initialize and tools/list are free (discovery and schema introspection).
• Each tools/call costs $0.01 USDC on Base mainnet, settled via x402.

Claude Desktop

Drop this into your claude_desktop_config.json:

{

"mcpServers": { "toolstem-finance": { "url": "https://mcp.toolstem.com/mcp/finance" } } }

Restart Claude Desktop, then ask: *"Use Toolstem to get a snapshot of NVDA."*

Any MCP client (LangChain.js)

The official @langchain/mcp-adapters library connects directly to the hosted URL:

import { MultiServerMCPClient } from "@langchain/mcp-adapters";

import { ChatOpenAI } from "@langchain/openai"; import { createReactAgent } from "@langchain/langgraph/prebuilt";

const client = new MultiServerMCPClient({ toolstem_finance: { transport: "http", url: "https://mcp.toolstem.com/mcp/finance", // Add your x402-signing middleware via headers, OR run an x402 // proxy locally and point url at it. See https://www.x402.org/clients. }, });

const tools = await client.getTools(); const agent = createReactAgent({ llm: new ChatOpenAI({ model: "gpt-4o-mini" }), tools }); await agent.invoke({ messages: "Compare AAPL, MSFT, and GOOGL on valuation and growth." });

LangChain quick-start (langchain-toolstem)

The langchain-toolstem wrapper handles x402 payment for you — pass a funded wallet key and the tools auto-pay per call:

import { createToolstemTools } from 'langchain-toolstem';

const tools = await createToolstemTools({ walletPrivateKey: process.env.WALLET_KEY }); // Tools auto-pay $0.01 USDC per call via x402

Prefer to run the server yourself with your own FMP key? See Advanced: self-host at the bottom.

Try the tools live in the Toolstem playground.

Product page: https://toolstem.com/finance/.

---

Pricing

• MCP `initialize` and `tools/list` are free — discovery, schema introspection, and health checks never cost anything.
• `tools/call` costs $0.01 USDC on Base mainnet per invocation, paid via [x402](https://www.x402.org). No API key, no signup, no marketplace account required — the agent's wallet pays directly.

How billing works

Toolstem uses the x402 payment protocol. Agents pay per call in USDC on Base — no API keys, no subscriptions, no invoices. The agent's wallet settles each call automatically via EIP-3009.

---

How it works

Toolstem ships as a Node MCP server (this repo) and as a hosted, x402-gated proxy.

Agent ──MCP──▶ Cloudflare Worker (x402 paywall) ──MCP──▶ Toolstem MCP server ──REST──▶ Financial Modeling Prep

│ │ └─ free: initialize, tools/list └─ composite tool: fans out to 3–5 FMP endpoints └─ paid: tools/call → 0.01 USDC on Base in parallel, derives signals, returns flat JSON

• Cloudflare Worker terminates the public MCP connection at mcp.toolstem.com and enforces the x402 payment for tools/call.
• MCP server (this package) implements the 3 composite tools and talks to Financial Modeling Prep.
• x402 on Base mainnet handles the micropayment — settlement is sub-second, no off-chain accounts.

---

Tools

Three composite tools, each one synthesizing multiple FMP endpoints with derived signals and pre-computed math.

All three are read-only, idempotent, and safe for agent retry.

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.

---

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"] }

symbols must be an array of 2 to 5 ticker strings.

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.

---

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 rea