MCP Financial Data Gateway

10 financial data tools. 1 universal MCP layer. Any LLM can use it in 60 seconds.

Why This Exists

The biggest bottleneck in enterprise AI adoption isn't model quality — it's data connectivity. Every team building an AI agent today faces the same problem: wiring up data sources one at a time, with brittle custom integrations that break when APIs change. MCP (Model Context Protocol) is Anthropic's answer — a single standard for connecting any data source to any LLM, the same way USB-C standardized device charging. This project demonstrates what that looks like in practice: ten financial data tools across six APIs — market data, economic indicators, sentiment, insider activity, and event calendars — unified behind a single discoverable interface that any MCP-compatible agent can call without writing a line of integration code.

Related MCP server: finance-mcp

Architecture

graph LR
    subgraph Clients["MCP Clients · stdio"]
        CD[Claude Desktop]
        CA[Custom Agent]
        CR[Cursor IDE]
    end

    TS[MCP Server<br/>server.py]

    subgraph Tools["10 Tool Handlers"]
        TG1["Market Data<br/>stock · crypto · forex"]
        TG2["Economic Intel<br/>indicators · yields · fear&greed"]
        TG3["Market Intel<br/>news · insider trades"]
        TG4["Calendars<br/>economic · IPO"]
    end

    subgraph MW["Middleware Pipeline"]
        CACHE[Cache<br/>TTLCache · 5-min]
        RL[Rate Limiter<br/>per-provider quotas]
        NORM[Validator<br/>Pydantic v2]
        ERR[Error Handler<br/>safe_execute]
    end

    subgraph APIs["External APIs · Free Tier"]
        AV[Alpha Vantage<br/>25 req/day]
        FRED[FRED<br/>120 req/min]
        FX[ExchangeRate API<br/>open endpoint]
        CG[CoinGecko<br/>30 req/min]
        FH[Finnhub<br/>60 req/min]
        SEC[SEC EDGAR<br/>10 req/sec]
        FG[CNN Fear & Greed<br/>public]
    end

    Clients -->|stdio| TS
    TS --> Tools
    Tools -->|"① check"| CACHE
    CACHE -->|"② miss"| RL
    RL -->|"③ fetch"| APIs
    APIs -->|"④ raw"| NORM
    NORM -->|"⑤ write"| CACHE
    RL -.->|rate exceeded| ERR
    NORM -.->|on error| ERR

    style TS fill:#4A90D9,stroke:#2E6BA6,color:#fff
    style CACHE fill:#F5A623,stroke:#D4891A,color:#fff
    style NORM fill:#7B68EE,stroke:#5B48CE,color:#fff
    style RL fill:#E74C3C,stroke:#C0392B,color:#fff
    style ERR fill:#2ECC71,stroke:#27AE60,color:#fff

Every tool call follows the same middleware chain: cache check → rate limit → API call → Pydantic validation → cache write. Errors at any stage return a structured ErrorResponse — the server never crashes.

The 10 Tools

Market Data

Economic Intelligence

Market Intelligence

Calendars

Four tools require no API key at all (Forex, Crypto, Insider Trades, Fear & Greed) — run them immediately after cloning.

Quick Start

Step 1 — Clone

git clone https://github.com/ranjittilekar/financial-data-gateway-mcp.git
cd financial-data-gateway-mcp

Step 2 — Install

python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -e .

Step 3 — Configure

cp .env.example .env
# Edit .env with your three API keys (Alpha Vantage, FRED, Finnhub — all free, ~60 sec each)
# Also set SEC_EDGAR_USER_AGENT="AppName Your Name email@example.com" (SEC policy, no key needed)
# ExchangeRate-API, CoinGecko, and SEC EDGAR need no API key

Step 4 — Run

# Option A: MCP server (for Claude Desktop / any MCP client)
python run_server.py

# Option B: Standalone demo UI
streamlit run demo/streamlit_app.py

Usage with Claude Desktop

Add this to your Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "financial-data-gateway": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["run_server.py"],
      "cwd": "/absolute/path/to/ai-pm-01-mcp-financial-gateway",
      "env": {
        "ALPHA_VANTAGE_API_KEY": "your_key",
        "FRED_API_KEY": "your_key",
        "FINNHUB_API_KEY": "your_key",
        "SEC_EDGAR_USER_AGENT": "FinancialDataGateway Your Name email@example.com"
      }
    }
  }
}

Important: Use the absolute path to your virtualenv's Python, not bare python. Claude Desktop doesn't inherit your shell's PATH.

Example conversation after connecting:

You: Should I invest in NVIDIA? Show me the stock price, recent insider activity, the treasury yield curve, the Fear & Greed Index, and any major economic events this week.

Claude: (calls get_stock_quote("NVDA"), get_insider_trades("NVDA"), get_treasury_yields(), get_fear_greed_index(), get_economic_calendar(...) simultaneously)

NVIDIA (NVDA): Trading at $875.40, up +2.1% today. Insiders have been net sellers — 3 Sale transactions totaling $12.4M in the past 30 days, with no open-market purchases. The yield curve is normal (+0.49% spread), suggesting no near-term recession signal. However, the Fear & Greed Index sits at 19 (Extreme Fear) — market-wide sentiment has deteriorated sharply over the past month from 45 to 19. Upcoming this week: PCE inflation data (Friday, high impact) and jobless claims (Thursday)...

Usage as Standalone (Streamlit Demo)

streamlit run demo/streamlit_app.py

The demo includes 11 tabs:

• Stocks — real-time quote with OHLCV metrics

• Economic Data — FRED time series with line chart

• Forex — live FX rates with inverse calculation

• Crypto — price + market cap + 24h change

• News — latest market headlines by category

• Insider Trades — SEC Form 4 filings as a formatted table

• Treasury Yields — full yield curve + inversion badge (green/red)

• Fear & Greed — color-coded score with all 7 component indicators

• Economic Calendar — upcoming US data releases grouped by impact

• IPO Calendar — upcoming listings with price range and exchange

• Multi-Query — ask a question in plain English; Gemini picks all 10 tools, fetches data, and synthesizes a full answer

Technical Decisions

Evals

259 tests, 6 test files, 0 mocks of business logic.

evals/test_middleware.py    50 tests   Unit tests — CacheManager, RateLimiter,
                                       ResponseNormalizer, ErrorHandler
evals/test_tools.py         93 tests   All 10 tool handlers — cache mutation,
                                       from_cache flag, schema roundtrip,
                                       missing key, live API fixtures
evals/test_stock_quotes.py  }
evals/test_economic_data.py }          45 tests   Per-tool integration tests with
evals/test_forex_rates.py   }          live API calls (session-scoped fixtures,
evals/test_crypto_prices.py }          one call shared per test session)
evals/test_market_news.py   }
evals/test_integration.py   22 tests   End-to-end MCP server tests — list_tools,
                                       call_tool, error shape, description quality

Run the suite:

pytest evals/ -v --tb=short
# → ~255 passed, ~4 skipped in ~10s
# Skips are live Alpha Vantage tests when the free daily quota is exhausted —
# handled gracefully with pytest.skip(), not errors.

Invariants enforced by tests:

• Every successful response contains a _meta block with source, retrieved_at, from_cache

• A second identical call always returns from_cache: true

• Cache hits never mutate the stored object (copy-on-return pattern)

• ErrorHandler.safe_execute() never propagates an exception — tested against 6 failure modes

• All 10 tool descriptions are ≥ 80 characters with example values (LLM routing quality check)

• Alpha Vantage daily cap fires correctly after minute windows reset (monkeypatched clock test)

• Fear & Greed index returns exactly 7 component indicators

• Treasury yields return exactly 8 maturities with float rates

Lessons Learned

Trade-offs I Made

5-minute cache TTL — I chose 5 minutes as a balance between API quota conservation and data freshness. Stock prices change by the second, but in the context of a demo or a planning conversation, a 5-minute-old quote is nearly always good enough. A production system would make TTL a per-tool configuration: 30 seconds for live prices, 1 hour for FRED economic data, 24 hours for news.

Free tier limits — All five APIs used are free, which creates real constraints: Alpha Vantage's 25 req/day limit means a heavy demo session can exhaust the daily budget in minutes. I handled this by building in both proactive rate limiting (tracks remaining quota before each call) and graceful degradation (returns structured error with retry guidance rather than crashing). The architectural lesson: design around your quota budget as a first-class constraint, not an afterthought.

stdio transport instead of SSE — stdio is the simplest MCP transport and works perfectly for a single local client. SSE (Server-Sent Events) would enable multiple concurrent clients and remote deployments. I chose stdio deliberately to ship faster and demonstrate the core value proposition; the upgrade path is documented.

What I'd Change With More Time

• WebSocket streaming for live price tickers — push updates instead of poll-on-demand

• SSE transport to support multi-client deployments and remote MCP servers

• Historical data endpoints — FRED observations go back decades; surfacing time-range queries would make the economic data tool far more powerful for trend analysis

• Tool composition — a get_market_summary meta-tool that calls all 5 in parallel and returns a unified snapshot, useful for "give me a full briefing" queries

• Persistent cache (Redis or SQLite) so the TTL budget survives server restarts in long-running deployments

Guardrails I Added

• Sliding window rate limiter with per-provider quotas (not just a token bucket) — catches both per-minute bursts and daily budget exhaustion

• Pydantic validation on every API response — if Alpha Vantage changes a field name, the error surfaces immediately with a clear message instead of silently passing bad data downstream

• Copy-on-return for cache hits — {**cached, "_meta": {**cached["_meta"], "from_cache": True}} creates a new dict rather than mutating the stored object; discovered this class of bug in testing

• safe_execute() wrapper — ErrorHandler catches RateLimitError, httpx.HTTPError, ValidationError, ValueError, and bare Exception; the MCP server returns a valid JSON response in every case

• Alpha Vantage soft-error detection — the API returns 200 OK with {"Note": "..."} for per-minute limits and {"Information": "..."} for daily limits; both are detected by key presence and raised as RateLimitError before Pydantic ever sees the response

Project Structure

ai-pm-01-mcp-financial-gateway/
├── run_server.py                # Entry point: python run_server.py
├── src/
│   ├── server.py                # MCP orchestrator — registers all 10 tools, wires middleware
│   ├── tools/                   # 10 tool handlers (dependency-injected pattern)
│   ├── middleware/              # CacheManager, RateLimiter, ResponseNormalizer, ErrorHandler
│   └── models/schemas.py        # Pydantic v2 models for all 10 tools + ErrorResponse
├── demo/streamlit_app.py        # Standalone demo UI — 11 tabs + Gemini multi-query
├── evals/                       # 259 tests across 6 files
├── docs/
│   ├── SETUP.md                 # Step-by-step setup for Claude Desktop
│   └── lessons-learned.md       # Extended PM reflections
└── claude_desktop_config.json   # Example MCP client config

License

MIT — see LICENSE.