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Crypto Intel Mcp

Crypto Intel Mcp

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github.com/cstamigo-droidFinance0Updated 5w ago

About Crypto Intel Mcp

Crypto Intel Mcp is an MCP server that provides LLMs with cryptocurrency prices, momentum, trending coins, and a combined signal. It uses CoinGecko's free public API and returns Markdown by default or JSON on request.

Key features

  1. 01Live cryptocurrency quotes
  2. 0224-hour, 7-day, and 30-day momentum
  3. 03Trending coin lookup
  4. 04Composite scored market signal
  5. 05Markdown or JSON results
  6. 06Graceful handling of missing data

Use cases

  1. 01Reviewing a coin's current price and market rank
  2. 02Comparing momentum across timeframes
  3. 03Checking which coins are trending
  4. 04Getting a combined crypto market signal

MCP support

Exposes quote, momentum, trending, and composite-analysis tools to MCP clients. The server runs over stdio and can be configured for clients such as Claude Desktop.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

crypto-intel-mcp

License: MIT Python MCP crypto-intel-mcp MCP server

crypto-intel-mcp - coin analysis demo

Crypto market intelligence for any LLM, over the Model Context Protocol.

Gives any MCP client (Claude Desktop, Claude Code, agents) clean tools for crypto price, momentum, and what's trending — and blends them into a single scored verdict. Runs entirely on CoinGecko's free public API (no key), fails gracefully (a missing source returns "no data", never a fabricated value), and outputs Markdown by default or JSON on demand.

# Crypto Intelligence — BTC

**Signal:** Lean positive  `[........|##......]` +22/100  · confidence 80%

Building momentum → LEAN LONG. BTC momentum: 24h +1.9% · 7d +4.4% · 30d -17.0%.
- In CoinGecko trending (elevated attention).
- Total crypto market cap +1.8% 24h, BTC dominance 57%.

Tools

ToolWhat it doesSourceStatus
crypto_analyzeHero tool. Blends momentum + trending + market tide into one scored verdict (RISK-ON → AVOID).composite✅
crypto_get_quoteLive price, 24h change, market-cap rank, volume, distance from ATH.CoinGecko✅
crypto_momentumMulti-timeframe momentum (24h/7d/30d) scored into one directional signal.CoinGecko✅
crypto_trendingThe coins the market is searching for most right now.CoinGecko✅

Every tool returns Markdown (human-readable, default) or JSON (response_format="json") for programmatic use.


Live demo output (2026-06-15)

crypto_get_quote bitcoin:
  BTC $66,504.00  +3.4% 24h  · rank #1  · -47% from ATH

crypto_momentum bitcoin:
  Lean positive  [........|##......] +21/100  · confidence 80%
  BTC momentum: 24h +3.4% · 7d +4.9% · 30d -14.8%

crypto_trending:
  ZEC, PENGU, SIREN, TAO, NEAR, BTC, HYPE, GRAM, SUI, H

crypto_analyze bitcoin:
  Lean positive  [........|###.....] +35/100  · confidence 80%
  Building momentum → LEAN LONG. In CoinGecko trending (elevated attention).
  Total crypto market cap +3.6% 24h, BTC dominance 57%.

All values are live from CoinGecko's free public API — no key, no fabrication.


Quick start

git clone https://github.com/cstamigo-droid/crypto-intel-mcp crypto-intel-mcp
cd crypto-intel-mcp
python -m venv .venv && .venv\Scripts\activate     # Windows
pip install -r requirements.txt

python -m crypto_intel_mcp     # starts the MCP server over stdio (no .env needed)

Smoke test (hits the live CoinGecko API and prints each signal):

python tests/test_smoke.py bitcoin

Use it in Claude Desktop

Add this to claude_desktop_config.json (%APPDATA%\Claude\ on Windows, ~/Library/Application Support/Claude/ on macOS), then restart Claude Desktop:

{
  "mcpServers": {
    "crypto-intel-mcp": {
      "command": "python",
      "args": ["-m", "crypto_intel_mcp"],
      "cwd": "C:/path/to/crypto-intel-mcp"
    }
  }
}

Why it's built this way

  • Uniform result contract. Every source returns the same Result shape, so an LLM can reason across tools instead of parsing N formats.
  • Graceful degradation. A source with no data returns "no data", not a fake value. Missing data never invents an answer.
  • Resilient + cached. A short per-source TTL cache avoids hammering rate-limited endpoints when an agent calls several tools in one turn.

Disclaimer

For research and educational use only. Data comes from third-party sources and may be delayed or incomplete.

License

MIT

Comments

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