├── .gitignore ├── .python-version ├── LICENSE ├── README.md ├── main.py ├── pyproject.toml └── uv.lock /.gitignore: -------------------------------------------------------------------------------- 1 | .venv 2 | .env 3 | __pycache__ -------------------------------------------------------------------------------- /.python-version: -------------------------------------------------------------------------------- 1 | 3.13 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 kukapay 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Crypto Sentiment MCP Server 2 | 3 | An MCP server that delivers cryptocurrency sentiment analysis to AI agents, leveraging Santiment's aggregated social media and news data to track market mood and detect emerging trends. 4 | 5 | ![GitHub License](https://img.shields.io/github/license/kukapay/crypto-sentiment-mcp) 6 | ![Python Version](https://img.shields.io/badge/python-3.10+-blue) 7 | ![Status](https://img.shields.io/badge/status-active-brightgreen.svg) 8 | 9 | 10 | ## Features 11 | 12 | - **Sentiment Analysis**: Retrieve sentiment balance (positive vs. negative) for specific cryptocurrencies. 13 | - **Social Volume Tracking**: Monitor total social media mentions and detect significant shifts (spikes or drops). 14 | - **Social Dominance**: Measure the share of discussions an asset occupies in crypto media. 15 | - **Trending Words**: Identify the most popular terms trending in cryptocurrency discussions. 16 | 17 | ## Tools 18 | 19 | | Tool Name | Description | Parameters | 20 | |-------------------------|-----------------------------------------------------------------------------------------------|---------------------------------------------| 21 | | `get_sentiment_balance` | Get the average sentiment balance for an asset over a specified period. | `asset: str`, `days: int = 7` | 22 | | `get_social_volume` | Fetch the total number of social media mentions for an asset. | `asset: str`, `days: int = 7` | 23 | | `alert_social_shift` | Detect significant spikes or drops in social volume compared to the previous average. | `asset: str`, `threshold: float = 50.0`, `days: int = 7` | 24 | | `get_trending_words` | Retrieve the top trending words in crypto discussions, ranked by score over a period. | `days: int = 7`, `top_n: int = 5` | 25 | | `get_social_dominance` | Measure the percentage of crypto media discussions dominated by an asset. | `asset: str`, `days: int = 7` | 26 | 27 | ## Prerequisites 28 | 29 | - **Python**: 3.10 or higher 30 | - **Santiment API Key**: Obtain a free or paid key from [Santiment](https://app.santiment.net/). 31 | 32 | ## Installation 33 | 34 | 1. **Clone the Repository**: 35 | ```bash 36 | git clone https://github.com/kukapay/crypto-sentiment-mcp.git 37 | cd crypto-sentiment-mcp 38 | ``` 39 | 40 | 2. **Configure Client**: 41 | ``` 42 | { 43 | "mcpServers": { 44 | "crypto-sentiment-mcp": { 45 | "command": "uv", 46 | "args": ["--directory", "path/to/crypto-sentiment-mcp", "run", "main.py"], 47 | "env": { 48 | "SANTIMENT_API_KEY": "your_api_key_here" 49 | } 50 | } 51 | } 52 | } 53 | ``` 54 | 55 | ## Examples 56 | 57 | Below are examples of natural language inputs and their corresponding outputs when interacting with the server via an MCP-compatible client: 58 | 59 | - **Input**: "What's the sentiment balance for Bitcoin over the last week?" 60 | - **Output**: "Bitcoin's sentiment balance over the past 7 days is 12.5." 61 | 62 | - **Input**: "How many times has Ethereum been mentioned on social media in the past 5 days?" 63 | - **Output**: "Ethereum's social volume over the past 5 days is 8,432 mentions." 64 | 65 | - **Input**: "Tell me if there's been a big change in Bitcoin's social volume recently, with a 30% threshold." 66 | - **Output**: "Bitcoin's social volume spiked by 75.0% in the last 24 hours, from an average of 1,000 to 1,750." 67 | 68 | - **Input**: "What are the top 3 trending words in crypto over the past 3 days?" 69 | - **Output**: "Top 3 trending words over the past 3 days: 'halving', 'bullrun', 'defi'." 70 | 71 | - **Input**: "How dominant is Ethereum in social media discussions this week?" 72 | - **Output**: "Ethereum's social dominance over the past 7 days is 18.7%." 73 | 74 | 75 | ## License 76 | 77 | This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. 78 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import requests 2 | from mcp.server.fastmcp import FastMCP 3 | from datetime import datetime, timedelta, UTC 4 | import os 5 | from dotenv import load_dotenv 6 | 7 | load_dotenv() 8 | 9 | mcp = FastMCP("CryptoSentiment") 10 | SANTIMENT_API_KEY = os.getenv("SANTIMENT_API_KEY") 11 | if not SANTIMENT_API_KEY: 12 | raise ValueError("SANTIMENT_API_KEY not found in environment variables. Please set it in .env file.") 13 | SANTIMENT_API_URL = "https://api.santiment.net/graphql" 14 | HEADERS = {"Authorization": f"Apikey {SANTIMENT_API_KEY}"} 15 | 16 | def fetch_santiment_data(metric: str, asset: str, days: int) -> dict: 17 | now = datetime.now(UTC) 18 | 19 | to_date = now 20 | from_date = to_date - timedelta(days=days) 21 | 22 | query = f""" 23 | {{ 24 | getMetric(metric: "{metric}") {{ 25 | timeseriesData( 26 | slug: "{asset}" 27 | from: "{from_date.isoformat()}" 28 | to: "{to_date.isoformat()}" 29 | interval: "1d" 30 | ) {{ 31 | datetime 32 | value 33 | }} 34 | }} 35 | }} 36 | """ 37 | response = requests.post(SANTIMENT_API_URL, json={"query": query}, headers=HEADERS) 38 | result = response.json() 39 | if result.get("errors"): 40 | raise Exception(f"API error: {result.get('errors')}") 41 | return result 42 | 43 | def fetch_trending_words(days: int = 7) -> dict: 44 | now = datetime.now(UTC) 45 | 46 | to_date = now 47 | from_date = to_date - timedelta(days=days) 48 | 49 | query = f""" 50 | {{ 51 | getTrendingWords(size: 10, from: "{from_date.isoformat()}", to: "{to_date.isoformat()}", interval: "1d") {{ 52 | datetime 53 | topWords {{ 54 | word 55 | score 56 | }} 57 | }} 58 | }} 59 | """ 60 | response = requests.post(SANTIMENT_API_URL, json={"query": query}, headers=HEADERS) 61 | result = response.json() 62 | if result.get("errors"): 63 | raise Exception(f"API error: {result.get('errors')}") 64 | return result 65 | 66 | @mcp.tool() 67 | def get_sentiment_balance(asset: str, days: int = 7) -> str: 68 | """ 69 | Retrieve the sentiment balance (sentiment_balance_total) for a given asset. 70 | 71 | Parameters: 72 | - asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required. 73 | - days (int): Number of days to calculate the average sentiment balance, defaults to 7. 74 | 75 | Usage: 76 | - Use this tool to get the average sentiment balance (positive minus negative sentiment) over a period. 77 | 78 | Returns: 79 | - A string with the average sentiment balance (e.g., "Bitcoin's sentiment balance over the past 7 days is 12.5"). 80 | """ 81 | try: 82 | data = fetch_santiment_data("sentiment_balance_total", asset, days) 83 | timeseries = data.get("data", {}).get("getMetric", {}).get("timeseriesData", []) 84 | if not timeseries: 85 | return f"Unable to fetch sentiment data for {asset}. Check subscription limits or asset availability." 86 | avg_balance = sum(float(d["value"]) for d in timeseries) / len(timeseries) 87 | return f"{asset.capitalize()}'s sentiment balance over the past {days} days is {avg_balance:.1f}." 88 | except Exception as e: 89 | return f"Error fetching sentiment balance for {asset}: {str(e)}" 90 | 91 | @mcp.tool() 92 | def get_social_volume(asset: str, days: int = 7) -> str: 93 | """ 94 | Retrieve the total social volume (social_volume_total) for a given asset. It calculates the total number of social data text documents that contain the given search term at least once. Examples of documents are telegram messages and reddit posts. 95 | 96 | Parameters: 97 | - asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required. 98 | - days (int): Number of days to sum the social volume, defaults to 7. 99 | 100 | Usage: 101 | - Call this tool to get the total number of social media mentions for an asset over a period. 102 | 103 | Returns: 104 | - A string with the total social volume (e.g., "Bitcoin's social volume over the past 7 days is 15,000 mentions"). 105 | """ 106 | try: 107 | data = fetch_santiment_data("social_volume_total", asset, days) 108 | timeseries = data.get("data", {}).get("getMetric", {}).get("timeseriesData", []) 109 | if not timeseries: 110 | return f"Unable to fetch social volume for {asset}. Check subscription limits or asset availability." 111 | total_volume = sum(int(d["value"]) for d in timeseries) 112 | return f"{asset.capitalize()}'s social volume over the past {days} days is {total_volume:,} mentions." 113 | except Exception as e: 114 | return f"Error fetching social volume for {asset}: {str(e)}" 115 | 116 | @mcp.tool() 117 | def alert_social_shift(asset: str, threshold: float = 50.0, days: int = 7) -> str: 118 | """ 119 | Detect significant shifts (spikes or drops) in social volume (social_volume_total) for a given asset. 120 | 121 | Parameters: 122 | - asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required. 123 | - threshold (float): Minimum percentage change (absolute value) to trigger an alert, defaults to 50.0 (i.e., 50%). 124 | - days (int): Number of days to analyze for baseline volume, defaults to 7. 125 | 126 | Usage: 127 | - Call this tool to check if the latest social volume has significantly spiked or dropped compared to the previous average. 128 | 129 | Returns: 130 | - A string indicating if a shift occurred (e.g., "Bitcoin's social volume spiked by 75.0% in the last 24 hours" or "Bitcoin's social volume dropped by 60.0% in the last 24 hours"). 131 | """ 132 | try: 133 | data = fetch_santiment_data("social_volume_total", asset, days) 134 | timeseries = data.get("data", {}).get("getMetric", {}).get("timeseriesData", []) 135 | 136 | if not timeseries or len(timeseries) < 2: 137 | return f"Unable to detect social volume shift for {asset}, insufficient data." 138 | 139 | latest_volume = int(timeseries[-1]["value"]) # Latest day's volume 140 | prev_avg_volume = sum(int(d["value"]) for d in timeseries[:-1]) / (len(timeseries) - 1) # Average of previous days 141 | change_percent = ((latest_volume - prev_avg_volume) / prev_avg_volume) * 100 142 | 143 | abs_change = abs(change_percent) 144 | if abs_change >= threshold: 145 | direction = "spiked" if change_percent > 0 else "dropped" 146 | return f"{asset.capitalize()}'s social volume {direction} by {abs_change:.1f}% in the last 24 hours, from an average of {prev_avg_volume:,.0f} to {latest_volume:,}." 147 | return f"No significant shift detected for {asset.capitalize()}, change is {change_percent:.1f}%." 148 | except Exception as e: 149 | return f"Error detecting social volume shift for {asset}: {str(e)}" 150 | 151 | @mcp.tool() 152 | def get_trending_words(days: int = 7, top_n: int = 5) -> str: 153 | """ 154 | Retrieve the top trending words in the crypto space over a specified period, aggregated and ranked by score. 155 | 156 | Parameters: 157 | - days (int): Number of days to analyze trending words, defaults to 7. 158 | - top_n (int): Number of top trending words to return, defaults to 5. 159 | 160 | Usage: 161 | - Call this tool to get a list of the most popular words trending in cryptocurrency discussions, ranked across the entire period. 162 | 163 | Returns: 164 | - A string listing the top trending words (e.g., "Top 5 trending words over the past 7 days: 'halving', 'bullrun', 'defi', 'nft', 'pump'"). 165 | """ 166 | try: 167 | data = fetch_trending_words(days) 168 | trends = data.get("data", {}).get("getTrendingWords", []) 169 | if not trends: 170 | return "Unable to fetch trending words. Check API subscription limits or connectivity." 171 | 172 | word_scores = {} 173 | for day in trends: 174 | for word_data in day["topWords"]: 175 | word = word_data["word"] 176 | score = word_data["score"] 177 | if word in word_scores: 178 | word_scores[word] += score 179 | else: 180 | word_scores[word] = score 181 | 182 | if not word_scores: 183 | return "No trending words data available for the specified period." 184 | 185 | top_words = sorted(word_scores.items(), key=lambda x: x[1], reverse=True)[:top_n] 186 | top_words_list = [word for word, _ in top_words] 187 | 188 | return f"Top {top_n} trending words over the past {days} days: {', '.join(top_words_list)}." 189 | except Exception as e: 190 | return f"Error fetching trending words: {str(e)}" 191 | 192 | @mcp.tool() 193 | def get_social_dominance(asset: str, days: int = 7) -> str: 194 | """ 195 | Retrieve the social dominance (social_dominance_total) for a given asset. Social Dominance shows the share of the discussions in crypto media that is referring to a particular asset or phrase. 196 | 197 | Parameters: 198 | - asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required. 199 | - days (int): Number of days to calculate average social dominance, defaults to 7. 200 | 201 | Usage: 202 | - Call this tool to get the percentage of social media discussion dominated by the asset. 203 | 204 | Returns: 205 | - A string with the average social dominance (e.g., "Bitcoin's social dominance over the past 7 days is 25.3%"). 206 | """ 207 | try: 208 | data = fetch_santiment_data("social_dominance_total", asset, days) 209 | timeseries = data.get("data", {}).get("getMetric", {}).get("timeseriesData", []) 210 | if not timeseries: 211 | return f"Unable to fetch social dominance for {asset}. Check subscription limits or asset availability." 212 | avg_dominance = sum(float(d["value"]) for d in timeseries) / len(timeseries) 213 | return f"{asset.capitalize()}'s social dominance over the past {days} days is {avg_dominance:.1f}%." 214 | except Exception as e: 215 | return f"Error fetching social dominance for {asset}: {str(e)}" 216 | 217 | if __name__ == "__main__": 218 | mcp.run() -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "crypto-sentiment-mcp" 3 | version = "0.1.0" 4 | description = "An MCP server that delivers cryptocurrency sentiment analysis to AI agents." 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "mcp[cli]>=1.4.1", 9 | "sanpy>=0.12.0", 10 | ] 11 | -------------------------------------------------------------------------------- /uv.lock: 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