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crypto-sentiment-mcp

crypto-sentiment-mcp

by kukapay
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Crypto Sentiment MCP Server

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.

GitHub License Python Version Status

Features

  • Sentiment Analysis: Retrieve sentiment balance (positive vs. negative) for specific cryptocurrencies.

  • Social Volume Tracking: Monitor total social media mentions and detect significant shifts (spikes or drops).

  • Social Dominance: Measure the share of discussions an asset occupies in crypto media.

  • Trending Words: Identify the most popular terms trending in cryptocurrency discussions.

Related MCP server: etf-flow-mcp

Tools

Tool Name

Description

Parameters

get_sentiment_balance

Get the average sentiment balance for an asset over a specified period.

asset: str, days: int = 7

get_social_volume

Fetch the total number of social media mentions for an asset.

asset: str, days: int = 7

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

get_trending_words

Retrieve the top trending words in crypto discussions, ranked by score over a period.

days: int = 7, top_n: int = 5

get_social_dominance

Measure the percentage of crypto media discussions dominated by an asset.

asset: str, days: int = 7

Prerequisites

  • Python: 3.10 or higher

  • Santiment API Key: Obtain a free or paid key from Santiment.

Installation

  1. Clone the Repository:

    git clone https://github.com/kukapay/crypto-sentiment-mcp.git
cd crypto-sentiment-mcp

  2. Configure Client:

    {
  "mcpServers": {
    "crypto-sentiment-mcp": {
      "command": "uv",
      "args": ["--directory", "path/to/crypto-sentiment-mcp", "run", "main.py"],
      "env": {
        "SANTIMENT_API_KEY": "your_api_key_here"
      }
    }
  }
}

Examples

Below are examples of natural language inputs and their corresponding outputs when interacting with the server via an MCP-compatible client:

  • Input: "What's the sentiment balance for Bitcoin over the last week?"

    • Output: "Bitcoin's sentiment balance over the past 7 days is 12.5."

  • Input: "How many times has Ethereum been mentioned on social media in the past 5 days?"

    • Output: "Ethereum's social volume over the past 5 days is 8,432 mentions."

  • Input: "Tell me if there's been a big change in Bitcoin's social volume recently, with a 30% threshold."

    • Output: "Bitcoin's social volume spiked by 75.0% in the last 24 hours, from an average of 1,000 to 1,750."

  • Input: "What are the top 3 trending words in crypto over the past 3 days?"

    • Output: "Top 3 trending words over the past 3 days: 'halving', 'bullrun', 'defi'."

  • Input: "How dominant is Ethereum in social media discussions this week?"

    • Output: "Ethereum's social dominance over the past 7 days is 18.7%."

License

This project is licensed under the MIT License - see the LICENSE file for details.

Available Tools

5 tools
alert_social_shiftA

Detect significant shifts (spikes or drops) in social volume (social_volume_total) for a given asset.

Parameters:

  • asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required.

  • threshold (float): Minimum percentage change (absolute value) to trigger an alert, defaults to 50.0 (i.e., 50%).

  • days (int): Number of days to analyze for baseline volume, defaults to 7.

Usage:

  • Call this tool to check if the latest social volume has significantly spiked or dropped compared to the previous average.

Returns:

  • 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").

Inputs: asset (required), threshold, days.

get_sentiment_balanceB

Retrieve the sentiment balance (sentiment_balance_total) for a given asset.

Parameters:

  • asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required.

  • days (int): Number of days to calculate the average sentiment balance, defaults to 7.

Usage:

  • Use this tool to get the average sentiment balance (positive minus negative sentiment) over a period.

Returns:

  • A string with the average sentiment balance (e.g., "Bitcoin's sentiment balance over the past 7 days is 12.5").

Inputs: asset (required), days.

get_social_dominanceA

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.

Parameters:

  • asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required.

  • days (int): Number of days to calculate average social dominance, defaults to 7.

Usage:

  • Call this tool to get the percentage of social media discussion dominated by the asset.

Returns:

  • A string with the average social dominance (e.g., "Bitcoin's social dominance over the past 7 days is 25.3%").

Inputs: asset (required), days.

get_social_volumeA

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.

Parameters:

  • asset (str): The cryptocurrency slug (e.g., "bitcoin", "ethereum"). Required.

  • days (int): Number of days to sum the social volume, defaults to 7.

Usage:

  • Call this tool to get the total number of social media mentions for an asset over a period.

Returns:

  • A string with the total social volume (e.g., "Bitcoin's social volume over the past 7 days is 15,000 mentions").

Inputs: asset (required), days.

get_trending_wordsA

Retrieve the top trending words in the crypto space over a specified period, aggregated and ranked by score.

Parameters:

  • days (int): Number of days to analyze trending words, defaults to 7.

  • top_n (int): Number of top trending words to return, defaults to 5.

Usage:

  • Call this tool to get a list of the most popular words trending in cryptocurrency discussions, ranked across the entire period.

Returns:

  • A string listing the top trending words (e.g., "Top 5 trending words over the past 7 days: 'halving', 'bullrun', 'defi', 'nft', 'pump'").

Inputs: days, top_n.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap. alert_social_shift detects volume changes, get_sentiment_balance measures sentiment polarity, get_social_dominance calculates discussion share, get_social_volume counts mentions, and get_trending_words identifies popular terms. The descriptions reinforce these unique functions, eliminating any confusion.

    Naming Consistency4/5

    The naming follows a consistent verb_noun pattern with four tools using 'get_' and one using 'alert_'. While 'alert_' deviates slightly, it still maintains readability and logical grouping. All names are snake_case and descriptive, making the set predictable and easy to navigate.

    Tool Count5/5

    With 5 tools, this server is well-scoped for crypto sentiment analysis. Each tool serves a specific, non-redundant function within the domain, covering key metrics like volume, sentiment, dominance, and trends. The count is neither too sparse nor bloated, fitting the purpose efficiently.

    Completeness4/5

    The toolset covers core aspects of crypto sentiment analysis: volume tracking, sentiment measurement, dominance calculation, and trend identification. A minor gap exists in lacking tools for historical data comparison or predictive analytics, but agents can work around this by combining existing tools for basic workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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