heisenberg-mcp-server 0.3.0

A Model Context Protocol server that enables capable LLMs to work with Heisenberg features, such as inference

Heisenberg FastMCP Agent

A Model Context Protocol server that provides tools to interact with Heisenberg services for real-time data analysis, social media monitoring, and specialized AI agent interactions.

Available Tools

Core Agent Tools

• list_of_agents - Retrieves all available AI agents for specialized data analysis

  • When to use: Automatically called for any request involving current events, real-time data, social media analysis, or domain-specific information
  • Returns: List of available agents with their IDs and descriptions

• inference_from_prompt - Queries specialized agents for current data or domain-specific analysis

  • Required arguments:
    • agent_id (integer): The ID of the agent (obtained from list_of_agents)
    • prompt (string): The text prompt for analysis

Twitter/X Trend Analysis Tools

• twitter_trends_options - Get available parameters for Twitter trend analysis

  • When to use: MUST be called first before using twitter_trends()
  • Returns: Available verticals (categories) and durations (time periods)

• twitter_trends - Analyze Twitter/X trends for specific categories and time periods

  • Required arguments:
    • vertical (string): Category from options (e.g., "politics", "tech", "sports")
    • duration (string): Time period from options (e.g., "1h", "24h", "7d")

Installation

Using uv (recommended)

uvx heisenberg-mcp-server

Using PIP

pip install heisenberg-mcp-server

Run as a script:

python -m heisenberg

Configuration

Configure for Claude.app

Using uvx

{
    "mcpServers": {
        "heisenberg": {
            "command": "uvx",
            "args": [
                "heisenberg-mcp-server"
            ],
            "env": {
                "HEISENBERG_TOKEN": "*****",
                "HEISENBERG_KEY": "*****"
            },
            "timeout": 60000
        }
    }
}

Using pip installation

{
    "mcpServers": {
        "heisenberg": {
            "command": "python",
            "args": [
                "-m",
                "heisenberg"
            ],
            "env": {
                "HEISENBERG_TOKEN": "*****",
                "HEISENBERG_KEY": "*****"
            },
            "timeout": 60000
        }
    }
}

Example Interactions

1. Political Analysis on X/Twitter

{
    "agent_id": 1,
    "prompt": "What are the latest political conversations on X about the upcoming elections?"
}

Response:

[
    {
        "tweet_text": "Special election results hint at what's next for Congress' power balance",
        "tweet_time": "2025-04-02T11:44:41Z",
        "tweet_url": "https://x.com/FoxNews/status/1907398780615373105"
    }
]

2. Twitter Trends Analysis

First, get available options:

twitter_trends_options()
# Returns: {"verticals": ["AI", "Crypto", "Football"], "durations": ["last_3_day", "last_day", "last_week"]}

Then analyze trends:

twitter_trends(vertical="Crypto", duration="last_day")
# Returns trending crypto topics from the last day

Workflow Summary

1. For general current data needs:

   • list_of_agents() → Select relevant agent → inference_from_prompt()

2. For Twitter/X specific trends:

   • twitter_trends_options() → Select parameters → twitter_trends()

Use Cases

• Real-time Social Media Monitoring: Track conversations and trends across platforms
• Current Events Analysis: Get up-to-date information on news and world events
• Market Intelligence: Monitor financial trends and market sentiment
• Technology Trends: Track emerging tech topics and developer discussions
• Entertainment & Sports: Follow trending topics and cultural phenomena

Requirements

• Valid Heisenberg API tokens (HEISENBERG_TOKEN and HEISENBERG_KEY)
• Network access to Heisenberg services