Event Horizon MCP Server
An MCP server implementation that provides evolutionary solution generation and optimization capabilities for LLMs. Event Horizon enables LLMs to evolve solutions across multiple generations using consistency check evaluations and genetic algorithm principles.
Features
- Multi-generational Evolution: Evolve solutions across multiple generations
- Consistency Check Evaluation: Score solutions against multiple criteria
- Crossover Analysis: Identify best aspects from different solutions
- Convergence Detection: Automatically detect when solutions reach optimal scores
- Progress Tracking: Monitor evolution progress and statistics
- Flexible Configuration: Customize population size, generations, and thresholds
How It Works
- Initialize Evolution: Define a problem statement and consistency checks
- Generate Population: Create initial solutions for generation 0
- Evaluate Solutions: Score each solution against all consistency checks
- Evolution Analysis: Identify best-performing aspects per consistency check
- Crossover Guidance: Get recommendations for combining best aspects
- Next Generation: Create new solutions based on crossover recommendations
- Repeat: Continue until convergence or max generations reached
Tools
start_evolution
Initialize an evolutionary solution system with a problem and consistency checks.
Parameters:
problemStatement(string): The problem or challenge to solveconsistencyChecks(array): Array of evaluation criteria (strings or objects with description/weight)populationSize(integer, optional): Solutions per generation (default: 3)maxGenerations(integer, optional): Maximum generations (default: 5)convergenceThreshold(number, optional): Score threshold for completion (default: 0.95)
add_solution
Add a new solution to the current generation.
Parameters:
content(string): The solution content addressing the problemparentSolutions(array, optional): Parent solution IDs if this is a crossover
score_solution
Score a solution against a specific consistency check.
Parameters:
solutionId(string): ID of the solution to scorecheckId(string): ID of the consistency checkscore(number): Score between 0.0 and 1.0reasoning(string, optional): Explanation of the scoring rationale
evolve_generation
Evolve to the next generation using crossover analysis.
Returns crossover recommendations identifying the best-performing solution for each consistency check and guidance for creating new solutions.
get_evolution_status
Get the current status of the evolutionary system including progress, statistics, and best solution found.
Installation
Smithery (Recommended)
Install directly from the Smithery registry for the easiest setup:
npx @smithery/cli install event-horizon --client claude
This automatically configures the server for Claude Desktop. You can also customize the installation:
npx @smithery/cli install event-horizon --client claude --config '{"disableLogging": true, "populationSize": 5}'
Manual Installation
If you prefer manual installation, add this to your claude_desktop_config.json:
{
"mcpServers": {
"event-horizon": {
"command": "node",
"args": [
"/path/to/EventHorizon/dist/index.js"
]
}
}
}
Clone and Build
For development or customization:
git clone https://github.com/manasp21/EventHorizon.git
cd EventHorizon
npm install
npm run build
Advanced Configuration
Configuration Options
The Event Horizon server supports several configuration options:
disableLogging(boolean): Disable evolution progress logging (default: false)populationSize(integer): Default population size for generations (default: 3)maxGenerations(integer): Default maximum generations (default: 5)convergenceThreshold(number): Default convergence threshold (default: 0.95)
Docker
Build and run with Docker:
docker build -t event-horizon-mcp .
docker run --rm -i event-horizon-mcp
Or use in Claude Desktop config:
{
"mcpServers": {
"event-horizon": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"event-horizon-mcp"
]
}
}
}
VS Code & Other MCP Clients
For other MCP clients, you can use Smithery CLI or manual configuration. Check the Smithery documentation for client-specific instructions.
Usage Example
Here's a typical evolutionary workflow:
1. Start Evolution
{
"tool": "start_evolution",
"arguments": {
"problemStatement": "Design an efficient sorting algorithm",
"consistencyChecks": [
"Algorithm must have optimal time complexity",
"Implementation must be memory efficient",
"Code must be readable and maintainable"
],
"populationSize": 3,
"maxGenerations": 5
}
}
2. Add Initial Solutions
{
"tool": "add_solution",
"arguments": {
"content": "Implement quicksort with random pivot selection..."
}
}
3. Score Solutions
{
"tool": "score_solution",
"arguments": {
"solutionId": "generated_solution_id",
"checkId": "check_1",
"score": 0.8
}
}
4. Evolve Generation
{
"tool": "evolve_generation",
"arguments": {}
}
The system will provide crossover recommendations like:
{
"crossoverRecommendations": [
{
"checkId": "check_1",
"bestSolutionId": "solution_a",
"score": 0.9,
"relevantContent": "Use random pivot selection for optimal performance..."
}
]
}
5. Create Next Generation
Based on recommendations, create new solutions combining the best aspects from previous generation.
Environment Variables
DISABLE_EVOLUTION_LOGGING: Set to"true"to disable progress logging
Building
npm run build
npm run prepare
Docker
docker build -t mcp/event-horizon -f Dockerfile .
Example Use Cases
- Algorithm Design: Evolve optimal algorithms for specific problems
- Code Optimization: Improve code performance across multiple metrics
- Creative Writing: Evolve stories or content meeting multiple criteria
- Problem Solving: Generate solutions optimized for multiple constraints
- System Design: Evolve architectural solutions balancing various requirements
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License.