ARCHITECTURE.md•12 kB
# AI Agent Generator MCP Server - Architecture Specification
## Overview
The AI Agent Generator MCP Server is designed to enable any MCP-compatible AI client to autonomously create and deploy AI agents through natural language instructions, similar to SmythOS Agent Weaver functionality.
## Core Philosophy
**Meta-Agent Architecture**: This MCP server acts as a "meta-agent" - an AI system that creates other AI agents by:
1. Parsing natural language specifications
2. Mapping requirements to technical components
3. Generating executable agent workflows
4. Providing deployment and management capabilities
## System Architecture
```
AI Client (Claude, GPT-4, etc.)
|
| MCP Protocol (JSON-RPC)
|
┌───▼────────────────────────────────────┐
│ AI Agent Generator MCP Server │
├─────────────────────────────────────────┤
│ Tools: │
│ ├── parse_agent_specification │
│ ├── generate_agent_workflow │
│ ├── map_components │
│ ├── validate_agent_design │
│ ├── deploy_agent │
│ ├── test_agent │
│ └── manage_agent_lifecycle │
├─────────────────────────────────────────┤
│ Resources: │
│ ├── component_library │
│ ├── workflow_templates │
│ ├── deployment_configs │
│ └── agent_registry │
└─────────────────────────────────────────┘
|
| Generated Artifacts
|
┌───▼────────────────────────────────────┐
│ Agent Execution Environment │
│ ├── Docker Containers │
│ ├── Serverless Functions │
│ ├── MCP Servers │
│ └── API Endpoints │
└─────────────────────────────────────────┘
```
## Core Components
### 1. Agent Specification Parser
**Purpose**: Converts natural language descriptions into structured agent specifications
**Input Processing**:
- Natural language prompts
- Goal definitions
- Constraint specifications
- Input/output requirements
- Integration needs
**Output**: Structured JSON specification following Agent Definition Schema
### 2. Component Library
**Standard Components**:
- **LLM Integrations**: OpenAI, Anthropic, Google, Local models
- **Data Sources**: APIs, Databases, Files, Web scraping
- **Processing Units**: Text analysis, Data transformation, Logic operations
- **Output Generators**: File creation, API calls, Notifications
- **Control Flow**: Conditionals, Loops, Error handling
- **Security**: Authentication, Rate limiting, Validation
### 3. Workflow Generator
**Capabilities**:
- Visual workflow representation (Mermaid diagrams)
- Executable code generation (Python, Node.js, Docker)
- MCP server templates
- API endpoint definitions
- Configuration management
### 4. Deployment Engine
**Target Platforms**:
- Docker containers
- Serverless functions (AWS Lambda, Vercel, etc.)
- MCP server instances
- Standalone applications
- Cloud platforms (Hugging Face Spaces, Railway, etc.)
## Tool Specifications
### Primary Tools
#### 1. `parse_agent_specification`
**Purpose**: Parse natural language into structured agent specification
**Parameters**:
```json
{
"prompt": "string", // Natural language description
"context": "object", // Optional context about existing systems
"constraints": "object", // Technical or business constraints
"preferences": "object" // Deployment, framework, or style preferences
}
```
**Returns**:
```json
{
"specification": {
"name": "string",
"description": "string",
"goal": "string",
"inputs": ["array of input definitions"],
"outputs": ["array of output definitions"],
"skills": ["array of required capabilities"],
"constraints": ["array of constraints"],
"integrations": ["array of external service requirements"]
},
"confidence_score": "number",
"missing_info": ["array of clarification questions"]
}
```
#### 2. `generate_agent_workflow`
**Purpose**: Create executable workflow from specification
**Parameters**:
```json
{
"specification": "object", // From parse_agent_specification
"target_platform": "string", // docker|serverless|mcp|api
"framework": "string", // langchain|llamaindex|custom
"language": "string" // python|typescript|javascript
}
```
**Returns**:
```json
{
"workflow": {
"components": ["array of workflow components"],
"connections": ["array of component connections"],
"configuration": "object"
},
"code": {
"files": ["array of generated code files"],
"dependencies": ["array of required packages"],
"deployment_config": "object"
},
"visualization": "string" // Mermaid diagram
}
```
#### 3. `map_components`
**Purpose**: Map agent requirements to available components
**Parameters**:
```json
{
"requirements": ["array of required capabilities"],
"preferences": "object", // Performance, cost, complexity preferences
"existing_components": ["array of available components"]
}
```
**Returns**:
```json
{
"component_mapping": [
{
"requirement": "string",
"component": "object",
"alternatives": ["array of alternative components"],
"confidence": "number"
}
],
"missing_components": ["array of requirements without matches"],
"custom_component_suggestions": ["array of suggested custom components"]
}
```
#### 4. `validate_agent_design`
**Purpose**: Validate agent design for correctness and best practices
**Parameters**:
```json
{
"workflow": "object", // Generated workflow
"specification": "object", // Original specification
"validation_rules": ["array of validation rules to apply"]
}
```
**Returns**:
```json
{
"is_valid": "boolean",
"validation_results": [
{
"rule": "string",
"status": "pass|fail|warning",
"message": "string",
"suggestions": ["array of improvement suggestions"]
}
],
"performance_estimates": "object",
"cost_estimates": "object"
}
```
#### 5. `deploy_agent`
**Purpose**: Deploy generated agent to target platform
**Parameters**:
```json
{
"workflow": "object", // Validated workflow
"deployment_config": "object", // Platform-specific config
"environment": "string", // development|staging|production
"secrets": "object" // API keys, credentials (handled securely)
}
```
**Returns**:
```json
{
"deployment_id": "string",
"endpoint": "string", // Access URL or connection details
"status": "string", // deploying|ready|failed
"logs": ["array of deployment logs"],
"monitoring_urls": ["array of monitoring dashboards"]
}
```
#### 6. `test_agent`
**Purpose**: Run comprehensive tests on deployed agent
**Parameters**:
```json
{
"deployment_id": "string",
"test_cases": ["array of test scenarios"],
"test_type": "string" // unit|integration|performance|security
}
```
**Returns**:
```json
{
"test_results": [
{
"test_name": "string",
"status": "pass|fail",
"execution_time": "number",
"output": "object",
"errors": ["array of errors if any"]
}
],
"overall_status": "string",
"performance_metrics": "object",
"recommendations": ["array of optimization suggestions"]
}
```
#### 7. `manage_agent_lifecycle`
**Purpose**: Handle agent lifecycle operations
**Parameters**:
```json
{
"deployment_id": "string",
"action": "string", // start|stop|restart|update|delete|scale
"parameters": "object" // Action-specific parameters
}
```
**Returns**:
```json
{
"action_id": "string",
"status": "string", // executing|completed|failed
"result": "object", // Action-specific results
"logs": ["array of action logs"]
}
```
### Helper Tools
#### 8. `get_component_library`
**Purpose**: Retrieve available components and their capabilities
#### 9. `get_workflow_templates`
**Purpose**: Get predefined workflow templates for common use cases
#### 10. `estimate_resources`
**Purpose**: Estimate computational resources and costs for agent
#### 11. `generate_documentation`
**Purpose**: Create comprehensive documentation for generated agent
#### 12. `export_agent_config`
**Purpose**: Export agent configuration in various formats (JSON, YAML, etc.)
## Resources
### 1. Component Library Resource
**URI**: `component-library://standard`
**Content**: JSON catalog of available components with schemas and examples
### 2. Workflow Templates Resource
**URI**: `workflow-templates://category/{category}`
**Content**: Pre-built workflow templates for common patterns
### 3. Deployment Configurations Resource
**URI**: `deployment-configs://platform/{platform}`
**Content**: Platform-specific deployment configuration templates
### 4. Agent Registry Resource
**URI**: `agent-registry://user/{user_id}`
**Content**: Registry of created and deployed agents for a user
## Data Schemas
### Agent Definition Schema
```json
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"metadata": {
"name": "string",
"version": "string",
"description": "string",
"tags": ["array of strings"]
},
"specification": {
"goal": "string",
"inputs": ["array of input schemas"],
"outputs": ["array of output schemas"],
"skills": ["array of skill definitions"],
"constraints": ["array of constraint definitions"]
},
"architecture": {
"components": ["array of component definitions"],
"connections": ["array of connection definitions"],
"configuration": "object"
},
"deployment": {
"platform": "string",
"resources": "object",
"environment": "object"
}
}
}
```
## Security Considerations
1. **Input Validation**: All inputs are validated against schemas
2. **Sandboxed Execution**: Generated code runs in isolated environments
3. **Access Control**: User-based permissions for agent creation and deployment
4. **Secret Management**: Secure handling of API keys and credentials
5. **Code Review**: Optional human review step before deployment
6. **Monitoring**: Comprehensive logging and monitoring of generated agents
## Integration Patterns
### With Existing MCP Clients
- **Claude Desktop**: Natural language agent creation interface
- **VS Code Extensions**: Integrated development workflow
- **Custom Applications**: API-based agent generation
### With Deployment Platforms
- **Docker**: Containerized agent deployment
- **Serverless**: AWS Lambda, Vercel Functions
- **Cloud Platforms**: Hugging Face Spaces, Railway, Fly.io
- **Edge**: Cloudflare Workers, Deno Deploy
## Future Enhancements
1. **Visual Workflow Editor**: Web-based drag-and-drop interface
2. **Agent Marketplace**: Share and discover agent templates
3. **Advanced Analytics**: Performance monitoring and optimization
4. **Multi-Agent Orchestration**: Coordinate multiple agents
5. **Natural Language Debugging**: Debug agents through conversation
6. **Auto-scaling**: Dynamic resource allocation based on usage
## Performance Targets
- **Agent Creation Time**: < 30 seconds for simple agents
- **Deployment Time**: < 2 minutes for containerized deployment
- **Success Rate**: > 95% for well-defined specifications
- **Resource Efficiency**: Minimal overhead for generated agents