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Agent Capabilities Database

The Agent Capabilities Database defines the specific functional abilities of each agent in the MOOD MNKY ecosystem. It serves as a comprehensive registry of what each agent can do, how these capabilities are implemented, and how they connect to other system components.

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Purpose and Role

The Capabilities Database defines “what” each agent can do, serving as the technical specification for agent functionality across the ecosystem.
This database stores detailed information about agent capabilities, including:
  • Technical functions and APIs
  • Implementation details and methods
  • Performance metrics and limitations
  • Permission requirements
  • Integration requirements
  • Tool connections (including MCP servers)

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Schema and Structure

  • Database Schema
  • Example Entry
{
  "capability_id": "string",
  "name": "string",
  "description": "string",
  "agent_id": "string",
  "capability_type": "string",
  "implementation": "string",
  "tools": "array",
  "permissions": "array",
  "metrics": "object",
  "status": "string",
  "version": "string",
  "mcp_servers": "array"
}

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Field Descriptions

A unique identifier for the capability.
The name of the capability in human-readable form.
A detailed description of what the capability does and how it’s used.
The ID of the agent that possesses this capability.
The category of capability (e.g., recommendation, analysis, creation).
The technical method used to implement the capability.
An array of tools used by this capability.
Required access permissions for this capability to function.
Performance metrics associated with this capability.
Current deployment status (active, beta, deprecated, etc.).
Current version number of the capability implementation.
Array of MCP servers required for this capability.

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Integration with Other Databases

The Capabilities Database integrates with multiple other databases in the system:
  • Agents Database: Links capabilities to specific agent identities
  • Integration Points Database: Defines how capabilities connect to external systems
  • Knowledge Base Database: Identifies information sources needed by each capability
  • Memory Systems Database: Specifies memory requirements for contextual capabilities
  • Training Database: Tracks how capabilities improve through training

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Implementation with OpenAI Agents SDK

Capabilities are implemented using the OpenAI Agents SDK, which provides a framework for integrating tools, maintaining context, and orchestrating agent behavior.

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Basic Capability Implementation

from agents import Agent, Tool, Runner

# Define tools for a capability
fragrance_search_tool = Tool(
    name="fragrance_search",
    description="Search for fragrances in the catalog",
    function=search_fragrances_func
)

scent_profile_tool = Tool(
    name="scent_profile",
    description="Analyze user's scent preferences and history",
    function=analyze_scent_profile_func
)

# Implement the capability within an agent
mood_mnky_agent = Agent(
    name="MOOD MNKY",
    instructions="""You are MOOD MNKY, the fragrance and self-care specialist.
    When recommending fragrances, analyze the user's preferences and match them to our catalog.
    Provide personalized, emotionally resonant recommendations that match their mood and style.""",
    tools=[fragrance_search_tool, scent_profile_tool]
)

# Execute the capability
runner = Runner()
result = await runner.run(mood_mnky_agent, "I need a calming scent for my bedroom")

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Integration with MCP Servers

Capabilities can also leverage external tools through MCP (Model Context Protocol) servers: 
from agents import Agent, Runner
from agents.mcp.server import MCPServerStdio, MCPServerSseParams

# Set up MCP server for file system access
async with MCPServerStdio(
    params={
        "command": "npx",
        "args": ["@mcp/fs-server", "--allow-paths", "./data,./content"]
    },
    name="file_system"
) as fs_server:
    
    # Set up MCP server for Shopify access
    async with MCPServerStdio(
        params={
            "command": "node",
            "args": ["./mcp-servers/shopify-server.js"]
        },
        name="shopify"
    ) as shopify_server:
        
        # Create agent with access to MCP servers
        mood_mnky_agent = Agent(
            name="MOOD MNKY",
            instructions="""You are MOOD MNKY, the fragrance and self-care specialist.
            You can access product data and user preferences to make recommendations.
            Provide personalized, emotionally resonant recommendations.""",
            mcp_servers=[fs_server, shopify_server]
        )
        
        # Execute the capability with MCP server access
        runner = Runner()
        result = await runner.run(mood_mnky_agent, "Can you recommend a fragrance based on my previous purchases?")

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Capability Types

Recommendation

Suggest products, content, or actions based on user preferences and context

Creation

Generate new content, designs, or configurations based on parameters

Analysis

Process and interpret data, providing insights and observations

Instruction

Guide users through processes, providing educational content

Integration

Connect with external systems and data sources

Memory

Store, retrieve, and manage contextual information

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Best Practices

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Capability Design

  • Single Responsibility: Each capability should do one thing well
  • Clear Documentation: Document purpose, limitations, and usage examples
  • Version Control: Track changes and improvements over time
  • Performance Monitoring: Establish metrics and monitor them consistently
  • Graceful Degradation: Design for resilience when dependencies fail

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MCP Server Integration

  • Appropriate Server Selection: Use Stdio servers for local development, SSE servers for distributed systems
  • Tool Caching: Enable caching for frequently used tools to improve performance
  • Permission Management: Carefully control what MCP servers can access
  • Error Handling: Implement robust error handling for MCP server failures
  • Monitoring: Set up tracing and logging to track MCP server interactions

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Implementation Guidelines

  • Specialized Agents: Create focused agents for specific capability domains rather than generalists
  • Clear Instructions: Provide detailed instructions for each capability
  • Contextual Awareness: Ensure capabilities have access to relevant user context
  • Testing Strategy: Develop comprehensive tests for each capability
  • Continuous Improvement: Use feedback and performance data to refine capabilities

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Additional Resources