Meta-tools

Meta-tools don't directly interact with external systems. Instead, they enhance agent capabilities by implementing agentic patterns. This section discusses workflow, agent graph, and memory meta-tools.

Workflow meta-tools

Workflow meta-tools manage the flow of agent execution:

• State management – Maintain context across multiple agent interactions

• Branching logic – Enable conditional execution paths

• Retry mechanisms – Handle failures with sophisticated retry strategies

Example frameworks with workflow meta-tools include LangGraph and Strands Agents workflow capabilities.

Agent graph meta-tools

Agent graph meta-tools coordinate multiple agents working together:

• Task delegation – Assign subtasks to specialized agents

• Result aggregation – Combine outputs from multiple agents

• Conflict resolution – Resolve disagreements between agents

Frameworks like AutoGen and CrewAI specialize in agent graph coordination.

Memory meta-tools

Memory meta-tools provide persistent storage and retrieval:

• Conversation history – Maintain context across sessions

• Knowledge bases – Store and retrieve domain-specific information

• Vector stores – Enable semantic search capabilities

MCP's resource system provides a standardized way to implement memory meta-tools that work across different agent frameworks.