MCP Agent Tool Adapter acts as a middleware layer between language model-based agents and external tool implementations. By registering function signatures or tool descriptors, the framework automatically parses agent outputs that specify tool calls, dispatches the appropriate adapter, handles input serialization, and returns the result back to the reasoning context. Features include dynamic tool discovery, concurrency control, logging, and error handling pipelines. It supports defining custom tool interfaces and integrating cloud or on-premise services. This enables building complex, multi-tool workflows such as API orchestration, data retrieval, and automated operations without modifying underlying agent code.

Rigging is a developer-focused framework that streamlines the creation and orchestration of AI agents. It provides tool and function registration, context and memory management, workflow chaining, callback events, and logging. Developers can integrate multiple LLM providers, define custom plugins, and assemble multi-step pipelines. Rigging’s type-safe TypeScript SDK ensures modularity and reusability, accelerating AI agent development for chatbots, data processing, and content generation tasks.