agent-zero

Implements the Model Context Protocol (MCP) server specification to expose agent capabilities as standardized resources, tools, and prompts that client applications can discover and invoke. Uses MCP's JSON-RPC 2.0 transport layer to handle bidirectional communication between clients and the agent runtime, enabling seamless integration with Claude Desktop, IDEs, and other MCP-compatible tools without custom protocol negotiation.

Exposes agent tools through MCP's tools resource type with JSON Schema definitions that describe parameters, return types, and usage constraints. Clients can introspect available tools at runtime, automatically generate UI for tool invocation, and validate parameters before sending requests. The agent runtime parses tool schemas to enforce type safety and parameter validation before execution.

Implements agent loop that decomposes user requests into subtasks, selects appropriate tools, executes them, evaluates results, and iterates until task completion. Uses chain-of-thought reasoning to maintain context across multiple steps, track dependencies between subtasks, and make decisions about which tools to invoke next. The agent maintains execution state and can backtrack or retry failed steps with different approaches.

Exposes agent knowledge and context through MCP's resources interface, allowing clients to read and potentially write structured data that the agent uses for decision-making. Resources can represent documents, code files, configuration, or domain knowledge. The agent can reference resources during reasoning, and clients can update resources to influence agent behavior without modifying agent code.

Exposes reusable prompt templates through MCP's prompts interface with support for variable substitution and dynamic content injection. Templates can include placeholders for context, tool outputs, or user inputs that are filled at runtime. Clients can discover available prompts, request completions with specific variables, and receive structured responses that guide agent behavior.

Implements MCP's JSON-RPC 2.0 protocol with support for both request-response and streaming message patterns. Agents can send notifications to clients asynchronously, stream long-running operation results incrementally, and maintain persistent connections for real-time updates. The transport layer handles connection management, message ordering, and error recovery.

Abstracts LLM interactions behind a provider-agnostic interface that supports multiple LLM providers (OpenAI, Anthropic, local models via Ollama, etc.). Agents can switch between models at runtime based on task requirements, cost constraints, or availability. The abstraction handles provider-specific API differences, token counting, and response formatting to present a unified interface.

Implements comprehensive error handling that catches tool failures, LLM errors, and network issues, then applies configurable retry strategies (exponential backoff, jitter, max attempts). Agents can detect failure patterns and switch to alternative tools or approaches. Errors are logged with full context for debugging and monitoring.