@mcpilotx/intentorch

Parses unstructured natural language commands into structured intent representations using LLM-based semantic analysis. The toolkit converts free-form user requests into machine-readable intent objects that capture user goals, required parameters, and execution context, enabling downstream MCP tool orchestration to understand what the user actually wants to accomplish rather than literal command syntax.

Automatically discovers available MCP tools from connected servers and creates runtime bindings that map parsed intents to executable tool calls. The toolkit introspects MCP server schemas, maintains a registry of available tools with their signatures and constraints, and dynamically binds intent parameters to tool arguments based on type compatibility and semantic matching.

Caches parsed intents and their execution results to avoid redundant LLM calls and tool executions for identical or similar requests. The system uses semantic similarity matching to detect duplicate intents, stores cached results with TTL-based expiration, and provides cache invalidation strategies. This reduces latency and cost for repetitive workflows.

Translates parsed intents into executable MCP workflow sequences, handling tool chaining, parameter passing between steps, and conditional execution logic. The orchestrator maintains execution state, manages tool call ordering, and coordinates multi-step workflows where outputs from one tool feed into inputs of subsequent tools, all while respecting MCP protocol constraints and error handling semantics.

Extracts parameters from natural language intents and validates them against MCP tool schemas before execution. The system performs type coercion, handles optional vs required parameters, detects missing critical arguments, and provides structured validation errors that guide users toward correcting malformed requests. Validation occurs both at intent parse time and at tool binding time.

Provides a unified interface for intent parsing and reasoning across multiple LLM providers (OpenAI, Anthropic, local models via Ollama, etc.) without changing application code. The abstraction handles provider-specific API differences, prompt formatting, response parsing, and model selection strategies, allowing developers to swap LLM backends or use multiple providers in parallel for redundancy.

Maintains execution context across multi-step workflows, tracking variables, intermediate results, and execution state. The system provides a scoped context object that persists data between tool calls, supports variable interpolation in tool parameters, and enables tools to read/write shared state. Context is isolated per workflow execution to prevent cross-contamination.

Provides structured error handling for intent parsing failures, tool execution errors, and parameter validation issues. The system captures error context, generates user-friendly error messages, and supports recovery strategies like parameter clarification requests or tool fallbacks. Errors are categorized by type (parsing, validation, execution) to enable targeted recovery logic.

Captures detailed execution logs for each step in a workflow, including intent parsing results, tool invocations, parameter values, execution times, and results. The logging system provides structured output suitable for debugging, auditing, and performance analysis. Logs can be streamed in real-time or collected for post-execution analysis.

Implements an interactive loop for refining ambiguous intents through follow-up questions and parameter clarification. When intent parsing produces ambiguous results or missing required parameters, the system generates clarification questions and re-parses user responses to refine the intent. This enables handling of complex requests that cannot be fully understood from a single user message.

Ensures all MCP interactions conform to the Model Context Protocol specification, validating request/response formats, enforcing protocol constraints, and detecting protocol violations. The system validates tool schemas, enforces MCP message structure, and provides detailed error reporting for protocol non-compliance to prevent integration issues with MCP servers.