{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-mcpilotxintentorch","slug":"npm-mcpilotxintentorch","name":"@mcpilotx/intentorch","type":"mcp","url":"https://www.npmjs.com/package/@mcpilotx/intentorch","page_url":"https://unfragile.ai/npm-mcpilotxintentorch","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","intent","orchestration","ai","workflow","automation","typescript","sdk","toolkit"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-mcpilotxintentorch__cap_0","uri":"capability://planning.reasoning.natural.language.to.intent.parsing","name":"natural-language-to-intent-parsing","description":"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.","intents":["I want to convert user requests into structured tasks my MCP server can execute","I need to extract intent and parameters from conversational input without rigid command parsing","I want to handle ambiguous or multi-step user requests by breaking them into discrete intents"],"best_for":["teams building conversational AI agents with MCP backends","developers creating intent-driven automation workflows","builders prototyping natural language interfaces to tool ecosystems"],"limitations":["Intent parsing accuracy depends on LLM quality and prompt engineering; ambiguous requests may produce incorrect intent structures","No built-in intent validation or schema enforcement — requires external validation layer for production safety","Latency scales with LLM inference time; real-time applications may experience 500ms-2s delays depending on model choice"],"requires":["TypeScript/JavaScript runtime (Node.js 16+)","API key for LLM provider (OpenAI, Anthropic, or compatible)","MCP server implementation to receive parsed intents"],"input_types":["text (natural language commands)","conversational context (optional chat history)"],"output_types":["structured intent objects (JSON)","parameter maps","execution metadata"],"categories":["planning-reasoning","natural-language-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_1","uri":"capability://tool.use.integration.mcp.tool.discovery.and.binding","name":"mcp-tool-discovery-and-binding","description":"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.","intents":["I want my agent to automatically discover what tools are available without hardcoding tool definitions","I need to map user intents to the right MCP tool based on capability matching","I want to handle tools from multiple MCP servers without manual configuration"],"best_for":["developers building extensible MCP agents","teams managing heterogeneous tool ecosystems across multiple MCP servers","builders creating zero-configuration orchestration layers"],"limitations":["Tool discovery is synchronous at startup; adding new tools requires agent restart or manual refresh","No automatic conflict resolution when multiple tools match an intent — requires explicit tool selection strategy","Parameter binding relies on schema matching; ambiguous or poorly-documented tool signatures may cause binding failures"],"requires":["Active MCP server(s) with schema-compliant tool definitions","TypeScript/JavaScript runtime with MCP client library","Tool schemas must follow MCP specification for discovery to work"],"input_types":["MCP server connection details","parsed intent objects","tool schema metadata"],"output_types":["tool binding registry (JSON)","executable tool call objects","parameter validation results"],"categories":["tool-use-integration","mcp-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_10","uri":"capability://memory.knowledge.intent.caching.and.deduplication","name":"intent-caching-and-deduplication","description":"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.","intents":["I want to avoid re-parsing identical user requests to reduce LLM API costs","I need to cache tool execution results for frequently-used operations","I want to detect similar requests and reuse cached intents when appropriate"],"best_for":["teams running high-volume agent systems with repetitive requests","developers optimizing for cost (reducing LLM API calls)","builders implementing caching layers for performance"],"limitations":["Cache hit rate depends on request similarity; diverse requests may have low cache utilization","No distributed caching; cache is local to process, limiting effectiveness in multi-instance deployments","Cache invalidation is time-based (TTL); no event-driven invalidation for data-dependent caches"],"requires":["In-memory cache storage (or external cache like Redis)","Semantic similarity matching (embedding-based or hash-based)","TTL configuration for cache expiration"],"input_types":["natural language requests","parsed intent objects","tool execution results"],"output_types":["cached intent objects","cached execution results","cache hit/miss metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_2","uri":"capability://automation.workflow.intent.to.mcp.workflow.orchestration","name":"intent-to-mcp-workflow-orchestration","description":"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.","intents":["I want to execute multi-step workflows where tool outputs become inputs to other tools","I need to handle conditional logic (if-then-else) based on tool results within a workflow","I want to orchestrate tools across multiple MCP servers in a single coherent workflow"],"best_for":["teams building complex automation workflows with multiple interdependent steps","developers creating agent systems that need to reason about tool dependencies","builders implementing multi-tool pipelines without manual orchestration code"],"limitations":["No built-in loop constructs (for/while) — complex iterative workflows require external state management","Execution is synchronous; parallel tool execution not supported, limiting throughput for independent operations","Error recovery is basic; no automatic retry logic or fallback strategies without explicit workflow definition"],"requires":["Parsed intent objects with clear execution semantics","MCP tool bindings from tool discovery capability","TypeScript/JavaScript runtime with async/await support"],"input_types":["structured intent objects","tool binding registry","execution context (variables, state)"],"output_types":["workflow execution results","step-by-step execution logs","final output from last tool in chain"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_3","uri":"capability://data.processing.analysis.parameter.extraction.and.validation","name":"parameter-extraction-and-validation","description":"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.","intents":["I want to ensure user-provided parameters match tool requirements before execution","I need to extract structured data from natural language and validate it's complete","I want to provide clear error messages when parameters are missing or invalid"],"best_for":["developers building user-facing conversational agents","teams requiring strict parameter validation before tool execution","builders implementing guardrails against malformed tool calls"],"limitations":["Type coercion is best-effort; complex nested types or custom objects may fail validation","No semantic validation (e.g., checking if a date is in the future) — only schema-level validation","Error messages are generated from schema mismatches; domain-specific validation requires custom handlers"],"requires":["MCP tool schemas with complete type definitions","Parsed intent objects with parameter data","JSON Schema or equivalent type specification"],"input_types":["natural language parameter values","tool schema definitions","type hints from intent parsing"],"output_types":["validated parameter objects","validation error reports","type-coerced values"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_4","uri":"capability://tool.use.integration.multi.provider.llm.abstraction","name":"multi-provider-llm-abstraction","description":"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.","intents":["I want to use different LLM providers without rewriting intent parsing logic","I need to switch between OpenAI, Anthropic, and local models based on cost or latency requirements","I want fallback LLM providers if my primary provider is unavailable"],"best_for":["teams building cost-conscious agents (local models for non-critical tasks)","developers requiring multi-provider redundancy for production reliability","builders experimenting with different LLM backends during development"],"limitations":["Prompt engineering must account for model differences; same prompt may produce different intent structures across providers","Response parsing assumes consistent JSON output format; some models may require custom response handlers","No automatic model selection based on task complexity; requires explicit configuration"],"requires":["API keys for desired LLM providers (OpenAI, Anthropic, etc.)","Network access to provider endpoints or local Ollama instance","TypeScript/JavaScript runtime"],"input_types":["natural language prompts","provider configuration","model selection strategy"],"output_types":["parsed intent objects","provider metadata (model used, latency, cost)","fallback results from alternate providers"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_5","uri":"capability://memory.knowledge.execution.context.and.state.management","name":"execution-context-and-state-management","description":"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.","intents":["I want tool outputs to be available as inputs to subsequent tools in a workflow","I need to track variables and state across multiple tool invocations","I want to pass user-provided context (like user ID or session data) through the entire workflow"],"best_for":["developers building stateful multi-step workflows","teams implementing session-aware agent systems","builders creating workflows with complex data dependencies"],"limitations":["Context is in-memory only; no persistence across process restarts without external storage","No built-in context versioning or rollback; state mutations are permanent within execution","Context size is unbounded; large intermediate results can cause memory issues in long-running workflows"],"requires":["TypeScript/JavaScript runtime with object reference semantics","Workflow execution engine (provided by orchestration capability)","Optional: external state store for persistence"],"input_types":["initial context variables","tool execution results","user-provided session data"],"output_types":["context object with accumulated state","variable interpolation results","final workflow output"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_6","uri":"capability://safety.moderation.error.handling.and.recovery","name":"error-handling-and-recovery","description":"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.","intents":["I want clear error messages when intent parsing fails so users can rephrase requests","I need to handle tool execution failures gracefully without crashing the workflow","I want to ask users for clarification when parameters are ambiguous or missing"],"best_for":["teams building user-facing conversational agents","developers requiring robust error reporting for debugging","builders implementing graceful degradation in agent systems"],"limitations":["Error recovery is passive (reporting only); no automatic retry or self-healing without explicit handlers","Error categorization is limited to parsing/validation/execution; domain-specific errors require custom classification","No built-in user interaction for clarification; requires external dialogue system for follow-up questions"],"requires":["Error handling middleware in orchestration layer","Structured error types for categorization","Optional: external dialogue system for clarification"],"input_types":["exceptions from intent parsing","tool execution errors","validation failures"],"output_types":["structured error objects","user-friendly error messages","recovery suggestions"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_7","uri":"capability://automation.workflow.workflow.logging.and.observability","name":"workflow-logging-and-observability","description":"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.","intents":["I want to debug why a workflow produced unexpected results by reviewing execution logs","I need to audit which tools were called and with what parameters for compliance","I want to identify performance bottlenecks in multi-step workflows"],"best_for":["developers debugging complex workflows","teams requiring audit trails for compliance","builders optimizing workflow performance"],"limitations":["Logging adds overhead; high-frequency workflows may experience latency impact from log serialization","No built-in log aggregation or centralized storage; requires external logging infrastructure","Sensitive data (API keys, user PII) may be captured in logs without explicit filtering"],"requires":["Logging middleware in orchestration layer","Optional: external logging service (e.g., ELK, Datadog)","Structured logging format (JSON)"],"input_types":["workflow execution events","tool invocation details","timing information"],"output_types":["structured execution logs (JSON)","real-time log streams","performance metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_8","uri":"capability://planning.reasoning.intent.refinement.and.clarification.loop","name":"intent-refinement-and-clarification-loop","description":"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.","intents":["I want to handle ambiguous user requests by asking clarifying questions","I need to collect missing parameters interactively rather than failing","I want to support multi-turn conversations where intent is refined over several exchanges"],"best_for":["teams building conversational agents with iterative intent refinement","developers handling complex requests requiring parameter collection","builders implementing dialogue-based workflows"],"limitations":["Clarification loop adds latency; each round requires LLM inference for question generation and response parsing","No built-in conversation history management; requires external dialogue context tracking","Question generation quality depends on LLM capability; poor questions may confuse users"],"requires":["LLM provider for question generation","Intent parsing capability","External dialogue context management (optional but recommended)"],"input_types":["ambiguous intent objects","user clarification responses","conversation history"],"output_types":["clarification questions","refined intent objects","conversation transcript"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-mcpilotxintentorch__cap_9","uri":"capability://tool.use.integration.mcp.protocol.compliance.and.validation","name":"mcp-protocol-compliance-and-validation","description":"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.","intents":["I want to ensure my MCP tool calls are protocol-compliant before sending to servers","I need to validate that MCP servers are responding with correct message formats","I want to catch protocol violations early to prevent integration failures"],"best_for":["developers integrating with multiple MCP servers","teams requiring strict protocol compliance for production systems","builders implementing MCP server adapters"],"limitations":["Validation is schema-based; cannot detect semantic violations (e.g., logically invalid tool calls)","Protocol version compatibility must be explicitly configured; no automatic version negotiation","Validation adds latency; high-frequency tool calls may experience measurable overhead"],"requires":["MCP protocol specification","JSON Schema validators","TypeScript/JavaScript runtime"],"input_types":["MCP request/response messages","tool schemas","protocol version information"],"output_types":["validation results (pass/fail)","protocol violation reports","corrected messages (if auto-correction enabled)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["TypeScript/JavaScript runtime (Node.js 16+)","API key for LLM provider (OpenAI, Anthropic, or compatible)","MCP server implementation to receive parsed intents","Active MCP server(s) with schema-compliant tool definitions","TypeScript/JavaScript runtime with MCP client library","Tool schemas must follow MCP specification for discovery to work","In-memory cache storage (or external cache like Redis)","Semantic similarity matching (embedding-based or hash-based)","TTL configuration for cache expiration","Parsed intent objects with clear execution semantics"],"failure_modes":["Intent parsing accuracy depends on LLM quality and prompt engineering; ambiguous requests may produce incorrect intent structures","No built-in intent validation or schema enforcement — requires external validation layer for production safety","Latency scales with LLM inference time; real-time applications may experience 500ms-2s delays depending on model choice","Tool discovery is synchronous at startup; adding new tools requires agent restart or manual refresh","No automatic conflict resolution when multiple tools match an intent — requires explicit tool selection strategy","Parameter binding relies on schema matching; ambiguous or poorly-documented tool signatures may cause binding failures","Cache hit rate depends on request similarity; diverse requests may have low cache utilization","No distributed caching; cache is local to process, limiting effectiveness in multi-instance deployments","Cache invalidation is time-based (TTL); no event-driven invalidation for data-dependent caches","No built-in loop constructs (for/while) — complex iterative workflows require external state management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.904Z","last_scraped_at":"2026-05-03T14:24:00.326Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-mcpilotxintentorch","compare_url":"https://unfragile.ai/compare?artifact=npm-mcpilotxintentorch"}},"signature":"2wYEBhY9WPhy0d/G+a66m/XSDbuZi0IWaCm4j0kZPmSDoR9yZsENt+95wlo7YQIJqEg/Vw88bLKcn4uUCCAQCg==","signedAt":"2026-06-20T15:17:08.359Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-mcpilotxintentorch","artifact":"https://unfragile.ai/npm-mcpilotxintentorch","verify":"https://unfragile.ai/api/v1/verify?slug=npm-mcpilotxintentorch","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}