{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-open-mercatoai-assistant","slug":"npm-open-mercatoai-assistant","name":"@open-mercato/ai-assistant","type":"mcp","url":"https://www.npmjs.com/package/@open-mercato/ai-assistant","page_url":"https://unfragile.ai/npm-open-mercatoai-assistant","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-open-mercatoai-assistant__cap_0","uri":"capability://tool.use.integration.mcp.based.tool.discovery.and.dynamic.capability.registration","name":"mcp-based tool discovery and dynamic capability registration","description":"Discovers and registers tools dynamically through the Model Context Protocol (MCP) standard, enabling AI assistants to introspect available capabilities without hardcoded tool definitions. Uses MCP's resource and tool announcement mechanisms to maintain a live registry of executable functions that can be invoked by LLM agents, supporting both local and remote tool providers.","intents":["I want my AI assistant to automatically discover what tools are available without manually defining them","I need to add new tools to my assistant without redeploying or modifying core code","I want to integrate third-party MCP servers and expose their capabilities to my LLM"],"best_for":["teams building extensible AI agents with pluggable tool ecosystems","developers integrating multiple MCP-compliant services into a single assistant","organizations standardizing on MCP for tool interoperability across AI systems"],"limitations":["MCP server availability directly impacts assistant capability — no fallback if tool provider is offline","Tool discovery latency depends on MCP server response times; no built-in caching of tool schemas","Requires all tool providers to implement MCP spec correctly; incompatible or malformed servers will cause registration failures"],"requires":["Node.js 16+","MCP-compliant tool servers or providers","@open-mercato/ai-assistant npm package","Network connectivity to MCP servers"],"input_types":["MCP tool schema definitions","MCP server endpoints or stdio connections"],"output_types":["structured tool registry (JSON schema)","callable tool references for LLM function calling"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_1","uri":"capability://tool.use.integration.schema.based.function.calling.with.multi.provider.llm.support","name":"schema-based function calling with multi-provider llm support","description":"Translates discovered MCP tool schemas into function-calling format compatible with multiple LLM providers (OpenAI, Anthropic, etc.), handling schema normalization and provider-specific function calling conventions. Manages the request-response cycle for tool invocation, including parameter validation against schemas and error handling for failed tool calls.","intents":["I want my assistant to call tools using the native function-calling APIs of different LLM providers","I need to validate tool parameters against their schemas before execution","I want to handle tool call failures gracefully and retry or fallback"],"best_for":["developers building LLM agents that need to work across multiple model providers","teams avoiding vendor lock-in by supporting OpenAI, Anthropic, and other LLM APIs","builders who need schema validation and type safety for tool parameters"],"limitations":["Provider-specific function calling syntax differences require adapter code; not all providers support identical schema features","Parameter validation happens at call time, not at schema registration — invalid schemas may not be caught until execution","No built-in retry logic for transient tool failures; applications must implement their own retry strategies"],"requires":["API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","Tool schemas in MCP-compatible format","Network connectivity to LLM provider APIs"],"input_types":["MCP tool schemas","LLM function-calling requests","tool parameter objects (JSON)"],"output_types":["provider-specific function call requests","tool execution results","error responses with diagnostic information"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_2","uri":"capability://text.generation.language.conversational.chat.interface.with.tool.aware.context.management","name":"conversational chat interface with tool-aware context management","description":"Maintains a conversation history that tracks both user messages and tool execution results, providing the LLM with full context about what tools have been called and their outcomes. Implements a chat loop that interleaves user input, LLM reasoning, tool invocation, and result integration, handling multi-turn conversations where tool calls may depend on previous results.","intents":["I want my assistant to remember what tools it called in previous turns and use those results in new requests","I need to build a chat interface where the assistant can iteratively call tools to answer complex questions","I want to see the full conversation history including tool calls and their results"],"best_for":["developers building interactive AI assistants for Open Mercato workflows","teams needing multi-turn tool orchestration where later steps depend on earlier tool results","builders who want transparent, auditable tool execution logs within conversations"],"limitations":["Context window is limited by the underlying LLM; long conversations may exceed token limits and require summarization or pruning","No built-in persistence — conversation history exists only in memory; requires external storage for durability","Tool execution results are included verbatim in context; large result sets can quickly consume token budget"],"requires":["LLM API with function-calling support","MCP tool servers for tool execution","Memory to store conversation history (in-process or external)"],"input_types":["user messages (text)","tool execution results (JSON/structured data)"],"output_types":["assistant responses (text)","tool invocation requests","conversation history (structured log)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_3","uri":"capability://data.processing.analysis.tool.result.interpretation.and.context.injection","name":"tool result interpretation and context injection","description":"Processes raw tool execution results from MCP servers and injects them into the LLM context in a format the model can reason about. Handles different result types (JSON, text, structured data) and formats them appropriately for the LLM, managing result truncation or summarization if outputs exceed context limits.","intents":["I want the assistant to understand and reason about tool results, not just execute tools blindly","I need to handle large tool outputs without overwhelming the LLM's context window","I want to format tool results in a way that makes them easy for the LLM to parse and use"],"best_for":["developers building agents that need to interpret complex tool outputs (APIs, databases, file systems)","teams dealing with large result sets that need intelligent truncation or summarization","builders who want structured result handling rather than raw string concatenation"],"limitations":["Result interpretation is format-dependent; no universal handling for all possible tool output types","Truncation or summarization of large results may lose important information; no built-in heuristics for what to keep","Assumes tool results are serializable to JSON or text; binary or streaming results require custom handling"],"requires":["Tool execution results from MCP servers","LLM context window with available tokens","Result format specifications or schemas"],"input_types":["raw tool execution results (JSON, text, structured data)","result metadata (size, type, schema)"],"output_types":["formatted results for LLM context","truncated/summarized results if needed","result metadata for debugging"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_4","uri":"capability://tool.use.integration.mcp.server.lifecycle.management.and.connection.handling","name":"mcp server lifecycle management and connection handling","description":"Manages the lifecycle of MCP server connections, including initialization, health checking, and graceful shutdown. Handles both stdio-based and network-based MCP server connections, implementing reconnection logic and error recovery for transient failures. Provides connection pooling and resource cleanup to prevent leaks.","intents":["I want my assistant to automatically connect to and manage MCP servers without manual setup","I need the assistant to handle MCP server crashes or disconnections gracefully","I want to ensure MCP connections are properly cleaned up when the assistant shuts down"],"best_for":["teams running multiple MCP servers and needing centralized connection management","developers building production AI assistants that need reliable tool availability","builders who want to avoid manual connection boilerplate and error handling"],"limitations":["Reconnection logic may introduce latency if MCP servers are frequently restarting; no built-in exponential backoff configuration","Health checking adds overhead; no way to disable it for performance-critical applications","Connection pooling is in-process only; no support for distributed connection management across multiple assistant instances"],"requires":["MCP servers running and accessible (stdio or network)","Node.js process with sufficient file descriptors for connection pooling","Proper cleanup handlers in application shutdown logic"],"input_types":["MCP server connection parameters (stdio path or network address)","health check configuration"],"output_types":["connection status","health check results","connection error diagnostics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_5","uri":"capability://safety.moderation.tool.execution.error.handling.and.diagnostic.reporting","name":"tool execution error handling and diagnostic reporting","description":"Captures and processes errors from tool execution, including schema validation failures, network errors, and tool-specific exceptions. Provides detailed diagnostic information about what failed and why, enabling the LLM to make informed decisions about retrying, using alternative tools, or reporting errors to the user. Implements structured error logging for debugging.","intents":["I want the assistant to gracefully handle tool failures without crashing","I need detailed error information to debug why a tool call failed","I want the assistant to suggest alternative approaches when a tool fails"],"best_for":["developers building production AI assistants that need robust error handling","teams debugging complex tool orchestration workflows","builders who want transparent error reporting and diagnostics"],"limitations":["Error handling is reactive, not preventive — validation happens at execution time, not schema registration","No built-in retry logic; applications must implement their own retry strategies based on error types","Error messages depend on tool implementation quality; poorly-designed tools may provide unhelpful error information"],"requires":["Tool execution infrastructure","Error logging/monitoring system (optional but recommended)","LLM capable of reasoning about error messages"],"input_types":["tool execution failures","validation errors","network/connection errors"],"output_types":["structured error objects","diagnostic logs","error messages for LLM context"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_6","uri":"capability://tool.use.integration.open.mercato.domain.specific.tool.integration","name":"open mercato domain-specific tool integration","description":"Provides pre-built integrations with Open Mercato-specific tools and workflows, including marketplace operations, order management, and commerce-related functions. Implements domain-specific tool schemas and execution logic tailored to Open Mercato's data models and APIs, enabling assistants to perform marketplace-specific tasks without custom tool development.","intents":["I want my assistant to understand and execute Open Mercato marketplace operations","I need the assistant to manage orders, inventory, or other commerce workflows","I want to build marketplace-specific AI features without writing custom tool code"],"best_for":["Open Mercato platform users building AI-powered marketplace assistants","teams integrating AI into existing Open Mercato deployments","developers who want pre-built marketplace domain knowledge in their assistants"],"limitations":["Tool set is limited to Open Mercato's supported operations; custom marketplace workflows require extending the tool set","Domain-specific tools may not be suitable for non-marketplace use cases","Requires Open Mercato API access and authentication; tools will fail without proper credentials"],"requires":["Open Mercato API credentials/access","Open Mercato account or deployment","@open-mercato/ai-assistant package"],"input_types":["marketplace operation requests","order/inventory data","user intents specific to commerce"],"output_types":["marketplace operation results","order confirmations","inventory updates","commerce-specific data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-open-mercatoai-assistant__cap_7","uri":"capability://text.generation.language.streaming.response.generation.with.incremental.tool.execution","name":"streaming response generation with incremental tool execution","description":"Supports streaming LLM responses while tools are being executed, enabling real-time feedback to users as the assistant reasons and acts. Implements incremental result injection where tool results become available and are streamed to the client as they complete, rather than waiting for all tools to finish before responding.","intents":["I want users to see the assistant's reasoning and tool execution in real-time","I need to reduce perceived latency by streaming responses as they're generated","I want to show tool execution progress without blocking the response stream"],"best_for":["developers building interactive chat interfaces where latency perception matters","teams with long-running tool executions that benefit from incremental feedback","builders who want to provide real-time visibility into assistant reasoning"],"limitations":["Streaming adds complexity to error handling — errors may occur mid-stream after partial results are sent","Client must support streaming responses; not compatible with request-response-only APIs","Tool execution order becomes less predictable with streaming; dependencies between tools may be harder to manage"],"requires":["LLM with streaming API support","HTTP/WebSocket infrastructure supporting streaming responses","Client-side streaming response handling"],"input_types":["user messages","streaming LLM responses","tool execution results"],"output_types":["streamed text responses","incremental tool execution updates","real-time reasoning traces"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 16+","MCP-compliant tool servers or providers","@open-mercato/ai-assistant npm package","Network connectivity to MCP servers","API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","Tool schemas in MCP-compatible format","Network connectivity to LLM provider APIs","LLM API with function-calling support","MCP tool servers for tool execution","Memory to store conversation history (in-process or external)"],"failure_modes":["MCP server availability directly impacts assistant capability — no fallback if tool provider is offline","Tool discovery latency depends on MCP server response times; no built-in caching of tool schemas","Requires all tool providers to implement MCP spec correctly; incompatible or malformed servers will cause registration failures","Provider-specific function calling syntax differences require adapter code; not all providers support identical schema features","Parameter validation happens at call time, not at schema registration — invalid schemas may not be caught until execution","No built-in retry logic for transient tool failures; applications must implement their own retry strategies","Context window is limited by the underlying LLM; long conversations may exceed token limits and require summarization or pruning","No built-in persistence — conversation history exists only in memory; requires external storage for durability","Tool execution results are included verbatim in context; large result sets can quickly consume token budget","Result interpretation is format-dependent; no universal handling for all possible tool output types","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.3,"match_graph":0.25,"freshness":0.6,"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":"2026-05-03T14:23:53.657Z","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-open-mercatoai-assistant","compare_url":"https://unfragile.ai/compare?artifact=npm-open-mercatoai-assistant"}},"signature":"qKoWE9hgIbGwdYuZ/5TzV7xjt0a/XJVmyWzMEF0h+c+TsKR/dLeiU4N11fyNCHuDOaLAe1nK+Gd64HvKQcVYDw==","signedAt":"2026-06-20T08:28:11.920Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-open-mercatoai-assistant","artifact":"https://unfragile.ai/npm-open-mercatoai-assistant","verify":"https://unfragile.ai/api/v1/verify?slug=npm-open-mercatoai-assistant","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"}}