{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_asigdel29-honcho","slug":"asigdel29-honcho","name":"Honcho Server","type":"mcp","url":"https://honcho.dev","page_url":"https://unfragile.ai/asigdel29-honcho","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:asigdel29/honcho"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_asigdel29-honcho__cap_0","uri":"capability://memory.knowledge.user.psychology.model.persistence","name":"user-psychology-model-persistence","description":"Maintains persistent, evolving models of individual user psychology and behavior patterns across conversation sessions using a server-side state store. Honcho tracks user preferences, communication styles, emotional patterns, and interaction history to build longitudinal profiles that inform subsequent interactions. This enables the AI to adapt responses based on accumulated knowledge of each user rather than treating each session as stateless.","intents":["Build an AI assistant that remembers my preferences and communication style across months of interactions","Create personalized LLM responses that account for a user's emotional state and history","Track how a user's needs and preferences evolve over time to improve recommendations"],"best_for":["teams building long-term user-facing AI applications requiring personalization","developers creating mental health or coaching AI agents","product teams needing user behavior analytics integrated with LLM interactions"],"limitations":["Requires external database or persistent storage backend — no in-memory-only option","Privacy implications of storing detailed user psychology models must be addressed by implementer","No built-in data retention policies or GDPR compliance utilities","Scaling to millions of concurrent user models requires careful database indexing strategy"],"requires":["MCP-compatible server runtime","Persistent storage backend (PostgreSQL, MongoDB, or compatible)","LLM API key (OpenAI, Anthropic, or self-hosted compatible)"],"input_types":["conversation messages","user metadata","interaction events"],"output_types":["user psychology profile (structured JSON)","personalization vectors","behavior pattern summaries"],"categories":["memory-knowledge","user-modeling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_1","uri":"capability://planning.reasoning.multi.participant.session.orchestration","name":"multi-participant-session-orchestration","description":"Manages concurrent multi-user conversation sessions with isolated state contexts and participant role tracking through MCP resource endpoints. Honcho coordinates interactions between multiple participants (users, agents, moderators) within bounded session contexts, maintaining separate psychology models and interaction histories per participant while enabling cross-participant reasoning and coordination.","intents":["Build a group chat application where an AI moderator understands each participant's perspective and communication style","Create a multi-agent debate or negotiation system where agents model each other's positions","Implement a classroom AI tutor that tracks individual student progress while managing whole-class sessions"],"best_for":["teams building collaborative AI applications with 2-10 concurrent participants","developers creating multi-agent systems with heterogeneous agent types","education and team productivity platforms integrating AI facilitation"],"limitations":["Session isolation adds latency for cross-participant queries — no built-in optimization for high-frequency inter-participant reasoning","Participant role definitions are application-defined; no pre-built role taxonomy","Scaling beyond 100+ concurrent sessions per server requires horizontal partitioning strategy","No built-in conflict resolution or consensus mechanisms for divergent participant models"],"requires":["MCP server implementation","Session storage backend","LLM with function-calling support for participant coordination"],"input_types":["participant messages","session metadata","role definitions"],"output_types":["session state snapshots","per-participant context windows","cross-participant reasoning summaries"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_10","uri":"capability://memory.knowledge.temporal.reasoning.over.user.evolution","name":"temporal-reasoning-over-user-evolution","description":"Enables agents to reason about how user psychology, preferences, and goals have evolved over time, identifying trends, inflection points, and long-term patterns in user behavior. Honcho maintains timestamped psychology models and enables agents to query historical snapshots, compare user state across time periods, and identify significant changes or patterns.","intents":["Detect that a user's risk tolerance has increased over the past 6 months and adjust investment recommendations","Identify that a user's communication style has become more formal and adapt tone accordingly","Recognize that a user's stated goals have shifted and proactively suggest new directions"],"best_for":["long-term user-facing applications (coaching, financial advisory, healthcare)","systems requiring trend analysis and pattern detection in user behavior","applications where understanding user evolution is key to personalization"],"limitations":["Requires months of interaction history to identify meaningful trends — poor for new users","Temporal patterns may reflect external factors (seasonality, life events) not captured in interaction data","No built-in causal inference — cannot determine what caused observed changes","Storing timestamped psychology snapshots increases storage requirements significantly"],"requires":["Timestamped user psychology models","Historical interaction data","LLM for trend analysis"],"input_types":["user psychology snapshots","time ranges","analysis queries"],"output_types":["temporal trend summaries","inflection point detection","evolution narratives"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_2","uri":"capability://planning.reasoning.asynchronous.reasoning.with.deferred.execution","name":"asynchronous-reasoning-with-deferred-execution","description":"Enables long-running AI reasoning tasks to execute asynchronously outside the request-response cycle, with results stored and retrievable via MCP resource endpoints. Honcho decouples expensive reasoning operations (multi-step planning, user psychology inference, cross-participant analysis) from immediate response requirements, allowing agents to perform deep analysis in background tasks and reference results in subsequent interactions.","intents":["Perform expensive user psychology inference without blocking the user-facing chat response","Run nightly batch analysis of user behavior patterns to update personalization models","Execute multi-step reasoning chains that take 30+ seconds without timing out the user request"],"best_for":["applications with latency-sensitive user interactions but complex reasoning requirements","teams needing to decouple real-time responses from background analysis","systems requiring periodic model updates without service interruption"],"limitations":["No built-in job scheduling or cron integration — requires external task queue (Celery, Bull, etc.)","Results consistency depends on application-level coordination between async tasks and live sessions","No automatic retry logic or failure recovery — must be implemented per use case","Debugging async reasoning chains requires correlation IDs and structured logging"],"requires":["MCP server with async/await support","Task queue or background job system","Persistent result storage"],"input_types":["reasoning task definitions","user context","analysis parameters"],"output_types":["task execution status","reasoning results (JSON)","analysis summaries"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_3","uri":"capability://planning.reasoning.theory.of.mind.agent.reasoning","name":"theory-of-mind-agent-reasoning","description":"Enables AI agents to reason about other agents' and users' mental states, beliefs, goals, and likely actions using structured theory-of-mind models exposed via MCP. Agents can query what they believe about other participants' knowledge, preferences, and intentions, then condition their responses on these models. This implements a form of recursive reasoning where agents model not just user behavior but user understanding of the agent.","intents":["Build an AI agent that adapts its explanation complexity based on its model of the user's technical knowledge","Create a negotiation agent that reasons about the other party's constraints and priorities","Implement an AI tutor that diagnoses misconceptions by modeling student understanding"],"best_for":["developers building empathetic or socially-aware AI agents","teams creating negotiation, mediation, or coaching AI systems","education and healthcare AI applications requiring deep user understanding"],"limitations":["Theory-of-mind models are inferences, not ground truth — can be systematically wrong about user intent","Requires sufficient interaction history to build accurate models — poor performance with new users","No built-in uncertainty quantification — agents may over-commit to incorrect mental models","Computational cost scales with number of participants and depth of recursive reasoning"],"requires":["LLM with strong reasoning capabilities (GPT-4, Claude 3+, or equivalent)","User psychology models from user-psychology-model-persistence capability","Multi-turn conversation history for model inference"],"input_types":["user psychology profiles","conversation history","agent state"],"output_types":["theory-of-mind models (JSON)","belief distributions","predicted user actions"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_4","uri":"capability://tool.use.integration.mcp.protocol.server.implementation","name":"mcp-protocol-server-implementation","description":"Implements the Model Context Protocol (MCP) server specification to expose Honcho capabilities as standardized resources, tools, and prompts that any MCP-compatible client can invoke. Honcho acts as an MCP server, defining resource schemas for user psychology models, session state, and reasoning results, and implementing the MCP transport layer (stdio, SSE, or custom) for client communication.","intents":["Integrate Honcho user psychology models into any MCP-compatible LLM application or IDE","Use Honcho as a backend for Claude Desktop, VS Code, or other MCP clients","Build custom agents that query Honcho psychology models via standard MCP resource endpoints"],"best_for":["teams already using MCP-compatible tools (Claude Desktop, VS Code extensions, etc.)","developers building polyglot AI systems that need standardized capability interfaces","organizations standardizing on MCP for AI tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms per request compared to direct library calls","Limited to MCP's resource/tool/prompt model — complex stateful interactions may require custom extensions","Client-side MCP implementations vary in feature completeness — not all MCP clients support all Honcho capabilities","Debugging MCP communication requires protocol-level logging and tracing"],"requires":["MCP client implementation (Claude Desktop, custom client, etc.)","MCP server runtime (Node.js, Python, or language-specific MCP SDK)","Network connectivity between client and server (stdio, SSE, or WebSocket)"],"input_types":["MCP resource requests","MCP tool calls","MCP prompt invocations"],"output_types":["MCP resource responses (JSON)","MCP tool results","MCP prompt completions"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_5","uri":"capability://data.processing.analysis.user.preference.extraction.and.inference","name":"user-preference-extraction-and-inference","description":"Automatically extracts and infers user preferences, values, and communication styles from conversation history using LLM-based analysis and stores them as queryable preference profiles. Honcho parses user messages to identify stated preferences, infers implicit preferences from behavior patterns, and maintains a structured preference model that agents can query to personalize responses without explicit user configuration.","intents":["Automatically learn that a user prefers concise bullet-point responses over detailed paragraphs","Infer a user's risk tolerance and decision-making style from past interactions","Detect a user's domain expertise level and adjust technical depth accordingly"],"best_for":["consumer-facing AI applications requiring implicit personalization","teams building AI assistants that adapt without explicit user settings","applications where users expect the AI to 'just know' their preferences"],"limitations":["Preference inference is probabilistic and can be wrong — no ground truth validation","Requires minimum conversation history (typically 5-10 exchanges) to build reliable preference models","Conflicting preferences from different contexts may not be resolved automatically","Cultural and demographic biases in preference inference are difficult to detect and mitigate"],"requires":["LLM with instruction-following and structured output capabilities","Conversation history storage","User psychology model persistence"],"input_types":["conversation messages","user metadata","interaction events"],"output_types":["preference profiles (JSON)","preference confidence scores","inferred user attributes"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_6","uri":"capability://memory.knowledge.conversation.context.windowing.with.psychology.awareness","name":"conversation-context-windowing-with-psychology-awareness","description":"Manages LLM context windows by selecting the most relevant conversation history and user psychology information to include in prompts, using importance scoring and psychology-aware ranking. Rather than simple recency-based truncation, Honcho ranks conversation turns by relevance to current user psychology state and agent goals, ensuring that psychologically-significant interactions are retained even if older.","intents":["Keep important context about a user's emotional state or past trauma in the LLM context even if it occurred many turns ago","Prioritize recent preference changes over older preferences when they conflict","Maintain context about a user's stated goals while truncating less relevant conversation history"],"best_for":["long-running conversational AI applications with extended interaction histories","mental health and coaching AI systems where historical context is psychologically significant","applications with limited token budgets that need intelligent context selection"],"limitations":["Importance scoring adds 50-200ms latency per context window construction","Ranking heuristics are application-specific — no one-size-fits-all importance metric","Removing context can cause agents to lose track of multi-turn reasoning or complex topics","No automatic detection of context dependencies — agents may reference truncated information"],"requires":["User psychology models","Conversation history storage","LLM with configurable context window"],"input_types":["conversation history","user psychology profile","agent goals/instructions"],"output_types":["truncated conversation context","importance-ranked turns","context selection rationale"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_7","uri":"capability://planning.reasoning.agent.behavior.modeling.and.prediction","name":"agent-behavior-modeling-and-prediction","description":"Builds and maintains models of agent behavior patterns, decision-making styles, and reasoning approaches based on historical agent actions and outputs. Honcho tracks how agents respond to similar situations, what reasoning patterns they use, and how their behavior evolves, enabling prediction of agent actions in novel scenarios and detection of anomalous behavior.","intents":["Predict how an agent will respond to a new user request based on its historical behavior patterns","Detect when an agent is behaving anomalously or deviating from its typical reasoning patterns","Model the interaction style and decision-making approach of a specific agent for multi-agent coordination"],"best_for":["teams running multiple AI agents that need to coordinate or model each other","systems requiring anomaly detection or behavior monitoring for AI agents","applications where agent consistency and predictability are important"],"limitations":["Agent behavior models are specific to training data distribution — poor generalization to out-of-distribution scenarios","Requires significant historical data (100+ interactions) to build reliable behavior models","LLM non-determinism means identical inputs may produce different outputs, confounding behavior modeling","No built-in causal inference — correlations in behavior patterns may not reflect true decision drivers"],"requires":["Agent action history storage","LLM for behavior pattern analysis","User psychology models for context"],"input_types":["agent action history","agent outputs/responses","interaction contexts"],"output_types":["agent behavior profiles","decision pattern summaries","behavior anomaly scores"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_8","uri":"capability://automation.workflow.interaction.event.streaming.and.analytics","name":"interaction-event-streaming-and-analytics","description":"Streams interaction events (messages, actions, state changes) to analytics backends in real-time, enabling live monitoring and post-hoc analysis of user-agent interactions. Honcho emits structured events for each interaction turn, psychology model update, and reasoning step, allowing downstream systems to build dashboards, run analytics, and audit agent behavior.","intents":["Monitor live user engagement and agent performance metrics in a dashboard","Analyze which user psychology factors most strongly predict user satisfaction","Audit agent reasoning and decisions for bias or harmful behavior"],"best_for":["teams needing real-time monitoring and observability of AI interactions","applications requiring audit trails and compliance logging","data-driven teams analyzing user behavior and agent performance"],"limitations":["Event streaming adds latency and overhead to interaction processing","Event schema must be defined upfront — schema changes require migration","High-volume event streams require robust backend infrastructure (Kafka, etc.)","Privacy implications of streaming detailed interaction data must be addressed"],"requires":["Event streaming backend (Kafka, Pub/Sub, etc.) or HTTP endpoint","Analytics platform (Mixpanel, Amplitude, custom data warehouse)","Event schema definition"],"input_types":["interaction events","state changes","reasoning steps"],"output_types":["structured event streams (JSON)","analytics summaries","audit logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_asigdel29-honcho__cap_9","uri":"capability://tool.use.integration.schema.based.function.calling.with.agent.coordination","name":"schema-based-function-calling-with-agent-coordination","description":"Defines and manages function schemas that agents can call to take actions, with built-in support for multi-agent function coordination and result aggregation. Honcho allows agents to declare available functions via JSON schemas, call functions with type-safe arguments, and coordinate function calls across multiple agents (e.g., one agent calls a function that another agent must handle).","intents":["Enable an AI agent to call external APIs or tools with type-safe argument validation","Coordinate function calls between multiple agents (e.g., one agent requests data from another)","Track which functions agents call and aggregate results for analysis"],"best_for":["teams building agentic systems that need to take actions beyond text generation","applications requiring multi-agent coordination through function calls","systems needing audit trails of agent actions"],"limitations":["Function schemas must be manually defined — no automatic schema generation from code","No built-in error handling or retry logic for failed function calls","Function execution is synchronous — long-running functions block agent reasoning","No built-in sandboxing or security isolation for function execution"],"requires":["Function schema definitions (JSON Schema format)","Function implementation or external API endpoints","LLM with function-calling support"],"input_types":["function schemas","function arguments","agent requests"],"output_types":["function results","execution logs","aggregated results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible server runtime","Persistent storage backend (PostgreSQL, MongoDB, or compatible)","LLM API key (OpenAI, Anthropic, or self-hosted compatible)","MCP server implementation","Session storage backend","LLM with function-calling support for participant coordination","Timestamped user psychology models","Historical interaction data","LLM for trend analysis","MCP server with async/await support"],"failure_modes":["Requires external database or persistent storage backend — no in-memory-only option","Privacy implications of storing detailed user psychology models must be addressed by implementer","No built-in data retention policies or GDPR compliance utilities","Scaling to millions of concurrent user models requires careful database indexing strategy","Session isolation adds latency for cross-participant queries — no built-in optimization for high-frequency inter-participant reasoning","Participant role definitions are application-defined; no pre-built role taxonomy","Scaling beyond 100+ concurrent sessions per server requires horizontal partitioning strategy","No built-in conflict resolution or consensus mechanisms for divergent participant models","Requires months of interaction history to identify meaningful trends — poor for new users","Temporal patterns may reflect external factors (seasonality, life events) not captured in interaction data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.57,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:22.209Z","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=asigdel29-honcho","compare_url":"https://unfragile.ai/compare?artifact=asigdel29-honcho"}},"signature":"IJ+tQeVEIFTTf+pGAe3U4+Emd+3QUUwlQuV0zearaubw9T14ZPA4Oxim8Rp4iay4UUdaCgn3rHDhvlStsqgeDg==","signedAt":"2026-06-23T04:19:40.519Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/asigdel29-honcho","artifact":"https://unfragile.ai/asigdel29-honcho","verify":"https://unfragile.ai/api/v1/verify?slug=asigdel29-honcho","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"}}