{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-av-harbor","slug":"mcp-av-harbor","name":"harbor","type":"cli","url":"https://github.com/av/harbor","page_url":"https://unfragile.ai/mcp-av-harbor","categories":["automation"],"tags":["ai","automation","bash","cli","container","docker","docker-compose","homelab","llm","local","mcp","npm","package","pypi","safetensors","self-hosted","server","tool","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-av-harbor__cap_0","uri":"capability://automation.workflow.docker.compose.based.service.orchestration.with.dynamic.configuration.resolution","name":"docker compose-based service orchestration with dynamic configuration resolution","description":"Harbor abstracts Docker Compose through a CLI system that dynamically resolves and merges compose files based on requested services, hardware capabilities (GPU detection via has_nvidia()), and user profiles. The orchestration engine uses a 'Lego-like' modular approach where each service is a pluggable module, with the core harbor.sh script handling service lifecycle management through functions like run_up() for starting services with flags like --tail or --open. Configuration is merged via compose_with_options() which combines base compose files with service-specific overrides.","intents":["I want to spin up a complete local LLM stack with one command without manually configuring Docker Compose files","I need to automatically detect my hardware (GPU/CPU) and apply the right compose configuration without manual intervention","I want to manage 50+ interconnected AI services as a single orchestrated unit rather than individual containers"],"best_for":["solo developers building local LLM infrastructure","teams deploying self-hosted AI stacks without cloud dependencies","researchers prototyping multi-service AI systems on heterogeneous hardware"],"limitations":["Abstraction adds complexity — debugging requires understanding both Harbor's resolution logic and underlying Docker Compose","Hardware detection is limited to NVIDIA GPUs; AMD/Intel GPU support requires manual compose file configuration","Service interdependencies are implicit in compose files — circular dependencies or missing services can cause silent failures"],"requires":["Docker 20.10+","Docker Compose 2.0+","Bash 4.0+","Linux/macOS (Windows requires WSL2)"],"input_types":["service names (strings)","environment variable overrides","hardware detection flags"],"output_types":["running Docker containers","merged Docker Compose YAML","service health status"],"categories":["automation-workflow","container-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_1","uri":"capability://automation.workflow.environment.variable.management.with.profile.based.configuration","name":"environment variable management with profile-based configuration","description":"Harbor provides a dedicated env_manager() function in harbor.sh (lines 1257-1350) that handles get, set, and list operations for the .env file, enabling users to configure services through environment variables without editing files directly. The system supports profile-based configuration through profiles/default.env, allowing users to switch between different hardware profiles, model selections, and service configurations. Configuration changes are persisted to the .env file and automatically loaded on subsequent service starts.","intents":["I want to configure Harbor services (model paths, API keys, resource limits) through CLI commands without editing .env files","I need to switch between different hardware profiles (CPU-only vs GPU-accelerated) without rewriting configuration","I want to version-control my Harbor configuration and share profiles across team members"],"best_for":["developers managing multiple Harbor deployments with different configurations","teams sharing Harbor setups with environment-specific overrides","users without direct file system access who need to configure services"],"limitations":["Profile system is file-based (profiles/default.env) — no built-in validation of configuration values before service startup","Environment variable changes require service restart to take effect — no hot-reload capability","No conflict detection when multiple profiles define overlapping variables"],"requires":["Bash 4.0+","Write access to .env file and profiles/ directory","Docker running to apply configuration changes"],"input_types":["environment variable names (strings)","configuration values (strings, numbers, paths)","profile names"],"output_types":["updated .env file","configuration values (get operation)","list of all configured variables"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_10","uri":"capability://automation.workflow.service.dependency.resolution.and.automatic.wiring.with.compose.file.merging","name":"service dependency resolution and automatic wiring with compose file merging","description":"Harbor implements automatic service dependency resolution through its compose file merging system (compose_with_options function in harbor.sh lines 402-520). When a user requests a service, Harbor analyzes service metadata to identify required dependencies, then merges the appropriate compose files in dependency order. This ensures that if a user enables a RAG service, the required vector database and embedding model services are automatically started. The system prevents circular dependencies and validates that all required services are available before starting the stack.","intents":["I want to enable a service and have its dependencies automatically started without manual configuration","I need to understand service dependencies before enabling a service to avoid broken configurations","I want Harbor to prevent me from starting incompatible service combinations"],"best_for":["developers building complex multi-service AI stacks","teams managing Harbor deployments with many interdependent services","users who want 'one-click' service enablement without understanding dependencies"],"limitations":["Dependency resolution is based on static metadata — no runtime dependency checking","Circular dependencies are detected but not automatically resolved — user must manually break cycles","Dependency chains can be deep — starting one service may start 5+ dependent services, consuming significant resources"],"requires":["Service metadata with dependency declarations","Compose files for all services and dependencies","Docker and Docker Compose"],"input_types":["service names (strings)","dependency metadata (JSON)"],"output_types":["merged Docker Compose file (YAML)","dependency graph (implicit in compose file order)","validation errors (text)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_11","uri":"capability://automation.workflow.version.synchronization.and.model.management.across.services","name":"version synchronization and model management across services","description":"Harbor includes version synchronization logic (routines/models/hf.ts, routines/models/llamacpp.ts) that manages model versions across different inference backends. The system tracks which models are available in each backend (Ollama, llama.cpp, HuggingFace), handles model downloads and caching, and ensures version consistency when switching backends. Users can specify model versions through environment variables, and Harbor automatically downloads the correct version for the selected backend. The system supports model quantization variants (e.g., 4-bit, 8-bit) and automatically selects the appropriate variant based on available hardware.","intents":["I want to download and manage LLM models without manually handling model files","I need to switch between different model quantizations (4-bit, 8-bit, full precision) based on available hardware","I want to ensure model versions are consistent across different inference backends"],"best_for":["developers managing multiple LLM models and quantization variants","teams deploying Harbor across hardware with different resource constraints","researchers comparing model performance across quantization levels"],"limitations":["Model downloads are not resumable — interrupted downloads require re-downloading the entire model","Quantization variant selection is manual — no automatic optimization based on hardware","Model caching is local to each service — no shared model cache across backends"],"requires":["Internet connectivity for model downloads","Sufficient disk space for model files (typically 4GB-70GB per model)","HuggingFace API access (for HuggingFace model downloads)"],"input_types":["model names (strings)","model versions (strings)","quantization levels (4-bit, 8-bit, etc.)"],"output_types":["downloaded model files","model metadata (size, quantization, version)","download progress"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_12","uri":"capability://safety.moderation.observability.and.evaluation.services.for.llm.monitoring.and.testing","name":"observability and evaluation services for llm monitoring and testing","description":"Harbor includes observability and evaluation services that enable monitoring of LLM inference (latency, throughput, token usage) and evaluation of model outputs (quality metrics, safety checks). These services integrate with Harbor Boost to collect metrics from every LLM request, and provide dashboards and APIs for analyzing performance. The system supports custom evaluation modules that can be plugged into the request/response pipeline to assess output quality, detect hallucinations, or check for safety violations.","intents":["I want to monitor LLM inference performance (latency, throughput, cost) in production","I need to evaluate model output quality and detect issues like hallucinations or safety violations","I want to track metrics across different models and backends to compare performance"],"best_for":["teams running production LLM services that need monitoring","researchers evaluating model quality and safety","developers optimizing LLM inference performance"],"limitations":["Evaluation metrics are custom and require implementation — no built-in quality metrics","Monitoring adds overhead to every request — exact overhead depends on metric collection complexity","Historical data is not persisted by default — requires external storage for long-term analysis"],"requires":["Harbor Boost running to intercept requests","Observability service enabled","Optional: external storage for metrics (database, time-series DB)"],"input_types":["LLM requests and responses (JSON)","custom evaluation logic (Python modules)"],"output_types":["performance metrics (latency, throughput, cost)","evaluation results (quality scores, safety flags)","dashboards and reports"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_13","uri":"capability://tool.use.integration.custom.service.creation.and.harbor.boost.module.development.framework","name":"custom service creation and harbor boost module development framework","description":"Harbor provides a framework for creating custom services and Harbor Boost modules that extend the platform's capabilities. Custom services are defined as Docker Compose services with metadata declarations, while Boost modules are Python classes that hook into the LLM request/response pipeline. The framework includes templates, documentation, and integration testing utilities to help developers build and test custom extensions. Custom services are automatically discovered and integrated into the service catalog, and Boost modules can be enabled through configuration without modifying Harbor core.","intents":["I want to add a custom service to Harbor that integrates with my existing infrastructure","I need to create a Harbor Boost module that applies custom transformations to LLM requests and responses","I want to extend Harbor's capabilities without forking the project or modifying core code"],"best_for":["developers building custom AI services that integrate with Harbor","teams extending Harbor with proprietary optimization logic","researchers prototyping new LLM middleware and evaluation techniques"],"limitations":["Custom service development requires Docker and Docker Compose knowledge","Boost module development requires Python knowledge and understanding of Harbor's module API","No built-in testing framework — developers must write their own integration tests","Custom services are not automatically updated when Harbor updates"],"requires":["Docker and Docker Compose for custom services","Python 3.9+ for Boost modules","Understanding of Harbor's service metadata format and Boost module API","Git (for version control and contribution)"],"input_types":["Docker Compose service definitions (YAML)","service metadata (JSON/TypeScript)","Boost module code (Python)"],"output_types":["custom service containers","Boost modules integrated into request pipeline","extended service catalog"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_2","uri":"capability://memory.knowledge.service.catalog.with.metadata.driven.discovery.and.tagging","name":"service catalog with metadata-driven discovery and tagging","description":"Harbor maintains a curated service catalog (app/src/serviceMetadata.ts lines 8-103) with over 50 AI-related services organized by Harbor Service Tags (HST). Each service has associated metadata including category (LLM backends, frontends, satellite services, RAG tools), dependencies, port mappings, and integration patterns. The catalog enables users to discover available services, understand their purpose, and understand how they integrate with other services in the stack. Service metadata drives the dynamic composition of Docker Compose files and the Harbor Desktop App's UI.","intents":["I want to discover what AI services are available in Harbor and understand what each one does","I need to understand service dependencies before enabling a service to avoid broken configurations","I want to filter services by category (e.g., 'show me all RAG services' or 'show me all inference backends')"],"best_for":["new Harbor users exploring available services and their capabilities","developers building custom services who need to understand integration patterns","teams documenting their Harbor deployment and service dependencies"],"limitations":["Metadata is static and requires code changes to update — no runtime service discovery","Service categories are predefined (HST tags) — custom categories require code modification","Metadata doesn't include real-time service health or version information"],"requires":["Access to serviceMetadata.ts or Harbor CLI","Harbor Desktop App or CLI to query metadata"],"input_types":["service names (strings)","category filters (strings)","tag filters (strings)"],"output_types":["service metadata (JSON)","service descriptions (text)","dependency graphs","port mappings"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_3","uri":"capability://tool.use.integration.harbor.boost.llm.proxy.with.python.based.module.system.for.optimization","name":"harbor boost — llm proxy with python-based module system for optimization","description":"Harbor Boost is an optimizing LLM proxy layer (services/boost/pyproject.toml) built with a Python-based module system that intercepts LLM requests and applies transformations such as prompt optimization, response caching, cost tracking, and multi-provider routing. The module system allows users to create custom Boost modules that hook into the request/response pipeline. Boost acts as a middleware between client applications and inference backends (Ollama, llama.cpp, OpenAI), enabling advanced features like artifact generation and visualization without modifying the underlying models.","intents":["I want to optimize LLM responses (caching, prompt engineering, cost tracking) without modifying my client code","I need to route requests to different LLM providers based on cost, latency, or capability without changing my application","I want to extend LLM behavior with custom modules (e.g., artifact generation, response formatting) without forking Harbor"],"best_for":["developers building production LLM applications that need optimization and monitoring","teams managing multiple LLM providers and needing unified routing logic","researchers experimenting with prompt engineering and response transformation techniques"],"limitations":["Module system requires Python knowledge — no low-code module creation interface","Boost adds latency to every request (exact overhead depends on module complexity) — not suitable for ultra-low-latency applications","Module state is not persisted across restarts — requires external storage for caching or analytics"],"requires":["Python 3.9+","Harbor running with Boost service enabled","LLM backend (Ollama, llama.cpp, or cloud provider API key)"],"input_types":["LLM requests (JSON with prompt, model, parameters)","custom Boost module code (Python)"],"output_types":["optimized LLM responses (JSON)","artifacts (code, visualizations)","metrics (latency, cost, cache hits)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_4","uri":"capability://automation.workflow.hardware.aware.service.composition.with.gpu.detection.and.fallback.profiles","name":"hardware-aware service composition with gpu detection and fallback profiles","description":"Harbor includes hardware detection logic (has_nvidia() function in harbor.sh lines 262-264) that automatically detects NVIDIA GPUs and applies GPU-accelerated compose files when available. The system maintains separate compose file variants for CPU-only and GPU-accelerated deployments, allowing the same service configuration to run optimally on different hardware without user intervention. If GPU detection fails or GPUs are unavailable, Harbor automatically falls back to CPU-only compose files, ensuring services remain functional across heterogeneous hardware.","intents":["I want Harbor to automatically use GPU acceleration if available, without manual configuration","I need to deploy the same Harbor configuration on both GPU and CPU-only machines without rewriting compose files","I want to test my LLM stack on CPU-only hardware and then scale to GPU without configuration changes"],"best_for":["developers deploying Harbor across multiple machines with different hardware","teams managing both development (CPU) and production (GPU) environments","researchers comparing performance across hardware configurations"],"limitations":["GPU detection is NVIDIA-only — AMD and Intel GPUs require manual compose file selection","Fallback to CPU is automatic but may result in unacceptable performance — no warnings about performance degradation","GPU memory allocation is not automatically optimized — users must manually configure CUDA_VISIBLE_DEVICES and memory limits"],"requires":["Docker 20.10+ with NVIDIA Container Runtime (for GPU support)","nvidia-docker or Docker with --gpus flag support","NVIDIA GPU drivers installed on host (for GPU detection)"],"input_types":["hardware detection flags (implicit, no user input required)"],"output_types":["GPU-accelerated or CPU-only compose configuration","hardware capability report"],"categories":["automation-workflow","infrastructure-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_5","uri":"capability://tool.use.integration.mcp.model.context.protocol.service.integration.and.tool.use.orchestration","name":"mcp (model context protocol) service integration and tool-use orchestration","description":"Harbor integrates with the Model Context Protocol (MCP) to enable LLM agents to call external tools and services as function calls. Services in Harbor expose MCP-compatible tool schemas that agents can discover and invoke. The integration allows agents to use tools like web search (SearXNG), code execution, file operations, and custom service APIs without hardcoding tool logic into the agent. Tool schemas are defined in service metadata and automatically registered with the MCP server when services start.","intents":["I want my LLM agent to call external tools (search, code execution, APIs) using standard MCP function-calling protocol","I need to expose Harbor services as callable tools to LLM agents without writing custom integration code","I want to build multi-step agent workflows that chain together Harbor services (e.g., search → summarize → generate code)"],"best_for":["developers building LLM agents that need access to external tools and services","teams building agentic AI systems that orchestrate multiple Harbor services","researchers experimenting with tool-use and function-calling in LLMs"],"limitations":["MCP schema definition is manual — no automatic schema generation from service APIs","Tool-use latency depends on service response time — no built-in timeout or retry logic","Agent error handling is application-specific — Harbor doesn't provide guardrails for tool misuse"],"requires":["MCP-compatible LLM client or agent framework","Harbor services with MCP tool schemas defined","Network connectivity between agent and Harbor services"],"input_types":["MCP tool schemas (JSON)","function call requests (JSON with tool name and parameters)"],"output_types":["tool execution results (JSON)","error messages (text)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_6","uri":"capability://automation.workflow.harbor.desktop.app.tauri.based.gui.for.service.management.and.monitoring","name":"harbor desktop app — tauri-based gui for service management and monitoring","description":"Harbor includes a Tauri-based desktop application (app/src-tauri/tauri.conf.json) that provides a graphical interface for managing services, viewing service status, configuring settings, and monitoring logs. The app is built with Tauri (Rust backend, TypeScript/React frontend) for cross-platform compatibility and minimal resource overhead. The app communicates with the Harbor CLI and Docker daemon to provide real-time service status, enable one-click service start/stop, and visualize service dependencies and health.","intents":["I want a GUI to manage Harbor services instead of using the CLI","I need to see real-time status of all running services and their logs in one place","I want to configure Harbor settings and enable/disable services through a visual interface"],"best_for":["non-technical users who prefer GUI over CLI","developers who want visual service monitoring alongside CLI management","teams managing Harbor deployments that need a shared dashboard"],"limitations":["Desktop app is optional — all functionality is available through CLI, so app development may lag behind CLI features","Real-time monitoring requires polling Docker daemon — high-frequency polling can impact performance","Cross-platform support (Windows, macOS, Linux) may have platform-specific bugs or limitations"],"requires":["Tauri runtime (included in app bundle)","Docker daemon running and accessible","Harbor CLI installed and in PATH"],"input_types":["user interactions (clicks, text input)","service configuration changes"],"output_types":["service status (running/stopped/error)","service logs (text)","configuration UI"],"categories":["automation-workflow","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_7","uri":"capability://tool.use.integration.multi.backend.llm.inference.with.ollama.llama.cpp.and.cloud.provider.support","name":"multi-backend llm inference with ollama, llama.cpp, and cloud provider support","description":"Harbor supports multiple LLM inference backends including Ollama (local model serving), llama.cpp (CPU-optimized inference), and cloud providers (OpenAI, Anthropic) through a unified interface. Each backend is a pluggable service with its own compose file and configuration. Harbor's LiteLLM Gateway (satellite service) provides a unified API endpoint that routes requests to the selected backend, allowing applications to switch backends without code changes. Model selection and backend routing are configured through environment variables and Harbor Boost modules.","intents":["I want to run local LLM inference using Ollama or llama.cpp without managing separate services","I need to switch between local and cloud LLM providers (OpenAI, Anthropic) without changing my application code","I want to compare inference performance across different backends (Ollama vs llama.cpp vs cloud) on the same hardware"],"best_for":["developers building LLM applications that need backend flexibility","teams comparing inference backends for cost and performance","researchers experimenting with different model architectures and quantization levels"],"limitations":["Model compatibility varies by backend — not all models run on all backends (e.g., some quantizations only work with llama.cpp)","Performance characteristics differ significantly — latency and throughput vary by backend and hardware","Cloud provider backends require API keys and incur per-token costs — no cost estimation built into Harbor"],"requires":["Docker and Docker Compose","For Ollama: 8GB+ RAM, GPU optional","For llama.cpp: 4GB+ RAM, CPU-only","For cloud providers: API key (OpenAI, Anthropic, etc.)"],"input_types":["model names (strings)","inference parameters (temperature, top_p, max_tokens, etc.)","backend selection (environment variable)"],"output_types":["LLM completions (text)","token usage statistics","inference latency metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_8","uri":"capability://memory.knowledge.rag.retrieval.augmented.generation.service.integration.with.knowledge.base.management","name":"rag (retrieval-augmented generation) service integration with knowledge base management","description":"Harbor includes satellite services for RAG workflows, including vector databases, document indexers, and retrieval engines. Services like SearXNG provide web search integration for RAG, while knowledge base services enable semantic search over local documents. Harbor's service composition automatically wires RAG services together (e.g., connecting a document indexer to a vector database), and Harbor Boost modules can intercept LLM requests to augment prompts with retrieved context. The system supports multiple RAG patterns including semantic search, hybrid search, and multi-hop retrieval.","intents":["I want to build a RAG system that retrieves relevant documents and augments LLM prompts without writing custom integration code","I need to index local documents and enable semantic search over them for LLM context","I want to combine web search (SearXNG) with local knowledge bases for comprehensive RAG"],"best_for":["developers building knowledge-grounded LLM applications","teams managing large document collections that need semantic search","researchers experimenting with RAG architectures and retrieval strategies"],"limitations":["Vector database configuration is manual — no automatic schema inference from documents","Retrieval quality depends on embedding model and chunking strategy — no built-in optimization","Multi-hop retrieval requires custom Harbor Boost modules — not provided out-of-the-box"],"requires":["Vector database service (e.g., Weaviate, Milvus) running in Harbor","Document indexer service","Embedding model (local or cloud-based)","LLM backend for generation"],"input_types":["documents (text, PDF, markdown)","search queries (text)","embedding parameters (model, dimension)"],"output_types":["retrieved documents (text with metadata)","augmented prompts (text)","LLM completions (text)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-av-harbor__cap_9","uri":"capability://automation.workflow.web.ui.frontends.open.webui.comfyui.with.unified.service.routing","name":"web ui frontends (open webui, comfyui) with unified service routing","description":"Harbor includes pre-configured web UI frontends including Open WebUI (chat interface for LLMs) and ComfyUI (node-based workflow builder for image generation). These frontends are automatically wired to the appropriate inference backends through Harbor's service composition. Open WebUI connects to LLM backends (Ollama, llama.cpp, cloud providers) through the LiteLLM Gateway, while ComfyUI connects to image generation models. The frontends are accessible through a unified landing page service that provides navigation and service discovery.","intents":["I want a web chat interface for my local LLM without setting up a separate UI service","I need a visual workflow builder for image generation without manual integration","I want to access multiple Harbor services (chat, image generation, etc.) through a unified web interface"],"best_for":["non-technical users who want to interact with LLMs through a web UI","teams building internal tools that need a user-friendly interface","developers prototyping LLM applications before building custom UIs"],"limitations":["UI customization requires forking or extending the frontend code — no built-in theming or branding","Open WebUI and ComfyUI are third-party projects — Harbor provides integration but not direct support","Web UI performance depends on network latency — not suitable for high-latency connections"],"requires":["Harbor running with frontend services enabled","LLM or image generation backend running","Web browser with JavaScript enabled","Network access to Harbor services (localhost or network)"],"input_types":["user prompts (text for chat, node configurations for ComfyUI)","file uploads (images, documents)"],"output_types":["LLM completions (text, markdown)","generated images (PNG, JPEG)","workflow results (JSON)"],"categories":["automation-workflow","user-interface"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Docker 20.10+","Docker Compose 2.0+","Bash 4.0+","Linux/macOS (Windows requires WSL2)","Write access to .env file and profiles/ directory","Docker running to apply configuration changes","Service metadata with dependency declarations","Compose files for all services and dependencies","Docker and Docker Compose","Internet connectivity for model downloads"],"failure_modes":["Abstraction adds complexity — debugging requires understanding both Harbor's resolution logic and underlying Docker Compose","Hardware detection is limited to NVIDIA GPUs; AMD/Intel GPU support requires manual compose file configuration","Service interdependencies are implicit in compose files — circular dependencies or missing services can cause silent failures","Profile system is file-based (profiles/default.env) — no built-in validation of configuration values before service startup","Environment variable changes require service restart to take effect — no hot-reload capability","No conflict detection when multiple profiles define overlapping variables","Dependency resolution is based on static metadata — no runtime dependency checking","Circular dependencies are detected but not automatically resolved — user must manually break cycles","Dependency chains can be deep — starting one service may start 5+ dependent services, consuming significant resources","Model downloads are not resumable — interrupted downloads require re-downloading the entire model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5206110004612294,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2026-04-26T21:24:47Z"},"community":{"stars":2901,"forks":198,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-av-harbor","compare_url":"https://unfragile.ai/compare?artifact=mcp-av-harbor"}},"signature":"wDkgDel5tzehp9ZB77Exfe28wgtms/p1o/4JbLEmXcpE0wUuBJLY+ilnzFC3iLnIdA2R39JDL+f6UKQYU87XDA==","signedAt":"2026-06-22T17:26:18.391Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-av-harbor","artifact":"https://unfragile.ai/mcp-av-harbor","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-av-harbor","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"}}