{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-magg","slug":"magg","name":"🧲 Magg 🧲","type":"mcp","url":"https://github.com/sitbon/magg","page_url":"https://unfragile.ai/magg","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-magg__cap_0","uri":"capability://tool.use.integration.multi.server.mcp.aggregation.with.unified.tool.namespace","name":"multi-server mcp aggregation with unified tool namespace","description":"Magg implements a hub-and-spoke proxy architecture that connects to multiple backend MCP servers and exposes their tools through a single aggregated interface. It uses configurable tool prefixes (e.g., calc_add, pw_screenshot) to namespace tools from different servers, maintains full MCP protocol semantics including notifications and progress updates, and routes incoming tool calls to the appropriate backend server based on prefix matching. The MaggServer class acts as both an MCP server (exposing aggregated tools) and MCP client (connecting to backends), creating a transparent proxy layer that unifies heterogeneous tool sources.","intents":["I want my LLM to access tools from 5+ different MCP servers without managing separate connections","I need to namespace tools from different servers to avoid naming conflicts","I want to present a single MCP interface to my AI assistant that aggregates multiple tool providers","I need to dynamically add or remove backend servers without restarting the aggregator"],"best_for":["AI teams building multi-tool agent systems with heterogeneous MCP server backends","LLM application developers needing a unified tool interface across multiple providers","Enterprise deployments requiring centralized tool management and access control"],"limitations":["Tool name collisions across servers require manual prefix configuration; no automatic conflict resolution","Latency increases with number of aggregated servers due to sequential message routing","No built-in load balancing or failover for backend servers — single point of failure per backend","Configuration changes require file-based updates; no runtime API for adding/removing servers without restart in some modes"],"requires":["Python 3.9+","FastMCP framework integration","Backend MCP servers accessible via stdio, HTTP, or SSE transport","Configuration file (YAML/JSON) defining server connections and tool prefixes"],"input_types":["MCP tool call requests (JSON-RPC format)","Configuration files (YAML/JSON with server definitions)","Backend MCP server responses (tools, resources, prompts)"],"output_types":["Aggregated MCP tool definitions with prefixed names","Proxied tool execution results from backend servers","MCP notifications and progress updates from backends"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_1","uri":"capability://automation.workflow.dynamic.configuration.hot.reloading.with.watchdog.file.monitoring","name":"dynamic configuration hot-reloading with watchdog file monitoring","description":"Magg uses watchdog-based file system monitoring to detect configuration changes in real-time and applies them without requiring server restart. The reload.py module watches the configuration file for modifications and triggers ConfigManager to parse updated server definitions, transport settings, and authentication rules. When changes are detected, the system gracefully updates the ServerManager's internal state, reconnecting to modified backends and re-exposing updated tool definitions to connected clients. This enables runtime configuration drift without service interruption.","intents":["I want to add a new MCP server to the aggregator without restarting the service","I need to update tool prefixes or server connection parameters on-the-fly","I want to enable/disable backend servers dynamically for maintenance or testing","I need to change authentication or transport settings without downtime"],"best_for":["Production deployments requiring zero-downtime configuration updates","Development teams iterating on tool configurations during agent development","Multi-tenant systems where different clients need different server configurations"],"limitations":["File system events may have race conditions if configuration is updated faster than watchdog can detect","In-flight tool calls to removed servers may fail mid-execution; no graceful draining of pending requests","Configuration validation errors during reload may leave system in inconsistent state if rollback is not implemented","Watchdog monitoring adds ~50-100ms latency between file write and configuration application"],"requires":["Python 3.9+","watchdog library for file system monitoring","Configuration file in YAML or JSON format","Write permissions to configuration file location"],"input_types":["Configuration file updates (YAML/JSON)","File system events (create, modify, delete)"],"output_types":["Updated ServerManager state","Re-exposed MCP tool definitions","Reconnection events to modified backends"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_10","uri":"capability://automation.workflow.cli.command.interface.for.server.management.and.configuration.operations","name":"cli command interface for server management and configuration operations","description":"Magg provides a comprehensive CLI interface (magg.cli module) with commands for starting the aggregation server, managing authentication, configuring kits, and inspecting server status. The CLI supports subcommands for auth token generation, kit installation/updates, server health checks, and configuration validation. The command processing system parses arguments, validates inputs, and executes operations with formatted output. This enables operators to manage Magg deployments from the command line without requiring programmatic access.","intents":["I want to start the Magg aggregation server with specific configuration","I need to generate JWT tokens for users to access the aggregator","I want to install or update a kit from the command line","I need to check the health and status of all aggregated servers"],"best_for":["DevOps teams managing Magg deployments via command line","Operators needing to perform routine configuration and maintenance tasks","CI/CD pipelines automating Magg deployment and configuration"],"limitations":["CLI is synchronous; long-running operations block the terminal","No built-in output formatting for machine consumption (JSON output requires custom parsing)","Error messages may be verbose or unclear for non-technical operators","No interactive prompts for confirmation; destructive operations require explicit flags","Limited to local operations; no remote management of Magg instances"],"requires":["Python 3.9+","Magg package installed and in PATH","Configuration file for server startup","Write permissions for token generation and kit installation"],"input_types":["CLI arguments and flags","Configuration file paths","User/role specifications for token generation","Kit names and versions"],"output_types":["Server startup logs","Generated JWT tokens","Kit installation status","Server health and status information","Configuration validation results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_11","uri":"capability://automation.workflow.docker.containerization.and.deployment.with.environment.based.configuration","name":"docker containerization and deployment with environment-based configuration","description":"Magg includes Docker support for containerized deployment, with Dockerfile definitions and docker-compose configurations for multi-container setups. The system uses environment variables for configuration, enabling container orchestration platforms (Kubernetes, Docker Swarm) to inject settings at runtime without rebuilding images. The Docker setup includes health checks, volume mounts for configuration files, and network configuration for multi-container deployments. This enables easy deployment to cloud platforms and container orchestration systems.","intents":["I want to deploy Magg as a Docker container in my Kubernetes cluster","I need to configure Magg via environment variables for different deployment environments","I want to run Magg alongside other services in a docker-compose setup","I need to ensure the container is healthy and restart it if it fails"],"best_for":["Cloud-native deployments using Kubernetes or Docker Swarm","Teams using containerized development and production environments","Multi-container deployments where Magg is one of several services"],"limitations":["Docker image size may be large if all dependencies are included; requires optimization for registry storage","Environment variable configuration is flat; complex nested configurations require custom parsing","Health checks are basic (HTTP or command-based); may not detect all failure modes","Volume mounts for configuration require careful permission management","No built-in support for secrets management; requires external secret store (Kubernetes Secrets, Vault)"],"requires":["Docker 20.10+ or compatible container runtime","Docker Compose 1.29+ (for multi-container deployments)","Configuration via environment variables","Network connectivity between containers (for multi-container setups)"],"input_types":["Dockerfile with base image and dependencies","docker-compose.yml with service definitions","Environment variables for configuration","Volume mounts for configuration files"],"output_types":["Docker image with Magg and dependencies","Running container with aggregation server","Health check status","Container logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_2","uri":"capability://tool.use.integration.transport.agnostic.server.communication.with.stdio.http.hybrid.modes","name":"transport-agnostic server communication with stdio/http/hybrid modes","description":"Magg abstracts transport layer complexity through FastMCP integration, supporting three operational modes: stdio (direct process pipes for desktop clients), HTTP (REST API for web/browser access), and hybrid (both simultaneously). The transport layer automatically handles protocol translation between MCP JSON-RPC format and the underlying transport mechanism, allowing the same MaggServer instance to serve multiple client types without code changes. The system selects transport based on configuration and can dynamically switch or add transports without restarting the core aggregation logic.","intents":["I want my MCP aggregator to work with both desktop clients (using stdio) and web-based clients (using HTTP)","I need to expose the same aggregated tools via multiple transport protocols simultaneously","I want to integrate Magg with Claude Desktop (stdio) and also expose it as a REST API for custom clients","I need to support hybrid deployments where some clients use direct pipes and others use HTTP"],"best_for":["Multi-client deployments serving both desktop and web-based LLM applications","Teams building custom MCP clients that need flexibility in transport protocol","Enterprise environments requiring both internal (stdio) and external (HTTP) access patterns"],"limitations":["HTTP mode adds network latency (~10-50ms) compared to stdio direct pipes","Hybrid mode requires managing two separate transport listeners, increasing memory footprint by ~20-30%","HTTP mode requires authentication/authorization layer (JWT-based); stdio mode has implicit process-level security","WebSocket support for HTTP mode not explicitly documented; may require additional configuration for streaming responses"],"requires":["Python 3.9+","FastMCP framework","For HTTP mode: uvicorn or equivalent ASGI server","For stdio mode: parent process managing stdin/stdout pipes","Configuration specifying transport type(s) to enable"],"input_types":["MCP JSON-RPC messages (stdio format or HTTP POST body)","HTTP requests with MCP tool call payloads","Configuration specifying transport modes"],"output_types":["MCP JSON-RPC responses via stdio or HTTP","HTTP REST API responses with tool results","Transport-agnostic MCP protocol messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_3","uri":"capability://tool.use.integration.autonomous.mcp.server.discovery.and.installation.with.package.manager.semantics","name":"autonomous mcp server discovery and installation with package manager semantics","description":"Magg implements package manager semantics for MCP servers, enabling LLMs to autonomously search for, evaluate, and install new servers from a registry without human intervention. The system maintains a searchable registry of available MCP servers with metadata (description, capabilities, dependencies), exposes search and install tools to the LLM, and handles dependency resolution, version management, and server lifecycle setup. When an LLM requests a new capability, it can discover matching servers, review their capabilities, and trigger installation which updates the configuration and reconnects the aggregator to the new backend.","intents":["I want my AI assistant to discover new MCP servers that provide capabilities it doesn't currently have","I need the LLM to autonomously install a new tool server when it encounters a task requiring new capabilities","I want to search for MCP servers by capability (e.g., 'find servers that can interact with databases')","I need the system to handle dependency resolution when installing servers that depend on other servers"],"best_for":["Autonomous AI agents that need to extend their capabilities dynamically based on task requirements","Research teams exploring self-improving AI systems with runtime capability expansion","Multi-user systems where different users need different tool sets without manual configuration"],"limitations":["Registry must be maintained and curated; no automatic validation of server safety or compatibility","LLM may install incompatible or malicious servers if registry is compromised; requires strong authentication","Dependency resolution is complex for servers with conflicting version requirements; may require backtracking","Installation latency varies widely (seconds to minutes) depending on server complexity and network conditions","No automatic rollback if installed server causes system instability; manual intervention required"],"requires":["Python 3.9+","MCP server registry (local or remote)","Package metadata for each server (description, capabilities, dependencies, installation instructions)","Ability to execute server installation scripts or download binaries","Configuration update permissions to add newly installed servers"],"input_types":["Search queries (natural language or structured capability descriptions)","Installation requests (server name, version, optional parameters)","Registry metadata (JSON/YAML with server definitions)"],"output_types":["Search results with server metadata and capability descriptions","Installation status and progress updates","Updated configuration with newly installed servers","Tool definitions from newly installed servers"],"categories":["tool-use-integration","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_4","uri":"capability://tool.use.integration.message.routing.and.protocol.proxying.with.semantic.preservation","name":"message routing and protocol proxying with semantic preservation","description":"Magg implements a message routing system that transparently proxies MCP protocol messages (tool calls, resources, prompts, notifications) from clients to appropriate backend servers based on tool prefix matching. The routing layer preserves full MCP semantics including streaming responses, progress updates, and resource references, translating between the aggregated namespace (prefixed tools) and backend namespaces (unprefixed tools). The system maintains request-response correlation to ensure responses are correctly routed back to clients, and handles protocol-level features like sampling, notifications, and resource subscriptions across server boundaries.","intents":["I want tool calls to be automatically routed to the correct backend server based on tool prefix","I need streaming responses from backend servers to be transparently proxied to the client","I want progress updates and notifications from backend servers to reach the client without modification","I need resource references from one server to be accessible through the aggregator's resource interface"],"best_for":["Complex multi-server deployments requiring transparent tool call routing","Systems with streaming tool responses that need end-to-end latency optimization","Scenarios where backend servers emit notifications or progress updates that must reach clients"],"limitations":["Request-response correlation adds ~5-10ms overhead per message due to tracking and lookup","Streaming responses may have buffering delays if proxy layer batches messages for efficiency","Resource references are namespace-scoped; cross-server resource sharing requires explicit mapping","Notification delivery is best-effort; no guaranteed delivery if client disconnects during notification","Protocol version mismatches between aggregator and backends may cause routing failures"],"requires":["Python 3.9+","MCP protocol implementation (FastMCP)","Backend servers implementing MCP protocol correctly","Tool prefix configuration mapping tools to backend servers","Request tracking mechanism (in-memory or persistent)"],"input_types":["MCP tool call requests with prefixed tool names","MCP resource requests","MCP prompt requests","Backend server responses (tool results, resources, notifications)"],"output_types":["Routed tool calls to backend servers (with prefix stripped)","Proxied tool results to clients (with prefix preserved in context)","Proxied notifications and progress updates","Resource and prompt responses from backends"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_5","uri":"capability://safety.moderation.jwt.based.authentication.and.bearer.token.authorization.for.aggregated.tools","name":"jwt-based authentication and bearer token authorization for aggregated tools","description":"Magg implements JWT-based authentication through the BearerAuthManager class, enabling fine-grained access control over aggregated tools. The system validates bearer tokens in incoming requests, decodes JWT claims to extract user identity and permissions, and enforces authorization rules that determine which tools each user can access. The authentication layer integrates with the tool routing system to filter available tools based on user permissions, and supports token refresh and expiration policies. This enables multi-tenant deployments where different users have different tool access levels.","intents":["I want to restrict which tools different users can access from the aggregated server","I need to implement multi-tenant access control where each tenant has a different set of available tools","I want to track which user executed which tool call for audit and compliance purposes","I need to enforce token expiration and refresh policies for security"],"best_for":["Multi-tenant SaaS deployments of Magg aggregators","Enterprise environments requiring fine-grained access control and audit trails","Systems where different users have different tool access levels based on roles or permissions"],"limitations":["JWT validation adds ~2-5ms latency per request for token parsing and signature verification","Token revocation requires external state store (Redis, database); no built-in token blacklist","Authorization rules are static (defined at startup); no runtime permission updates without restart","No built-in role-based access control (RBAC); requires custom claims in JWT to implement roles","Stdio mode (direct pipes) has implicit process-level security; bearer tokens are less relevant"],"requires":["Python 3.9+","JWT library (PyJWT or similar)","Secret key for JWT signing and verification","Authorization policy definition (mapping users/roles to tool access)","HTTP transport mode (bearer tokens are HTTP-specific)"],"input_types":["HTTP Authorization header with bearer token","JWT claims (user ID, roles, permissions)","Authorization policy configuration"],"output_types":["Validated user identity and permissions","Filtered tool list based on user authorization","Audit logs with user identity and tool execution"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_6","uri":"capability://tool.use.integration.mbro.interactive.mcp.browser.and.repl.for.tool.exploration.and.testing","name":"mbro interactive mcp browser and repl for tool exploration and testing","description":"Magg includes MBRO (MCP Browser and REPL), an interactive command-line interface for exploring and testing aggregated MCP tools without writing code. MBRO connects to the Magg aggregator as an MCP client, discovers available tools, displays their schemas and descriptions, and provides a REPL where users can execute tools with custom parameters and inspect results. The interface includes command processing for navigation, tool inspection, and result formatting, with support for advanced input features like parameter validation and result pretty-printing. This enables developers to interactively test tool configurations and debug tool behavior.","intents":["I want to explore what tools are available in the aggregator without reading configuration files","I need to test a tool with different parameters to verify it works correctly","I want to inspect the schema and documentation of a tool before using it in my agent","I need to debug why a tool call is failing by testing it interactively"],"best_for":["Developers building and testing MCP aggregator configurations","DevOps teams validating tool availability and connectivity","Researchers exploring MCP tool capabilities interactively"],"limitations":["REPL is single-threaded; cannot test concurrent tool execution patterns","Result formatting is text-based; complex nested structures may be hard to read","No built-in persistence of test results or command history across sessions","Parameter validation is basic; complex schema constraints may not be enforced","No support for testing streaming responses or long-running tool executions"],"requires":["Python 3.9+","Running Magg aggregator instance (accessible via stdio or HTTP)","Terminal/console environment for REPL interaction","MCP client library (FastMCP)"],"input_types":["REPL commands (list tools, describe tool, execute tool, etc.)","Tool parameters (JSON or command-line format)","Navigation commands (change server, filter tools, etc.)"],"output_types":["Tool list with names and descriptions","Tool schemas (parameter definitions, return types)","Tool execution results (formatted as text or JSON)","Error messages and debugging information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_7","uri":"capability://automation.workflow.server.lifecycle.management.with.graceful.startup.shutdown.and.health.monitoring","name":"server lifecycle management with graceful startup, shutdown, and health monitoring","description":"Magg implements comprehensive server lifecycle management through the ServerManager class, handling startup initialization of backend server connections, graceful shutdown with pending request draining, and continuous health monitoring. The system tracks connection state for each backend server, detects disconnections and reconnects automatically, and exposes health status through MCP tools. The lifecycle manager ensures that configuration changes trigger appropriate server reconnections, and that shutdown sequences properly close all connections and release resources. This enables reliable long-running deployments with automatic recovery from transient failures.","intents":["I want the aggregator to automatically reconnect to backend servers if they become unavailable","I need to gracefully shut down the aggregator without losing in-flight tool calls","I want to monitor the health of backend server connections and get alerts if servers are down","I need the aggregator to handle backend server restarts without requiring aggregator restart"],"best_for":["Production deployments requiring high availability and automatic recovery","Long-running agent systems that need to survive transient backend failures","Multi-server deployments where individual backend servers may restart independently"],"limitations":["Health monitoring adds ~50-100ms latency per health check interval","Automatic reconnection may mask underlying backend issues; requires monitoring to detect patterns","Graceful shutdown timeout is fixed; long-running tool calls may be forcefully terminated","No built-in circuit breaker pattern; failing backends may cause cascading timeouts","Health status is point-in-time; no historical health metrics or trend analysis"],"requires":["Python 3.9+","Backend MCP servers with stable connection protocols","Configuration specifying health check intervals and timeout thresholds","Monitoring infrastructure to observe health status (optional but recommended)"],"input_types":["Backend server connection parameters","Health check configuration (interval, timeout, retry policy)","Shutdown signals (SIGTERM, SIGINT)"],"output_types":["Server connection state (connected, disconnected, reconnecting)","Health status (healthy, unhealthy, unknown)","Reconnection events and logs","Graceful shutdown confirmation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_8","uri":"capability://automation.workflow.kit.management.for.bundled.server.configurations.and.deployment.packages","name":"kit management for bundled server configurations and deployment packages","description":"Magg implements kit management through the KitManager class, enabling packaging of related MCP servers, configurations, and dependencies into reusable deployment bundles. Kits define a set of servers to install, their configurations, and any shared dependencies, allowing teams to version and distribute pre-configured tool sets. The system supports kit discovery, installation, and updates, with dependency resolution to ensure all required servers are available. Kits can be stored locally or in a registry, enabling easy sharing of tool configurations across teams or projects.","intents":["I want to package a set of related MCP servers with their configurations as a reusable kit","I need to distribute a pre-configured tool set to my team without requiring manual setup","I want to version my tool configurations and roll back to previous versions if needed","I need to manage dependencies between servers in a kit (e.g., database server requires auth server)"],"best_for":["Teams sharing common tool configurations across projects","Organizations distributing standardized tool sets to multiple deployments","Projects requiring reproducible tool configurations across environments"],"limitations":["Kit versioning requires external registry or version control; no built-in version management","Dependency resolution is basic; complex dependency graphs may require manual ordering","Kit updates may conflict with local customizations; no merge strategy for configuration conflicts","No built-in validation that all kit dependencies are compatible with each other","Kit installation is all-or-nothing; partial installation on failure may leave system in inconsistent state"],"requires":["Python 3.9+","Kit definition file (YAML/JSON with server list and configurations)","Access to kit registry (local directory or remote repository)","Ability to install servers specified in kit"],"input_types":["Kit definition (YAML/JSON with server list, versions, configurations)","Kit registry location (local path or remote URL)","Installation parameters (override configurations, selective installation)"],"output_types":["Installed servers and configurations","Kit status and version information","Dependency resolution results","Installation logs and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-magg__cap_9","uri":"capability://tool.use.integration.resource.and.prompt.management.with.cross.server.reference.resolution","name":"resource and prompt management with cross-server reference resolution","description":"Magg extends MCP protocol support to include resource and prompt management, enabling backend servers to expose resources (files, data, URLs) and prompts (reusable instruction templates) that clients can discover and use. The system maintains a registry of resources and prompts from all backend servers, handles cross-server references (e.g., a tool in server A referencing a resource in server B), and provides resolution mechanisms to translate between aggregated and backend namespaces. This enables rich tool interactions where tools can reference shared resources and prompts across server boundaries.","intents":["I want tools from different servers to reference shared resources without duplicating data","I need to discover available prompts from all aggregated servers and use them in my agent","I want to manage resources (files, URLs, data) that are shared across multiple tool servers","I need to resolve resource references across server boundaries transparently"],"best_for":["Complex multi-server deployments with shared resources and prompts","Systems where tools from different servers need to collaborate using shared data","Scenarios requiring rich tool interactions with reusable instruction templates"],"limitations":["Resource namespace conflicts across servers require manual resolution; no automatic deduplication","Cross-server resource references add latency for reference resolution (~10-20ms per reference)","Resource lifecycle management is complex; no built-in garbage collection for unused resources","Prompt versioning is not supported; updates to prompts may break existing tool calls","No built-in access control for resources; all users see all resources from all servers"],"requires":["Python 3.9+","Backend servers implementing MCP resource and prompt protocols","Resource registry and reference resolution mechanism","Namespace mapping configuration for cross-server references"],"input_types":["Resource requests (by URI or name)","Prompt requests (by name or description)","Cross-server resource references","Resource metadata (type, size, access control)"],"output_types":["Resource content (files, data, URLs)","Prompt definitions (instruction templates)","Resource and prompt listings with metadata","Resolved cross-server references"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastMCP framework integration","Backend MCP servers accessible via stdio, HTTP, or SSE transport","Configuration file (YAML/JSON) defining server connections and tool prefixes","watchdog library for file system monitoring","Configuration file in YAML or JSON format","Write permissions to configuration file location","Magg package installed and in PATH","Configuration file for server startup","Write permissions for token generation and kit installation"],"failure_modes":["Tool name collisions across servers require manual prefix configuration; no automatic conflict resolution","Latency increases with number of aggregated servers due to sequential message routing","No built-in load balancing or failover for backend servers — single point of failure per backend","Configuration changes require file-based updates; no runtime API for adding/removing servers without restart in some modes","File system events may have race conditions if configuration is updated faster than watchdog can detect","In-flight tool calls to removed servers may fail mid-execution; no graceful draining of pending requests","Configuration validation errors during reload may leave system in inconsistent state if rollback is not implemented","Watchdog monitoring adds ~50-100ms latency between file write and configuration application","CLI is synchronous; long-running operations block the terminal","No built-in output formatting for machine consumption (JSON output requires custom parsing)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=magg","compare_url":"https://unfragile.ai/compare?artifact=magg"}},"signature":"0gXw30hQeoYkvmbXBbwRuIQia+OAQ6McIMq++16+ngho1DV5TEHkMH5sRuTbd6TQsytUv82qSO5VRlXkiY0oDA==","signedAt":"2026-06-20T11:15:43.629Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/magg","artifact":"https://unfragile.ai/magg","verify":"https://unfragile.ai/api/v1/verify?slug=magg","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"}}