{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mcpjungle","slug":"mcpjungle","name":"MCPJungle","type":"mcp","url":"https://github.com/mcpjungle/MCPJungle","page_url":"https://unfragile.ai/mcpjungle","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mcpjungle__cap_0","uri":"capability://tool.use.integration.unified.mcp.server.aggregation.and.proxy.gateway","name":"unified mcp server aggregation and proxy gateway","description":"MCPJungle acts as a centralized MCP-compliant proxy that consolidates multiple upstream MCP servers (stdio, SSE, HTTP transports) into a single gateway endpoint. Agents connect once to MCPJungle's /mcp endpoint instead of configuring individual server connections; the gateway internally maintains persistent connections to all registered servers, multiplexes tool discovery requests, and routes tool invocations to the correct upstream server based on canonical naming (server__tool format). This eliminates N×M configuration complexity where N agents must each configure M servers.","intents":["I want my AI agent to access tools from 10+ MCP servers without reconfiguring the agent for each new server","I need a single connection point for all my MCP tools instead of managing dozens of individual server endpoints","I want to add or remove MCP servers from my agent ecosystem without touching agent configuration"],"best_for":["teams deploying multiple MCP servers across development, staging, and production environments","enterprises needing centralized tool management and observability across AI agents","developers building multi-tool AI agents who want to decouple agent configuration from server topology"],"limitations":["Adds single point of failure — if MCPJungle gateway goes down, all connected agents lose access to all tools (mitigated by horizontal scaling and load balancing)","Introduces network latency for tool discovery and invocation compared to direct agent-to-server connections (typically <50ms per hop)","Requires persistent connections to all upstream servers — high-cardinality server counts (100+) may require connection pooling tuning","Tool name collisions across servers are resolved via canonical naming, but agents must know the server prefix to invoke tools"],"requires":["Go 1.21+ (for building from source) or Docker/binary deployment","At least one MCP server registered (stdio, SSE, or HTTP transport)","Network connectivity from MCPJungle to all upstream MCP servers","MCP-compatible client (Claude Desktop, Cursor, or custom MCP client)"],"input_types":["MCP protocol messages (tool list requests, tool invocation requests, prompt requests)","Server registration configuration (transport type, endpoint, authentication)"],"output_types":["MCP protocol responses (tool definitions, invocation results, prompt templates)","Aggregated tool catalog with canonical naming"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_1","uri":"capability://tool.use.integration.multi.transport.mcp.server.connection.management","name":"multi-transport mcp server connection management","description":"MCPJungle manages connections to upstream MCP servers across three transport types: stdio (local process spawning), SSE (Server-Sent Events over HTTP), and HTTP (bidirectional JSON-RPC). The gateway maintains a transport abstraction layer that handles protocol-specific connection lifecycle (spawn/connect/reconnect/disconnect), message serialization/deserialization, and error recovery. Each registered server's transport type is persisted in the database; MCPJungle automatically handles reconnection logic with exponential backoff for failed connections, enabling heterogeneous server ecosystems where some servers are local processes and others are remote HTTP endpoints.","intents":["I have MCP servers deployed as local processes, remote HTTP services, and SSE endpoints — I need one gateway to manage all of them","I want automatic reconnection and health checking for upstream MCP servers without manual intervention","I need to migrate a server from stdio to HTTP without reconfiguring my agents"],"best_for":["polyglot teams with MCP servers in different languages and deployment models (local binaries, Docker containers, cloud services)","operations teams managing MCP infrastructure with mixed on-premises and cloud deployments","developers building resilient AI agent systems that tolerate upstream server failures"],"limitations":["Stdio transport requires MCPJungle to run on the same machine or have filesystem access to server binaries — not suitable for serverless deployments","SSE transport is unidirectional (server → client) — MCPJungle must use HTTP POST for tool invocations, adding latency vs bidirectional protocols","HTTP transport requires upstream servers to implement JSON-RPC 2.0 correctly — malformed responses may cause connection drops","No automatic service discovery — servers must be manually registered; no integration with Kubernetes service discovery or Consul"],"requires":["For stdio: executable MCP server binary accessible from MCPJungle process","For SSE: upstream server implementing MCP SSE transport specification","For HTTP: upstream server implementing MCP HTTP transport with JSON-RPC 2.0","Network connectivity from MCPJungle to all upstream servers (for SSE/HTTP)"],"input_types":["Server registration config (transport type, endpoint URL or binary path, environment variables, authentication credentials)"],"output_types":["Connection status (connected, disconnected, reconnecting)","Transport-agnostic MCP messages routed to/from upstream servers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_10","uri":"capability://automation.workflow.development.vs.enterprise.deployment.modes","name":"development vs enterprise deployment modes","description":"MCPJungle supports two deployment modes: development mode (single-process, in-memory state, no authentication) and enterprise mode (distributed, persistent database, authentication/authorization, observability). Development mode is suitable for local testing and prototyping; enterprise mode adds production-grade features including database persistence, access control, audit logging, and metrics collection. Mode is selected at startup via configuration; switching modes requires database migration.","intents":["I want to quickly prototype MCPJungle locally without setting up a database or authentication","I need to deploy MCPJungle in production with persistent state, access control, and observability","I want to migrate from development to enterprise mode as my deployment scales"],"best_for":["developers prototyping MCPJungle locally in development mode","enterprises deploying MCPJungle in production with enterprise mode","teams transitioning from development to production deployments"],"limitations":["Development mode has no persistence — all configuration is lost on restart","Development mode has no access control — any client can invoke any tool","Enterprise mode requires database setup (PostgreSQL, MySQL, SQLite) — adds operational complexity","Mode switching requires database migration — cannot easily switch between modes without data loss"],"requires":["For development mode: no additional requirements","For enterprise mode: database (PostgreSQL, MySQL, or SQLite) and database credentials"],"input_types":["Configuration (mode selection, database connection string, authentication settings)"],"output_types":["MCPJungle instance running in selected mode"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_11","uri":"capability://automation.workflow.docker.and.binary.deployment.with.production.configuration","name":"docker and binary deployment with production configuration","description":"MCPJungle provides multiple deployment options: Docker containers (with docker-compose for local development and production), standalone binaries (Linux, macOS, Windows), and Kubernetes-ready configurations. Production deployments support environment variable configuration, database connection pooling, TLS/mTLS for upstream server connections, and horizontal scaling behind a load balancer. Docker images are published to registries; binaries are built via GoReleaser for multiple platforms.","intents":["I want to run MCPJungle locally using Docker Compose for development","I need to deploy MCPJungle to production with proper TLS, database pooling, and load balancing","I want to run MCPJungle on Kubernetes with auto-scaling and high availability"],"best_for":["teams deploying MCPJungle in containerized environments (Docker, Kubernetes)","DevOps teams managing MCPJungle infrastructure with infrastructure-as-code","organizations requiring high availability and horizontal scaling"],"limitations":["Docker deployment requires Docker and Docker Compose — not suitable for serverless environments","Kubernetes deployment requires Kubernetes cluster — adds operational complexity","Horizontal scaling requires shared database — database becomes a potential bottleneck","TLS configuration is manual — requires certificate management and renewal"],"requires":["For Docker: Docker and Docker Compose installed","For binary: compatible OS (Linux, macOS, Windows) and architecture (x86_64, ARM64)","For production: database (PostgreSQL, MySQL, SQLite), TLS certificates, load balancer"],"input_types":["Docker Compose configuration or environment variables for binary deployment"],"output_types":["Running MCPJungle instance accessible via HTTP/MCP endpoints"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_12","uri":"capability://tool.use.integration.go.client.library.for.programmatic.mcpjungle.integration","name":"go client library for programmatic mcpjungle integration","description":"MCPJungle provides a native Go client library that enables Go applications to programmatically manage MCPJungle (register servers, manage tools, define access policies) and invoke tools through the gateway. The client library wraps the HTTP API with type-safe Go methods, handles authentication, and provides structured error handling. This enables Go-based infrastructure automation, monitoring systems, and custom management tools to integrate with MCPJungle without writing HTTP requests manually.","intents":["I want to write Go code that programmatically manages MCPJungle servers and tools","I need to build a custom management dashboard or monitoring system in Go that integrates with MCPJungle","I want to automate MCPJungle configuration from Go-based infrastructure tools"],"best_for":["Go developers building custom management tools or dashboards for MCPJungle","teams using Go-based infrastructure automation (Terraform providers, Kubernetes operators)","organizations building Go-based monitoring or observability systems"],"limitations":["Go client library is Go-only — no support for other languages (Python, Node.js, etc.)","Client library is not auto-generated from OpenAPI spec — may lag behind HTTP API changes","No async/await support — all operations are synchronous (Go goroutines can be used for concurrency)","Limited documentation — requires reading source code or HTTP API docs to understand client usage"],"requires":["Go 1.21+ (for building applications using the client library)","MCPJungle HTTP API accessible (localhost:8080 by default)","Authentication credentials (API key, OAuth token, or mTLS certificate)"],"input_types":["Go method calls with structured parameters (server config, tool names, access policies, etc.)"],"output_types":["Go structs with operation results, server status, tool lists, error details"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_2","uri":"capability://search.retrieval.tool.discovery.and.canonical.naming.with.collision.resolution","name":"tool discovery and canonical naming with collision resolution","description":"MCPJungle aggregates tool definitions from all registered upstream servers via the MCP tools/list protocol, applies a canonical naming scheme (server__toolname) to prevent collisions, and exposes the merged catalog through a single tools/list endpoint. The gateway caches tool definitions in its database with server provenance metadata, enabling fast discovery without querying all upstream servers on every request. When agents invoke tools, MCPJungle parses the canonical name to route the invocation to the correct upstream server, transparently stripping the server prefix before forwarding to the target server.","intents":["I want to see all available tools from all my MCP servers in one place without name conflicts","I need to know which server provides each tool so I can understand tool provenance and dependencies","I want fast tool discovery that doesn't require querying all upstream servers every time"],"best_for":["teams with multiple MCP servers that may provide tools with identical names (e.g., multiple 'execute' or 'search' tools)","developers building tool-aware AI agents that need to understand tool availability and metadata","operations teams needing visibility into which tools are available and which servers provide them"],"limitations":["Canonical naming (server__toolname) requires agents to know the server prefix — agents cannot discover tools by name alone without parsing the prefix","Tool definition caching is not real-time — if an upstream server adds/removes tools, MCPJungle must be refreshed (via CLI or API) to see changes","No automatic tool versioning — if a server updates a tool's schema, MCPJungle serves the cached version until explicitly refreshed","Tool name collisions are resolved by prefix, but if two servers have identical tool names, agents must explicitly choose which to invoke"],"requires":["All upstream servers must implement MCP tools/list protocol","Tool definitions must be valid JSON Schema (MCPJungle validates on registration)","Database connectivity to persist tool metadata"],"input_types":["MCP tools/list responses from upstream servers (tool name, description, input schema, output schema)"],"output_types":["Aggregated tool catalog with canonical names (server__toolname)","Tool metadata including server provenance, schema, and description"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_3","uri":"capability://tool.use.integration.tool.invocation.routing.with.session.aware.context.preservation","name":"tool invocation routing with session-aware context preservation","description":"MCPJungle intercepts tool invocation requests (tools/call) from agents, parses the canonical tool name (server__toolname) to identify the target upstream server, and routes the invocation to that server while preserving session context and request metadata. The gateway maintains per-session state including authentication tokens, request IDs, and invocation history, enabling stateful tool interactions where multiple tool calls within a session share context. Tool results are returned to the agent with metadata about execution time, server, and any errors, enabling observability and debugging.","intents":["I want to invoke tools from different servers in sequence while maintaining context across calls","I need to understand which server executed each tool call and how long it took","I want to handle tool invocation errors gracefully and retry failed calls"],"best_for":["AI agents executing multi-step workflows that require tools from different servers","developers debugging tool invocation failures and needing detailed execution traces","teams requiring audit trails of which tools were called, when, and by whom"],"limitations":["Session state is in-memory by default — if MCPJungle restarts, session context is lost (requires external state store for persistence)","No built-in retry logic for failed tool invocations — agents must implement retry logic or MCPJungle must be configured with retry policies","Tool invocation timeout is configurable but global — cannot set per-tool or per-server timeouts","No transaction semantics — if a multi-step workflow fails partway through, MCPJungle does not automatically rollback previous tool calls"],"requires":["Upstream server must implement MCP tools/call protocol","Tool input must match the tool's input schema (MCPJungle validates before forwarding)","Network connectivity to the target upstream server"],"input_types":["MCP tools/call request (tool name, arguments, optional session ID)"],"output_types":["Tool invocation result (output data, execution metadata, error details)","Session context (request ID, timestamp, server, execution time)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_4","uri":"capability://tool.use.integration.tool.grouping.and.selective.tool.filtering","name":"tool grouping and selective tool filtering","description":"MCPJungle supports organizing tools into logical groups (e.g., 'file-operations', 'web-search', 'database-admin') and filtering which tools are available to specific agents or users. Tool groups are defined in the database and can include tools from multiple servers; agents can request a filtered tool list (e.g., tools/list?group=file-operations) to see only tools in that group. This enables fine-grained access control and reduces cognitive load for agents that only need a subset of available tools.","intents":["I want to expose only certain tools to specific AI agents based on their role or use case","I want to organize my tools into logical groups (e.g., 'read-only', 'admin', 'user-facing') for easier discovery","I want to prevent agents from accessing sensitive tools like database deletion or system administration"],"best_for":["multi-tenant deployments where different users/teams should see different tools","enterprises with role-based access control (RBAC) requirements for tool access","teams with large tool catalogs (50+ tools) that want to organize tools by function or risk level"],"limitations":["Tool group membership is static — tools cannot be dynamically added/removed from groups at runtime without API calls","No hierarchical grouping — groups are flat; cannot create nested groups like 'admin/database/delete'","Filtering is applied at discovery time only — if an agent somehow obtains a tool name outside its allowed group, MCPJungle will still invoke it (requires additional authorization checks)","No group-level rate limiting or quota management — all tools in a group share the same rate limits"],"requires":["Tool groups must be pre-defined in the database","Agents must support filtering parameters in tools/list requests (or MCPJungle must provide a filtered endpoint)"],"input_types":["Tool group definitions (name, description, member tools)","Filtered tool list requests (group name or filter criteria)"],"output_types":["Filtered tool catalog containing only tools in the requested group"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_5","uri":"capability://safety.moderation.enterprise.access.control.with.server.level.allowlists","name":"enterprise access control with server-level allowlists","description":"MCPJungle's enterprise mode provides server-level access control via allowlists, enabling administrators to specify which agents or users can access which upstream servers. Access control is enforced at the gateway level before tool invocations are routed to upstream servers. The system supports multiple authentication methods (API keys, OAuth, mutual TLS) and integrates with the HTTP API and CLI for managing access policies. Access decisions are logged for audit purposes.","intents":["I want to restrict which agents can access sensitive servers (e.g., production database tools)","I need to implement role-based access control where different teams can only access their own servers","I want to audit which agents accessed which servers and when"],"best_for":["enterprises with multi-team deployments requiring isolation between teams","regulated industries (finance, healthcare) requiring audit trails and access control","organizations with sensitive data or operations that should only be accessible to authorized agents"],"limitations":["Access control is enforced at the server level, not the tool level — if an agent has access to a server, it can invoke all tools on that server (tool-level filtering requires tool groups)","No dynamic access control based on tool input — cannot restrict access based on what data a tool will operate on","Enterprise mode requires additional configuration and database setup — development mode has no access control","Access decisions are not cached — every tool invocation requires an access control check, adding latency"],"requires":["Enterprise mode enabled (requires configuration and database)","Authentication credentials for agents (API keys, OAuth tokens, or mTLS certificates)","Access control policies defined in the database"],"input_types":["Agent authentication credentials (API key, OAuth token, mTLS certificate)","Access control policy definitions (agent/user, server, allow/deny)"],"output_types":["Access control decision (allow/deny)","Audit log entry (agent, server, timestamp, decision)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_6","uri":"capability://text.generation.language.prompt.template.aggregation.and.management","name":"prompt template aggregation and management","description":"MCPJungle aggregates prompt templates from upstream MCP servers (via the MCP prompts/list protocol) and exposes them through a unified prompts/list endpoint, similar to tool aggregation. Prompts are cached in the database with server provenance metadata. Agents can request prompt templates by canonical name (server__promptname), and MCPJungle retrieves the template from the upstream server, applies any variable substitution, and returns the rendered prompt to the agent. This enables agents to access reusable prompt templates from multiple servers without reconfiguring.","intents":["I want my agents to access prompt templates from multiple MCP servers without configuring each server individually","I need to discover what prompt templates are available across all my MCP servers","I want to use prompt templates from different servers in my agent workflows"],"best_for":["teams using MCP servers that provide domain-specific prompt templates (e.g., code review templates, documentation templates)","developers building agents that leverage pre-built prompts for consistency and quality","organizations standardizing on prompt templates across multiple teams"],"limitations":["Prompt caching is not real-time — if an upstream server updates a prompt template, MCPJungle must be refreshed to see changes","No prompt versioning — if a server updates a prompt, MCPJungle serves the cached version until explicitly refreshed","Variable substitution is server-specific — MCPJungle does not provide a unified variable substitution mechanism across servers","Prompt templates are read-only from the agent perspective — agents cannot create or modify prompts through MCPJungle"],"requires":["Upstream servers must implement MCP prompts/list protocol","Prompt definitions must be valid (MCPJungle validates on registration)","Database connectivity to persist prompt metadata"],"input_types":["MCP prompts/list responses from upstream servers (prompt name, description, arguments, template text)"],"output_types":["Aggregated prompt catalog with canonical names (server__promptname)","Rendered prompt templates with variable substitution"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_7","uri":"capability://automation.workflow.centralized.observability.and.metrics.collection","name":"centralized observability and metrics collection","description":"MCPJungle collects detailed metrics about tool invocations, server connections, and gateway performance, including execution time, success/failure rates, error types, and per-server statistics. Metrics are exposed via Prometheus-compatible endpoints and can be scraped by monitoring systems like Prometheus or Datadog. The gateway also maintains structured logs of all tool invocations, server connections, and access control decisions, enabling debugging and audit trails. Observability data includes request IDs, timestamps, agent identifiers, and execution context.","intents":["I want to monitor the health and performance of all my MCP servers through a single dashboard","I need to understand which tools are being used most frequently and which servers are bottlenecks","I want to debug tool invocation failures and understand where errors are occurring"],"best_for":["operations teams managing MCP infrastructure in production","developers debugging tool invocation failures and performance issues","teams requiring observability for compliance and audit purposes"],"limitations":["Metrics are in-memory by default — if MCPJungle restarts, historical metrics are lost (requires external metrics storage like Prometheus)","No built-in alerting — metrics must be exported to external monitoring systems for alerting","Logging is structured but not queryable — requires external log aggregation (ELK, Datadog) for full-text search","High cardinality metrics (per-tool, per-agent) may cause performance issues with large tool catalogs"],"requires":["Prometheus or compatible metrics scraper (for metrics collection)","Optional: log aggregation system (ELK, Datadog, CloudWatch) for log storage and analysis"],"input_types":["Tool invocation requests and responses","Server connection events","Access control decisions"],"output_types":["Prometheus metrics (execution time, success rate, error rate, per-server statistics)","Structured logs (JSON format with request ID, timestamp, agent, server, tool, result)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_8","uri":"capability://automation.workflow.cli.based.server.and.tool.management","name":"cli-based server and tool management","description":"MCPJungle provides a comprehensive CLI (mcpjungle command) for managing servers, tools, prompts, and access control without using the HTTP API. The CLI supports commands for server registration/deregistration, tool enable/disable, tool group management, access control policy definition, and server lifecycle operations (start, stop, status). CLI commands interact with the MCPJungle HTTP API and persist configuration to the database. This enables infrastructure-as-code workflows where server configurations can be version-controlled and deployed via CI/CD pipelines.","intents":["I want to register and manage MCP servers using command-line tools that integrate with my CI/CD pipeline","I need to enable/disable tools and manage access control without writing API calls","I want to version-control my MCPJungle configuration and deploy it consistently across environments"],"best_for":["DevOps teams managing MCPJungle infrastructure via infrastructure-as-code","developers who prefer CLI tools over HTTP APIs","teams deploying MCPJungle in containerized environments where CLI scripting is natural"],"limitations":["CLI is synchronous — long-running operations (server startup) block until completion","No interactive mode — CLI is designed for scripting, not interactive exploration","Error messages are text-based — no structured error responses for programmatic error handling","CLI requires network access to MCPJungle HTTP API — cannot manage local MCPJungle instances without network connectivity"],"requires":["mcpjungle CLI binary (included in releases or built from source)","Network connectivity to MCPJungle HTTP API (localhost:8080 by default)","Authentication credentials (API key or OAuth token) for API access"],"input_types":["CLI arguments and flags (server name, transport type, endpoint, tool names, group names, access policies)"],"output_types":["CLI output (success/error messages, server status, tool lists, configuration details)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcpjungle__cap_9","uri":"capability://automation.workflow.http.api.for.programmatic.gateway.management","name":"http api for programmatic gateway management","description":"MCPJungle exposes a comprehensive HTTP API (REST + JSON) for all management operations: server registration/deregistration, tool enable/disable, tool group management, access control policy definition, and server status queries. The API supports authentication via API keys, OAuth, or mutual TLS. All API responses are JSON with consistent error handling and status codes. The API enables programmatic integration with external systems (CI/CD, monitoring, configuration management) and supports both synchronous and asynchronous operations.","intents":["I want to programmatically register and manage MCP servers from my CI/CD pipeline or configuration management system","I need to query MCPJungle's state (server status, tool availability, access policies) from external systems","I want to integrate MCPJungle management with my existing infrastructure automation tools"],"best_for":["teams integrating MCPJungle with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)","developers building custom management tools or dashboards on top of MCPJungle","organizations automating infrastructure provisioning with Terraform, Ansible, or similar tools"],"limitations":["API is synchronous by default — long-running operations (server startup) block until completion","No webhooks or event streaming — external systems must poll the API for state changes","Rate limiting is global — no per-client or per-endpoint rate limits","API versioning is not explicitly versioned — breaking changes may occur between releases"],"requires":["HTTP client library (curl, requests, axios, etc.)","Authentication credentials (API key, OAuth token, or mTLS certificate)","Network connectivity to MCPJungle HTTP API (default: localhost:8080)"],"input_types":["HTTP requests (JSON body with server config, tool names, access policies, etc.)"],"output_types":["HTTP responses (JSON with operation results, server status, tool lists, error details)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Go 1.21+ (for building from source) or Docker/binary deployment","At least one MCP server registered (stdio, SSE, or HTTP transport)","Network connectivity from MCPJungle to all upstream MCP servers","MCP-compatible client (Claude Desktop, Cursor, or custom MCP client)","For stdio: executable MCP server binary accessible from MCPJungle process","For SSE: upstream server implementing MCP SSE transport specification","For HTTP: upstream server implementing MCP HTTP transport with JSON-RPC 2.0","Network connectivity from MCPJungle to all upstream servers (for SSE/HTTP)","For development mode: no additional requirements","For enterprise mode: database (PostgreSQL, MySQL, or SQLite) and database credentials"],"failure_modes":["Adds single point of failure — if MCPJungle gateway goes down, all connected agents lose access to all tools (mitigated by horizontal scaling and load balancing)","Introduces network latency for tool discovery and invocation compared to direct agent-to-server connections (typically <50ms per hop)","Requires persistent connections to all upstream servers — high-cardinality server counts (100+) may require connection pooling tuning","Tool name collisions across servers are resolved via canonical naming, but agents must know the server prefix to invoke tools","Stdio transport requires MCPJungle to run on the same machine or have filesystem access to server binaries — not suitable for serverless deployments","SSE transport is unidirectional (server → client) — MCPJungle must use HTTP POST for tool invocations, adding latency vs bidirectional protocols","HTTP transport requires upstream servers to implement JSON-RPC 2.0 correctly — malformed responses may cause connection drops","No automatic service discovery — servers must be manually registered; no integration with Kubernetes service discovery or Consul","Development mode has no persistence — all configuration is lost on restart","Development mode has no access control — any client can invoke any tool","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"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=mcpjungle","compare_url":"https://unfragile.ai/compare?artifact=mcpjungle"}},"signature":"5aib1V2F7PNe4noGQx1ty2dl497rekJfssYSKKSwCI9LvyqAp+FQYmQTf0B7+zJjKvyfXhxps6KcBNZQziyiAg==","signedAt":"2026-06-20T17:30:28.307Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcpjungle","artifact":"https://unfragile.ai/mcpjungle","verify":"https://unfragile.ai/api/v1/verify?slug=mcpjungle","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"}}