| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Implements a stateful MCP proxy gateway in Go with persistent upstream connections and canonical naming (server__tool) to prevent tool name collisions across multiple servers, combined with session-aware tool invocation routing that maintains context across distributed server calls
vs alternatives: Unlike manual agent configuration or simple load balancers, MCPJungle provides MCP-native aggregation with built-in collision resolution and centralized access control, eliminating the need to reconfigure agents when server topology changes
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.
Unique: Implements a pluggable transport layer with unified connection lifecycle management across stdio, SSE, and HTTP transports, including automatic reconnection with exponential backoff and per-transport error handling strategies, allowing heterogeneous MCP server ecosystems to be managed as a single logical system
vs alternatives: Most MCP clients support only one transport type; MCPJungle's transport abstraction enables mixing stdio (local), SSE (streaming), and HTTP (cloud) servers in a single gateway without agent-side complexity
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.
Unique: Provides two distinct deployment modes (development and enterprise) with different feature sets and operational requirements, enabling rapid prototyping in development mode and production-grade deployments in enterprise mode from the same codebase
vs alternatives: Most tools require separate development and production versions; MCPJungle provides both modes in a single binary, enabling easy progression from prototyping to production without code changes
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.
Unique: Provides multiple deployment options (Docker, binary, Kubernetes) with production-grade features (TLS, database pooling, load balancing, horizontal scaling), enabling MCPJungle to be deployed from local development to large-scale production environments
vs alternatives: Many tools support only one deployment model; MCPJungle supports Docker, binary, and Kubernetes deployments from the same codebase, enabling flexibility in deployment choices
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.
Unique: Provides a native Go client library with type-safe methods for all management operations, enabling Go applications to integrate with MCPJungle without writing HTTP requests, and supporting Go-based infrastructure automation and custom tooling
vs alternatives: HTTP API requires manual HTTP request construction; Go client library provides type-safe, idiomatic Go methods, making it easier to integrate MCPJungle into Go-based infrastructure tools and applications
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.
Unique: Implements a canonical naming scheme (server__toolname) combined with database-backed caching of tool definitions and server provenance, enabling collision-free tool discovery across multiple servers while maintaining fast lookups without querying upstream servers on every request
vs alternatives: Unlike agents that must configure each server individually and handle name collisions manually, MCPJungle provides automatic collision resolution and centralized tool discovery with caching, reducing agent-side complexity
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.
Unique: Implements session-aware tool invocation routing that preserves context across multiple tool calls to different servers, with built-in metadata tracking (execution time, server, request ID) and per-session state management, enabling stateful multi-step workflows across distributed tool providers
vs alternatives: Direct agent-to-server connections require agents to manage routing and session state; MCPJungle centralizes this logic, enabling agents to invoke tools without knowing server topology and providing built-in observability
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.
Unique: Implements database-backed tool grouping with query-time filtering, allowing tools from multiple servers to be organized into logical groups and selectively exposed to agents based on group membership, enabling fine-grained access control without modifying upstream servers
vs alternatives: Upstream MCP servers have no concept of tool grouping or filtering; MCPJungle adds this capability at the gateway layer, enabling multi-tenant and RBAC scenarios without requiring changes to server implementations
+5 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs MCPJungle at 26/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities