| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements a proxy pattern that bridges MCP clients to multiple backend MCP servers through a single stdio endpoint. The aggregator launches and manages child processes for each configured backend server, establishes JSON-RPC communication channels with each, and presents all discovered tools through a unified interface. This solves the fundamental limitation of MCP clients like Cursor that can only connect to 2-3 servers simultaneously by multiplexing connections server-side.
Unique: Uses a bidirectional proxy architecture where the aggregator acts as both an MCP server (to clients) and MCP client (to backends), managing full process lifecycle and stdio communication for each backend rather than requiring pre-running servers or external orchestration
vs alternatives: Eliminates the need for clients to support multiple simultaneous connections by centralizing multiplexing server-side, unlike manual configuration of multiple client connections which hits hard limits in tools like Cursor
Implements a three-layer name management system to handle tool naming conflicts across multiple backend servers while maintaining compatibility with MCP clients like Cursor. Tools are automatically prefixed with server identifiers (e.g., 'shortcut_search_stories'), sanitized by replacing dashes with underscores for Cursor compatibility, and mapped bidirectionally so sanitized names route back to original names for backend invocation. This prevents tool name collisions while preserving backend tool semantics.
Unique: Implements automatic bidirectional name mapping with server-based prefixing and character sanitization in a single pass during tool discovery, rather than requiring manual tool name configuration or client-side name resolution logic
vs alternatives: Avoids manual tool renaming or client configuration by automatically handling both naming conflicts and client compatibility constraints, whereas manual approaches require per-tool configuration and don't scale with new servers
Includes CI/CD pipeline configuration for automated testing, building, and releasing the MCP aggregator. The pipeline runs tests on code changes, builds binaries for multiple platforms (Linux/Darwin, amd64/arm64), and publishes releases to GitHub. This enables developers to contribute with confidence that changes are tested, and operators to deploy pre-built binaries without building from source. The pipeline is configured via GitHub Actions or similar CI/CD systems.
Unique: Provides automated multi-platform binary building and release publishing via CI/CD pipeline, eliminating manual build and release steps for operators
vs alternatives: Enables automated testing and release workflows compared to manual building and publishing, and provides pre-built binaries for multiple platforms reducing deployment friction
Provides configurable allowlists for each backend MCP server to selectively expose only specified tools through the aggregator. Tool filtering is defined in the JSON configuration via 'tools.allowed' arrays per server, enabling fine-grained control over which tools are discoverable and invokable by clients. This allows operators to restrict tool exposure based on security policies, licensing, or organizational requirements without modifying backend servers.
Unique: Implements server-side allowlisting at the aggregator level rather than relying on backend server configuration, enabling centralized tool exposure control across multiple backends from a single configuration file
vs alternatives: Provides centralized tool filtering without modifying backend servers or requiring per-client configuration, whereas backend-level filtering would require changes to each server and client-side filtering would duplicate logic across clients
Manages the full lifecycle of backend MCP server processes by launching them as child processes, establishing stdio communication channels, and handling JSON-RPC message routing over those channels. The system carefully isolates stdout to prevent backend server logging from corrupting the JSON-RPC protocol stream, implements error handling for process failures, and maintains bidirectional communication with each backend server. This enables the aggregator to transparently invoke tools on remote servers as if they were local.
Unique: Implements careful stdout isolation and JSON-RPC message routing to prevent backend server logging from corrupting protocol streams, using a dedicated communication channel per backend server rather than multiplexing all servers over a single stdio connection
vs alternatives: Provides transparent process management without requiring pre-running servers or external orchestration tools, whereas alternatives like Docker Compose or systemd require separate configuration and don't provide unified tool aggregation
Supports forcing specific MCP protocol versions via the 'MCP_PROTOCOL_VERSION' environment variable and includes Cursor-specific adjustments configurable via 'MCP_CURSOR_MODE'. This allows the aggregator to adapt its protocol behavior to match client expectations, ensuring compatibility with different MCP client implementations that may have varying protocol support or quirks. The system can present different protocol versions to clients while maintaining compatibility with backend servers.
Unique: Provides environment-variable-based protocol version forcing and Cursor-specific compatibility mode rather than automatic protocol negotiation, allowing explicit control over protocol behavior for known client quirks
vs alternatives: Enables compatibility with specific MCP clients like Cursor without modifying client code, whereas automatic negotiation might not handle client-specific quirks or undocumented protocol expectations
Uses a declarative JSON configuration file to specify all backend MCP servers, their launch commands, tool allowlists, and aggregator behavior. The configuration system parses server definitions, tool filtering rules, and environment variables from a single config file, enabling operators to manage the entire aggregator topology without code changes. Configuration is loaded at startup and applied to all subsequent tool discovery and invocation operations.
Unique: Uses a single declarative JSON configuration file for all server topology and tool filtering rather than requiring separate configuration files per server or environment variables for each setting, enabling centralized management of complex multi-server setups
vs alternatives: Provides a single source of truth for MCP server configuration compared to environment-variable-based approaches which scatter configuration across multiple variables, or code-based configuration which requires recompilation
Automatically discovers available tools from each connected backend MCP server by querying their tool schemas at startup. The discovery process retrieves tool names, descriptions, input schemas, and other metadata from each backend, aggregates them with server-based prefixes and name sanitization, and presents the unified tool set to clients. This eliminates the need for manual tool registration or configuration while maintaining accurate tool metadata for client-side tool selection and parameter validation.
Unique: Performs automatic tool discovery at aggregator startup by querying backend MCP servers rather than requiring manual tool registration or maintaining a separate tool registry, enabling zero-configuration tool exposure
vs alternatives: Eliminates manual tool registration overhead compared to systems requiring explicit tool configuration, and provides accurate tool schemas directly from backends rather than relying on cached or manually-maintained metadata
+3 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 MCP Aggregator at 26/100.
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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