| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Establishes and manages connections to MCP servers across three transport protocols (STDIO, SSE, Streamable HTTP) with automatic server discovery. The ServerConnector component handles protocol negotiation, session management, and transport-specific serialization/deserialization, enabling seamless integration with heterogeneous MCP server implementations without requiring manual transport configuration.
Unique: Implements a unified ServerConnector abstraction that handles all three MCP 1.10.1 transport types with automatic protocol detection and fallback logic, eliminating the need for users to manually specify transport types — the system infers the correct transport from server configuration and connection behavior.
vs alternatives: Supports all three MCP transports in a single client unlike most MCP clients which focus on single-transport implementations, enabling broader server ecosystem compatibility.
Orchestrates tool invocation through a ToolManager that enables/disables tools, formats tool calls from LLM responses, executes them against MCP servers, and presents results to the user with optional approval gates. The system parses LLM-generated tool calls, validates them against available tool schemas, executes them via MCP protocol, and streams results back into the conversation context with human-in-the-loop checkpoints for safety-critical operations.
Unique: Implements a ToolManager with explicit approval gates that pause execution before tool invocation, allowing users to review and approve/reject each tool call — this is distinct from cloud-based LLM APIs which execute tools server-side without user visibility or control.
vs alternatives: Provides local tool execution with human-in-the-loop safety controls unlike Copilot or Claude API which execute tools server-side, giving users full visibility and veto power over tool invocation.
Automatically discovers and introspects MCP server capabilities including available tools, resources, and prompts with their full schema definitions. When connecting to an MCP server, the client queries the server's capabilities, parses tool schemas (including parameters, descriptions, and constraints), and makes this information available for tool selection, validation, and autocomplete. The system maintains an index of all discovered tools and their schemas for runtime validation.
Unique: Implements automatic server capability discovery that introspects tool schemas and maintains an indexed registry of all available tools from connected servers, enabling schema-based validation and autocomplete — most MCP clients require manual tool definition or static configuration.
vs alternatives: Provides automatic tool discovery and schema introspection unlike static MCP clients, enabling dynamic tool availability and validation without manual configuration.
Maintains conversation history and intelligently injects tool execution results back into the context for the LLM to process. The system tracks all user messages, LLM responses, and tool calls/results in a structured conversation object, formats tool results appropriately for LLM consumption, and includes relevant context in subsequent requests. This enables multi-turn conversations where the LLM can reason about tool results and take follow-up actions.
Unique: Implements intelligent context management that tracks conversation history and injects tool results back into context for LLM processing, enabling multi-turn reasoning where the LLM can refine results based on tool execution outcomes — most MCP clients treat tool execution as isolated operations.
vs alternatives: Provides conversation-aware tool result injection unlike stateless MCP clients, enabling multi-turn workflows where the LLM can reason about tool results and take follow-up actions.
Runs entirely locally using Ollama for LLM inference and local MCP servers, with no requirement for cloud API calls or external services. All model inference, tool execution, and data processing happens on the user's machine, providing privacy, offline capability, and cost savings. The system is designed for air-gapped environments and provides full functionality without internet connectivity.
Unique: Implements a completely local-first architecture using Ollama for inference and local MCP servers for tools, with zero cloud dependencies — this is fundamentally different from cloud-based LLM clients which require API keys and internet connectivity.
vs alternatives: Provides complete local execution unlike cloud-based LLM clients, enabling offline use, full privacy, and cost savings while maintaining full tool-use capability through local MCP servers.
The StreamingManager processes MCP server responses and Ollama model outputs in real-time, handling token-by-token streaming from both sources with metrics collection and formatted output. It manages SSE streams from MCP servers, processes Ollama's streaming API responses, buffers partial tokens, and renders them to the terminal with latency tracking and throughput metrics.
Unique: Implements a unified StreamingManager that handles both Ollama model streaming and MCP server SSE streams with synchronized metrics collection, allowing users to see real-time performance data alongside response generation — most MCP clients buffer responses entirely before display.
vs alternatives: Provides real-time token streaming with integrated performance metrics unlike traditional MCP clients which buffer entire responses, enabling better user feedback and performance visibility.
The ModelManager abstracts Ollama model selection, parameter configuration (temperature, top_p, top_k, etc.), and request formatting. It maintains model state, validates parameter ranges, constructs properly-formatted Ollama API requests, and handles model switching without losing conversation context. The manager translates user-friendly parameter names to Ollama API fields and enforces model-specific constraints.
Unique: Implements a ModelManager that maintains model state across the session and provides client-side parameter validation with human-readable error messages, preventing invalid requests from reaching Ollama — most MCP clients pass parameters directly without validation.
vs alternatives: Provides model parameter validation and switching without session loss unlike raw Ollama API clients which require manual request construction and don't maintain conversation context across model changes.
The ConfigManager handles saving and loading client configurations including server definitions, model preferences, tool selections, and custom system prompts. It persists state to ~/.mcp/config.json and supports multiple configuration profiles, enabling users to save different setups (e.g., 'creative-writing', 'code-generation') and switch between them. The manager handles defaults, migration, and validation of configuration files.
Unique: Implements a ConfigManager with profile-based persistence that allows users to save and switch between multiple named configurations (e.g., 'research', 'coding', 'writing'), enabling rapid context switching between different MCP server and model setups without manual reconfiguration.
vs alternatives: Provides multi-profile configuration management unlike stateless MCP clients, allowing users to save and restore complete session setups including servers, models, and tools.
+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 mcp-client-for-ollama at 41/100.
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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