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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Terry CLI commands as MCP tools through a standardized Model Context Protocol server interface, enabling LLM clients to discover and invoke Terry operations without direct shell access. Implements MCP tool schema generation that maps CLI arguments to structured function parameters, allowing Claude and other MCP-compatible clients to call Terry commands with type-safe argument passing and response handling.
Unique: Bridges Terry CLI (a specific domain tool) into the MCP ecosystem by wrapping CLI invocations as discoverable, schema-validated tools that LLM clients can call with structured parameters rather than raw shell commands
vs alternatives: Provides type-safe tool calling for Terry workflows compared to naive shell execution, while maintaining full compatibility with the MCP standard that Claude and other clients already support
Automatically generates MCP-compliant tool schemas by introspecting Terry CLI's command structure, argument definitions, and help text. Converts CLI flags, options, and positional arguments into JSON Schema definitions with proper type constraints, descriptions, and required field markers, enabling clients to validate inputs before execution and provide intelligent autocomplete.
Unique: Implements CLI-to-schema mapping that extracts argument definitions from Terry's help output and converts them into JSON Schema with proper type inference, rather than requiring manual schema definition per command
vs alternatives: Reduces boilerplate compared to manually defining MCP tool schemas for each CLI command, while maintaining compatibility with standard JSON Schema validation that MCP clients expect
Implements the MCP server-side protocol handler using Node.js stdio streams, establishing bidirectional JSON-RPC communication with MCP clients (like Claude). Handles message framing, request routing, and response serialization according to the MCP specification, allowing clients to send tool invocation requests and receive results through standard input/output channels.
Unique: Implements MCP server protocol handling over Node.js stdio streams with proper JSON-RPC framing, enabling seamless integration with Claude Desktop and other MCP clients without requiring HTTP infrastructure
vs alternatives: Simpler deployment than HTTP-based MCP servers (no port management, firewall rules, or TLS certificates needed), while maintaining full MCP protocol compliance for client compatibility
Executes Terry CLI commands as child processes and captures stdout/stderr output, returning results to MCP clients with proper exit code handling and error propagation. Uses Node.js child_process module to spawn Terry with arguments derived from MCP tool invocation parameters, managing process lifecycle and timeout behavior.
Unique: Wraps Terry CLI execution in a child process with structured output capture and error handling, mapping MCP tool parameters directly to CLI arguments without shell interpretation
vs alternatives: Safer than shell execution (no injection vulnerabilities) and more reliable than direct library calls, while maintaining full compatibility with Terry's CLI interface
Manages the MCP server process lifecycle including initialization, client connection handling, and graceful shutdown. Implements proper signal handling for SIGTERM/SIGINT to clean up resources, manages the stdio transport connection, and ensures the server remains responsive to client requests throughout its lifetime.
Unique: Implements MCP server lifecycle with proper signal handling and resource cleanup, ensuring the server can be safely started/stopped by parent applications like Claude Desktop without leaving orphaned processes
vs alternatives: More robust than naive process spawning by handling OS signals and cleanup, while remaining lightweight compared to full application servers
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 terry-mcp at 20/100.
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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