| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables users to discover, register, and establish connections to MCP (Model Context Protocol) tool servers through a CLI interface. The CLI maintains a registry of available MCP servers and handles the connection lifecycle, including authentication negotiation and protocol handshaking. Supports both local and remote server endpoints with configurable transport layers (stdio, HTTP, WebSocket).
Unique: Provides CLI-first MCP server management with support for multiple transport protocols (stdio, HTTP, WebSocket) in a single unified interface, rather than requiring separate client libraries per transport type
vs alternatives: Simpler than building custom MCP clients for each tool server; more flexible than hardcoded tool integrations because it leverages the standardized MCP protocol
Parses and displays the schema of available tools exposed by connected MCP servers, including input parameters, output types, and tool descriptions. Uses JSON schema introspection to extract tool metadata and presents it in human-readable format. Enables developers to understand tool capabilities before execution without consulting external documentation.
Unique: Provides real-time schema introspection directly from the MCP server rather than relying on static documentation, ensuring schema accuracy matches the live server implementation
vs alternatives: More accurate than reading docs because it queries live server state; faster than API exploration tools because it's optimized for CLI output
Executes tools on connected MCP servers by accepting CLI arguments, mapping them to tool parameters via schema validation, and returning structured results. Implements argument parsing with type coercion (string to number, boolean, JSON object) and validates inputs against the tool's JSON schema before transmission. Handles both synchronous and asynchronous tool execution with timeout management.
Unique: Implements client-side schema validation with automatic type coercion before tool invocation, reducing round-trips to the server and providing immediate feedback on parameter errors
vs alternatives: Faster iteration than raw HTTP calls because validation happens locally; more ergonomic than manual curl commands because it handles schema mapping automatically
Stores and loads MCP server connection profiles from configuration files (JSON/YAML), enabling users to define named server configurations with connection parameters, authentication details, and default tool settings. Supports environment variable interpolation for sensitive credentials and profile switching via CLI flags. Configuration is persisted locally and can be version-controlled.
Unique: Supports environment variable interpolation in configuration files, allowing credentials to be injected at runtime without storing them in version-controlled config
vs alternatives: More flexible than hardcoded server URLs because profiles can be switched per invocation; more secure than embedding credentials in config because it supports env var injection
Executes multiple tools sequentially or in parallel from a batch configuration file, aggregating results into a single output. Supports defining tool chains where output from one tool feeds into the next, with error handling and conditional execution based on previous results. Results are collected and formatted as JSON or CSV for downstream processing.
Unique: Supports declarative tool chaining via configuration files with automatic result passing between steps, enabling non-programmers to define complex tool workflows
vs alternatives: More accessible than writing custom orchestration code because workflows are defined declaratively; more efficient than sequential CLI invocations because it maintains server connection across steps
Transforms tool execution results into multiple output formats (JSON, YAML, CSV, plain text, markdown) with customizable field selection and filtering. Supports piping results to external tools via stdout and writing to files with automatic format detection. Includes pretty-printing for terminal display and compact formatting for machine consumption.
Unique: Provides multiple output formats from a single tool execution result, enabling seamless integration with downstream tools and data pipelines without requiring separate transformation steps
vs alternatives: More convenient than piping through jq or other JSON processors because format conversion is built-in; supports more formats than generic tools because it understands MCP tool result structure
Provides an interactive read-eval-print loop where users can execute tools, inspect results, and chain operations without exiting the session. Maintains connection state across multiple tool invocations, supports command history with readline, and provides autocomplete for tool names and parameters. Results from previous commands are accessible as variables for use in subsequent commands.
Unique: Maintains persistent connection and state across multiple tool invocations in a single REPL session, enabling rapid iteration and result chaining without connection overhead
vs alternatives: More efficient than repeated CLI invocations because it avoids connection setup overhead; more interactive than batch mode because results are immediately visible and can inform next steps
Provides detailed error messages, stack traces, and debugging information when tool execution fails. Supports verbose logging modes that expose MCP protocol messages, parameter validation errors, and server-side error details. Includes error recovery suggestions and links to relevant documentation. Errors are structured as JSON for programmatic handling in scripts.
Unique: Provides structured error output in JSON format alongside human-readable messages, enabling both interactive debugging and programmatic error handling in scripts
vs alternatives: More informative than generic error codes because it includes MCP protocol details and recovery suggestions; more actionable than raw server errors because it contextualizes failures
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 opentool-cli at 27/100.
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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
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