| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Claude's extended thinking capability through the Model Context Protocol (MCP) as a callable tool, allowing Claude Desktop to invoke structured reasoning sessions without direct API calls. Implements MCP server specification to register a 'think' tool that Claude can call during conversation, routing thinking requests through the Anthropic API with budget_tokens parameter to control reasoning depth.
Unique: First-party MCP wrapper for Anthropic's extended thinking API, enabling Claude Desktop users to access thinking capability as a native tool without SDK integration or custom client code. Uses MCP's resource and tool registration patterns to expose thinking as a first-class citizen in Claude's tool ecosystem.
vs alternatives: Simpler than building custom Claude Desktop plugins or using raw API calls, and more integrated than copy-pasting thinking prompts manually into Claude
Implements the full MCP server lifecycle including initialization, tool registration, request handling, and graceful shutdown. Manages the bidirectional JSON-RPC communication channel between Claude Desktop and the think tool server, handling protocol versioning, capability negotiation, and error propagation according to the MCP specification.
Unique: Minimal, focused MCP server implementation that handles only the think tool without extraneous features, reducing attack surface and startup latency. Uses Node.js streams for efficient bidirectional communication with Claude Desktop.
vs alternatives: Lighter weight than building a full MCP framework or using generic server templates, with less boilerplate than implementing MCP from scratch
Allows configuration of the budget_tokens parameter sent to the Anthropic API, controlling the maximum number of tokens Claude can spend in the thinking phase. Implemented as a server-level setting (likely environment variable or config file) that applies uniformly to all thinking requests, enabling operators to trade off reasoning depth against API cost and latency.
Unique: Exposes Anthropic's budget_tokens parameter as a configurable server setting, enabling operators to enforce cost and latency constraints at the MCP layer rather than requiring API-level controls or custom client logic.
vs alternatives: More flexible than hard-coded thinking budgets, but less granular than per-request budget negotiation or dynamic budget allocation based on task complexity
Wraps the Anthropic API client to invoke extended thinking on specified Claude models (claude-3-7-sonnet, claude-3-5-sonnet, or later). Handles API authentication via ANTHROPIC_API_KEY environment variable, manages request/response serialization, and propagates API errors back to Claude Desktop with human-readable messages.
Unique: Centralizes Anthropic API authentication and model selection at the MCP server level, allowing Claude Desktop users to leverage extended thinking without managing API keys directly. Supports model version selection to enable gradual migration as new Claude versions are released.
vs alternatives: Simpler than embedding API keys in Claude Desktop config, and more maintainable than requiring users to manage API credentials in multiple places
Captures the thinking process and final response from the Anthropic API and formats them for display in Claude Desktop. Likely streams thinking tokens as they arrive (if API supports streaming) or batches them into readable chunks, preserving the structure of Claude's reasoning while making it human-readable in the chat interface.
Unique: Bridges Anthropic's extended thinking API output format with Claude Desktop's UI expectations, handling the translation from raw API response to user-facing reasoning display without requiring custom client modifications.
vs alternatives: More integrated than raw API output, and more transparent than hiding thinking details from the user
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 @cgize/mcp-think-tool at 22/100. @cgize/mcp-think-tool leads on ecosystem, while Supabase is stronger on quality.
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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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