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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables AI agents to initiate Bitcoin Lightning Network payments by exposing standardized MCP tool endpoints that translate agent requests into Lightning invoice creation and payment routing. The implementation wraps Alby's wallet API through MCP's tool-calling interface, allowing agents to specify payment amounts, recipients, and metadata which are then routed through the Lightning Network for near-instant settlement at minimal fees.
Unique: Directly exposes Lightning Network payment capability through MCP's standardized tool interface, allowing any MCP-compatible agent to transact without custom wallet SDKs or key management — the agent never handles private keys, only delegates payment requests to Alby's managed wallet service.
vs alternatives: Unlike REST API integrations that require agents to manage HTTP requests and error handling, MCP's tool-calling abstraction lets agents treat Lightning payments as native capabilities with automatic schema validation and structured error handling.
Generates Lightning Network invoices (BOLT11 format) that agents can embed in responses or share with users, enabling inbound payments to the Alby wallet. The capability accepts amount specifications, optional descriptions, and expiration parameters, then returns a scannable invoice string and corresponding LNURL that can be used by any Lightning-compatible wallet to pay the agent or service.
Unique: Wraps Alby's invoice generation API through MCP, allowing agents to programmatically create Lightning invoices without manual wallet interaction — invoices are generated on-demand and can be embedded directly in agent responses or shared via QR codes.
vs alternatives: More seamless than traditional payment gateways because invoices are generated instantly without third-party processing delays, and Lightning's native format means users can pay directly from any Lightning wallet without account creation.
Exposes read-only MCP tools that allow agents to query the connected Alby wallet's current balance (on-chain and Lightning), active channel states, liquidity availability, and transaction history. This capability enables agents to make informed decisions about payment feasibility before attempting transactions and to provide users with accurate wallet status information.
Unique: Provides agents with direct read access to Alby wallet state through MCP tools, enabling conditional payment logic based on real-time balance and liquidity — agents can query before attempting payments and adjust behavior based on available funds.
vs alternatives: Unlike webhook-based balance notifications, MCP tool queries are synchronous and agent-initiated, allowing agents to proactively check state before making decisions rather than reacting to asynchronous events.
Resolves Lightning addresses (e.g., user@domain.com) and LNURL endpoints to extract payment routing information, enabling agents to validate recipient addresses before initiating payments. The capability handles the LNURL protocol's metadata exchange, verifies recipient information, and returns routing details that can be used to construct payment requests with confidence.
Unique: Implements LNURL protocol resolution as an MCP tool, allowing agents to validate and resolve Lightning addresses without manual parsing — handles the full LNURL metadata exchange and returns structured recipient information.
vs alternatives: More robust than simple string parsing because it validates addresses against actual LNURL servers and retrieves metadata, preventing agents from attempting payments to invalid or incompatible recipients.
Provides MCP tools to query the status of previously initiated payments, including confirmation state, routing details, and failure reasons. Agents can poll payment status to determine if transactions have settled, enabling workflows that depend on payment confirmation before proceeding to next steps.
Unique: Exposes payment status as queryable MCP tools, enabling agents to implement confirmation-dependent workflows without external state management — agents can poll status and make decisions based on confirmation state.
vs alternatives: More agent-native than webhook-based confirmations because agents can synchronously query status within their decision logic, though less efficient than event-based notifications for high-volume payment tracking.
Abstracts Alby wallet operations behind a standardized MCP interface that could theoretically support multiple Lightning wallet providers (though currently Alby-focused). The abstraction allows agents to interact with Lightning payments through a consistent tool schema regardless of underlying wallet implementation, enabling potential future support for other providers like LND, Breez, or Eclair.
Unique: Designs MCP tool schemas to be provider-agnostic, allowing potential future implementation of multiple Lightning wallet backends without changing agent code — currently Alby-only but architecturally extensible.
vs alternatives: More flexible than wallet-specific SDKs because the MCP abstraction layer could support multiple providers, though currently only Alby is implemented and multi-provider support would require additional development.
Provides structured error responses and recovery guidance when payments fail, including specific failure reasons (insufficient balance, channel saturation, routing failure, timeout) and suggested remediation steps. Agents can parse these errors to implement intelligent retry logic, fallback payment methods, or user-facing error messages.
Unique: Structures payment failure responses with categorized error codes and recovery guidance, enabling agents to implement intelligent error handling rather than treating all failures identically — agents can distinguish between temporary routing failures and permanent balance issues.
vs alternatives: More informative than generic API errors because failure responses include specific categorization and suggested remediation, allowing agents to make smarter decisions about retries and fallbacks.
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 Alby Bitcoin Payments MCP at 24/100.
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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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