| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Creates and manages cryptocurrency wallets across multiple blockchains through a standardized MCP tool interface that abstracts blockchain-specific wallet creation logic. The system maintains wallet metadata (name, type, blockchain) in a unified data model and exposes create_wallet, get_all_wallets, archive_wallets tools that translate AI agent requests into Armor API calls, handling authentication via API keys and returning structured wallet objects with balances and addresses.
Unique: Exposes wallet management as MCP tools callable by AI agents, abstracting Armor API authentication and blockchain-specific wallet creation into a schema-based function registry that works natively with Claude, Cline, and n8n without custom integration code
vs alternatives: Simpler than building direct blockchain RPC integrations because it delegates key management to Armor's backend and provides a unified interface across planned multi-chain support, whereas alternatives like ethers.js or Solana Web3.js require per-chain implementation
Executes token swaps on supported blockchains by fetching real-time conversion quotes and submitting signed transactions through the Armor API. The system accepts source token, destination token, and amount parameters, queries current market rates via get_swap_quote, and then executes the swap via execute_swap, handling slippage tolerance, gas estimation, and transaction signing server-side through Armor's custody infrastructure.
Unique: Separates quote fetching from execution as distinct MCP tools, allowing AI agents to inspect conversion rates before committing transactions, and delegates transaction signing to Armor's backend rather than exposing private keys to the agent layer
vs alternatives: More secure than direct DEX integrations like 1inch API because private keys never leave Armor's custody, and simpler than building custom quote aggregation because Armor handles liquidity routing internally
Enables AI agents to transfer tokens across different blockchains through a unified bridging interface that abstracts bridge protocol selection and execution. The system exposes bridge_token and get_bridge_quote tools that query available bridge routes, estimate fees and execution times, and submit cross-chain transfer transactions, handling bridge protocol integration (e.g., Wormhole, Stargate) server-side.
Unique: Abstracts bridge protocol selection and execution into a single MCP tool, allowing agents to bridge tokens without understanding Wormhole, Stargate, or other bridge mechanics, and handles bridge route optimization server-side
vs alternatives: Simpler than direct bridge protocol integration because Armor selects optimal routes and handles protocol-specific transaction construction, and more reliable than manual bridge usage because execution is managed server-side with retry logic
Enables AI agents to create recurring token purchase orders that execute at fixed intervals with fixed amounts, abstracting the complexity of scheduling and transaction batching. The system exposes create_dca_order, list_dca_orders, and cancel_dca_order tools that store DCA configuration (token pair, amount, frequency, start/end dates) in Armor's backend and trigger automatic swaps on a schedule, handling gas optimization and order state management.
Unique: Implements DCA as a server-side scheduled task managed by Armor backend rather than requiring the AI agent to maintain scheduling state, eliminating the need for persistent cron jobs or external schedulers in the agent layer
vs alternatives: More reliable than agent-side scheduling because execution is guaranteed by Armor's infrastructure even if the AI agent disconnects, and simpler than building custom scheduling logic because frequency and execution are handled server-side
Allows AI agents to place conditional orders that execute automatically when market prices reach specified thresholds, without requiring the agent to monitor prices continuously. The system exposes create_limit_order and create_stop_order tools that store price conditions in Armor's backend and trigger swaps when conditions are met, handling price feed integration, order state transitions, and partial fill scenarios.
Unique: Implements conditional order execution server-side using Armor's price feed infrastructure, eliminating the need for agents to poll price data or maintain order state, and supporting complex order types (limit, stop) without custom agent logic
vs alternatives: More efficient than agent-side price monitoring because Armor's backend handles continuous price checking, and more reliable than manual order placement because conditions are evaluated server-side with guaranteed execution when triggered
Enables AI agents to stake tokens on supported blockchains and track staking rewards through a unified interface that abstracts blockchain-specific staking mechanics. The system exposes stake_token, unstake_token, and get_staking_balance tools that submit staking transactions, manage validator selection, and return staking position data including APY, earned rewards, and unstaking timelines.
Unique: Abstracts blockchain-specific staking mechanics (validator selection, unbonding periods, reward calculation) into a unified MCP tool interface, allowing agents to stake without understanding per-chain staking protocols
vs alternatives: Simpler than direct blockchain staking because Armor handles validator selection and reward tracking, and more secure than agent-managed staking because private keys remain in Armor's custody
Provides AI agents with real-time and historical token data including prices, market caps, trading volumes, and trending tokens through a data retrieval interface. The system exposes get_token_info, get_trending_tokens, and search_tokens tools that query Armor's token database and external price feeds, returning structured token metadata and market statistics without requiring agents to integrate multiple data sources.
Unique: Aggregates token metadata and price data from multiple sources into a single MCP tool interface, eliminating the need for agents to integrate separate price feed APIs (CoinGecko, Chainlink, etc.) and manage data freshness
vs alternatives: More convenient than direct price feed APIs because it provides a unified schema across tokens, and more reliable than web scraping because data is sourced from official APIs and cached server-side
Enables AI agents to organize wallets into logical groups and perform batch operations across multiple wallets simultaneously, reducing the complexity of managing multi-wallet portfolios. The system exposes create_group, add_wallet_to_group, and list_group_wallets tools that maintain group metadata and enable batch queries (e.g., total balance across a group, aggregate staking positions) without requiring agents to iterate through individual wallets.
Unique: Implements wallet grouping as a server-side organizational primitive with aggregate query support, allowing agents to reason about wallet cohorts without maintaining group state locally
vs alternatives: More efficient than agent-side wallet tracking because aggregate queries are computed server-side, and more scalable than individual wallet queries because batch operations reduce API call overhead
+3 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 Armor Crypto MCP at 26/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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