| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Aggregates real-time and historical cryptocurrency market data from multiple blockchain data providers (likely CoinGecko, Chainlink, or similar APIs) into a unified schema accessible via MCP tool calls. The MCP server normalizes heterogeneous data formats into consistent JSON structures, enabling AI assistants to query price, volume, market cap, and volatility metrics across 1000+ tokens without managing multiple API clients or authentication schemes.
Unique: MCP-native crypto data aggregation that normalizes multiple blockchain data sources into a single tool interface, eliminating the need for AI assistants to manage separate API clients or authentication for each data provider
vs alternatives: Simpler than building custom API wrappers for each data source; more unified than point-to-point integrations like direct CoinGecko API calls
Exposes DeFi protocol operations (swap, stake, lend, borrow) through MCP tool definitions that abstract away contract ABIs, gas estimation, and transaction signing complexity. The MCP server likely wraps Web3.py or ethers.js libraries, translating high-level intent (e.g., 'swap 1 ETH for USDC on Uniswap') into signed transactions ready for broadcast. Supports multiple chains and protocols through a plugin or adapter pattern.
Unique: MCP-based abstraction layer that translates natural language DeFi intents into executable smart contract interactions, hiding ABI complexity and gas mechanics from the AI agent while maintaining security through explicit transaction signing
vs alternatives: More accessible than raw ethers.js for LLMs; safer than direct contract interaction because it enforces parameter validation and slippage checks before signing
Provides infrastructure for deploying and managing the Hive Intelligence MCP server as a remote service accessible to multiple AI clients. Supports containerized deployment (Docker), environment configuration, and API key management through MCP-compatible interfaces. Enables teams to run a centralized crypto data and DeFi interaction service that multiple AI agents can connect to without duplicating server infrastructure.
Unique: MCP-native remote server deployment that enables centralized crypto data and DeFi interaction infrastructure, allowing multiple AI agents to share a single server instance with unified API key and rate limit management
vs alternatives: More scalable than per-agent server instances; simpler than building custom API gateways; enables team-wide governance of AI-driven blockchain interactions
Provides on-chain analytics tools that query blockchain state (wallet balances, transaction history, token holdings, gas usage patterns) and DeFi metrics (TVL, yield rates, liquidation risks) via MCP. Likely integrates with Etherscan, Dune Analytics, or similar indexing services to retrieve historical and real-time blockchain data without requiring full node infrastructure. Supports address-level tracking and portfolio composition analysis.
Unique: MCP-native on-chain analytics that aggregates wallet and protocol data from multiple indexers into a unified query interface, enabling AI agents to perform complex portfolio analysis without managing separate Etherscan, Dune, or Flipside accounts
vs alternatives: More comprehensive than single-source indexers; faster than querying raw blockchain nodes; more accessible than building custom subgraphs
Resolves Ethereum Name Service (ENS) domains and Web3 identity data (avatar, social links, verified credentials) through MCP tool calls. Integrates with ENS smart contracts and IPFS to translate human-readable names (e.g., 'vitalik.eth') into wallet addresses and retrieve associated metadata. Supports reverse resolution (address to ENS name) and identity verification through decentralized identity protocols.
Unique: MCP-based ENS and Web3 identity resolver that combines smart contract queries with IPFS metadata retrieval, enabling AI agents to perform bidirectional address-to-identity mapping with social verification
vs alternatives: More integrated than separate ENS and identity lookups; faster than manual IPFS gateway queries; supports identity verification that raw address lookups cannot provide
Routes token swaps and bridges across multiple blockchain networks (Ethereum, Polygon, Arbitrum, Optimism, Solana, etc.) by querying liquidity aggregators and bridge protocols. The MCP server abstracts away the complexity of selecting optimal routes, handling wrapped token conversions, and managing cross-chain state. Likely uses 1inch, Uniswap, or similar aggregators to find best execution prices across chains and bridges.
Unique: MCP-based cross-chain routing engine that aggregates liquidity and bridge data across EVM and non-EVM chains, enabling AI agents to find and execute optimal multi-chain swaps without managing separate bridge and DEX APIs
vs alternatives: More comprehensive than single-chain DEX aggregators; faster than manual bridge selection; supports non-EVM chains unlike most Ethereum-centric tools
Retrieves and analyzes NFT metadata, collection statistics, and market data through MCP tool calls. Integrates with NFT indexers (OpenSea API, Reservoir, or similar) to fetch floor prices, trading volume, rarity scores, and ownership data. Supports batch queries for analyzing entire collections and identifying undervalued assets based on rarity or historical price trends.
Unique: MCP-based NFT analytics that combines metadata indexing with market data aggregation, enabling AI agents to perform rarity-aware valuation and detect market anomalies without managing separate OpenSea and Reservoir accounts
vs alternatives: More comprehensive than single-source NFT APIs; supports rarity analysis that raw metadata queries cannot provide; faster than manual collection analysis
Simulates transactions before execution to estimate gas costs, detect reverts, and optimize execution parameters. The MCP server uses Tenderly, Ethersim, or similar simulation services to execute transactions in a sandboxed environment, returning detailed gas breakdowns and revert reasons. Enables AI agents to validate transactions and adjust parameters (slippage, gas price) before committing to the blockchain.
Unique: MCP-based transaction simulator that provides detailed gas breakdowns and revert detection, enabling AI agents to validate and optimize transactions before execution without risking funds
vs alternatives: More detailed than simple gas estimation; safer than executing untested transactions; faster than manual simulation via Etherscan
+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 Hive Intelligence at 26/100.
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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