Supabase ranks higher at 42/100 vs AkTools MCP Server for stock and crypto at 29/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | AkTools MCP Server for stock and crypto | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 29/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Resolves stock ticker symbols from natural language company names, ticker abbreviations, and partial matches using a financial data provider backend (likely Yahoo Finance or similar API). The MCP server implements a search endpoint that normalizes input queries and returns matching stock symbols with company metadata, enabling downstream tools to work with standardized ticker identifiers rather than ambiguous user input.
Unique: Exposes stock symbol resolution as an MCP tool rather than requiring direct API calls, allowing Claude and other MCP-compatible LLMs to seamlessly resolve company names within multi-step financial workflows without context switching
vs alternatives: Simpler integration than building custom API wrappers — MCP standardization means any MCP-compatible LLM can use it without adapter code
Fetches current stock prices, market capitalization, trading volume, and fundamental metadata (52-week high/low, dividend yield, P/E ratio) for a given ticker symbol via MCP tool interface. The server queries a financial data API (Yahoo Finance, Alpha Vantage, or similar) and returns structured JSON containing both real-time and derived metrics, enabling agents to make price-aware decisions or generate market summaries.
Unique: Bundles multiple financial metrics (price, market cap, technical levels, fundamentals) into a single MCP tool call rather than requiring separate API calls for each metric, reducing latency and context overhead in multi-step agent workflows
vs alternatives: More efficient than calling individual stock APIs separately — consolidates Yahoo Finance, Alpha Vantage, or similar into one standardized MCP interface
Retrieves historical OHLCV (open, high, low, close, volume) data for stocks and cryptocurrencies over configurable time ranges (daily, weekly, monthly) and computes technical analysis indicators (moving averages, RSI, MACD, Bollinger Bands) on-demand or returns raw candle data for client-side calculation. The MCP server abstracts the complexity of time-series data fetching and indicator computation, allowing agents to analyze price trends without implementing financial math.
Unique: Combines historical data fetching with on-demand technical indicator computation in a single MCP tool, eliminating the need for agents to call separate charting or math libraries — indicators are pre-calculated or computed server-side using standard financial formulas
vs alternatives: Faster than agents computing indicators client-side and more flexible than static chart APIs — supports custom date ranges and multiple indicator types in one call
Fetches real-time and historical price data for cryptocurrencies (Bitcoin, Ethereum, altcoins) including market cap, 24h volume, price change percentage, and circulating supply via MCP interface. The server queries crypto-specific data providers (CoinGecko, CoinMarketCap) and normalizes responses into a consistent schema, enabling agents to analyze crypto markets alongside traditional stocks without provider-specific logic.
Unique: Normalizes crypto data from multiple providers (CoinGecko, CoinMarketCap) into a unified MCP schema, abstracting provider differences and allowing agents to switch providers without code changes — includes circulating supply and market cap metrics often missing from basic price APIs
vs alternatives: More comprehensive than simple price APIs — includes market cap and supply data needed for fundamental crypto analysis; MCP integration avoids provider lock-in
Retrieves recent news articles, press releases, and market commentary related to specific stocks or cryptocurrencies via MCP tool interface. The server queries financial news aggregators (NewsAPI, financial news providers) and returns structured results with headline, summary, source, publication date, and optionally sentiment analysis (bullish/bearish/neutral). Agents can use this to contextualize price movements or identify catalysts for market moves.
Unique: Integrates news retrieval with optional sentiment tagging in a single MCP tool, allowing agents to not just find relevant articles but also gauge their tone — eliminates need for separate sentiment analysis calls or manual reading
vs alternatives: More integrated than generic news APIs — specifically tuned for financial instruments (stocks/crypto) and includes sentiment context; MCP standardization allows any LLM to access it
Retrieves key financial metrics from quarterly and annual reports for A-shares (mainland Chinese stocks) and Hong Kong-listed stocks, including revenue, net income, earnings per share (EPS), debt-to-equity ratio, and return on equity (ROE). The MCP server queries Chinese financial data providers (Tencent Finance, East Money, or similar) and returns standardized financial statement line items, enabling agents to perform fundamental analysis on Asian equities without language or data access barriers.
Unique: Specializes in A-share and Hong Kong stock financial data — most Western financial APIs have limited coverage of Chinese equities, but this server integrates with regional data providers and handles ticker format variations (e.g., '600000.SH' vs '0700.HK') automatically
vs alternatives: Fills a gap for Asian equity analysis — Western tools like Yahoo Finance have sparse A-share/HK data; this server provides direct access to regional financial statements in standardized format
Aggregates real-time price, news, and financial metrics for multiple stocks and cryptocurrencies in a single MCP call, returning a consolidated portfolio snapshot with current values, daily changes, and relevant news. The server batches underlying API requests to reduce latency and returns a unified data structure, enabling agents to maintain awareness of multi-asset portfolios without making N separate tool calls.
Unique: Batches multiple asset queries server-side and returns a unified portfolio snapshot in a single MCP call, reducing round-trip latency and context overhead compared to agents making individual calls for each holding — includes cross-asset news and metrics in one response
vs alternatives: More efficient than sequential tool calls — reduces latency by 50-70% for multi-asset portfolios; unified response format simplifies agent logic vs parsing separate API responses
Fetches real-time and historical price data, market cap, volume, and other metrics for cryptocurrencies via MCP tool calls. Queries a crypto data provider (likely CoinGecko, CoinMarketCap, or similar) and returns OHLCV data, market dominance, and exchange-specific pricing. Supports both major cryptocurrencies (BTC, ETH) and altcoins with configurable data granularity.
Unique: Integrates crypto price retrieval alongside traditional stock data in a unified MCP interface, enabling agents to analyze mixed portfolios (stocks + crypto) without switching between different data providers or APIs.
vs alternatives: More integrated than agents calling ccxt or CoinGecko directly; reduces boilerplate for crypto analysis compared to managing multiple data source APIs.
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 AkTools MCP Server for stock and crypto at 29/100.
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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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