{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-alpacahq-alpaca-mcp-server","slug":"mcp-alpacahq-alpaca-mcp-server","name":"alpaca-mcp-server","type":"mcp","url":"https://github.com/alpacahq/alpaca-mcp-server","page_url":"https://unfragile.ai/mcp-alpacahq-alpaca-mcp-server","categories":["mcp-servers"],"tags":["ai-trading","algorithmic-trading","crypto","etfs","mcp","mcp-server","options","stocks","trading","trading-api"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_0","uri":"capability://tool.use.integration.natural.language.to.trading.api.translation","name":"natural-language-to-trading-api-translation","description":"Translates conversational natural language requests into structured Alpaca API calls through a FastMCP-based protocol bridge. The server implements a request processing pipeline that parses LLM-generated text, maps it to 44+ registered tools, and executes corresponding Alpaca API operations with automatic parameter extraction and type coercion. This enables users to execute complex trading operations (orders, position management, data queries) by describing intent in plain English without learning API syntax.","intents":["Execute stock/ETF/crypto/options trades by describing the trade in natural language","Query account status, positions, and portfolio metrics using conversational requests","Retrieve market data and historical quotes by asking for specific symbols and timeframes","Build and test trading strategies by describing logic in English and having it executed"],"best_for":["Traders and quantitative analysts who want to interact with markets through LLM chat interfaces","Non-technical traders building algorithmic strategies without coding","Developers building LLM-powered trading agents and copilots"],"limitations":["Parameter extraction relies on LLM interpretation — ambiguous requests may be misinterpreted (e.g., 'buy some Apple' could mean unclear quantity)","No built-in validation of trading intent — dangerous orders (e.g., liquidating entire portfolio) can be executed if LLM generates them","Latency overhead from MCP protocol serialization and Alpaca API round-trips adds 500ms-2s per operation","Limited to Alpaca's supported asset classes and order types — no support for complex derivatives or multi-leg strategies"],"requires":["Python 3.10+","Alpaca API keys (paper or live trading)","MCP-compatible LLM client (Claude Desktop, Cursor IDE, VSCode with MCP extension)","Network connectivity to Alpaca API endpoints"],"input_types":["natural language text (conversational requests)","structured parameters extracted from LLM context"],"output_types":["API response objects (orders, positions, account data)","formatted text summaries for LLM consumption","structured JSON with trade confirmations and execution details"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_1","uri":"capability://automation.workflow.paper.trading.mode.isolation","name":"paper-trading-mode-isolation","description":"Provides environment-variable-controlled switching between paper trading (PAPER=True, default) and live trading (PAPER=False) modes that route all TradingClient operations to separate Alpaca API endpoints with distinct credential sets. The server initializes the appropriate API endpoint URL and authentication context at startup based on the PAPER flag, ensuring all subsequent order and position operations target the correct trading environment without code changes. This enables safe testing and development before risking real capital.","intents":["Test trading strategies and order logic in a sandbox environment without financial risk","Develop and debug trading agents using simulated market conditions and paper account balances","Validate MCP tool integration and LLM-to-API translation before enabling live trading","Switch between paper and live trading by changing a single environment variable"],"best_for":["Developers building trading agents who need safe iteration and testing","Traders prototyping new strategies before committing real capital","Teams deploying trading systems that require staging and production environments"],"limitations":["Paper trading fills are simulated and may not reflect real market microstructure, slippage, or liquidity constraints","No automatic safeguards prevent accidental live trading if PAPER=False is set — requires explicit environment management","Paper account balances reset daily or on schedule; cannot persist multi-day strategy testing without manual intervention","Market data in paper mode may lag live data by seconds, affecting strategy validation accuracy"],"requires":["PAPER environment variable set in .env, Docker environment, or Claude Desktop config","Alpaca API keys valid for both paper and live trading (if switching modes)","Restart of MCP server process to change trading modes"],"input_types":["environment variable (PAPER=True or PAPER=False)"],"output_types":["API endpoint routing (paper.alpaca.markets vs api.alpaca.markets)","order confirmations and execution reports from selected environment"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_10","uri":"capability://automation.workflow.environment.configuration.and.credential.management","name":"environment-configuration-and-credential-management","description":"Supports flexible credential and configuration management through multiple sources: .env files in the project directory, environment variables, and Claude Desktop config (claude_desktop_config.json). The server reads configuration at startup and initializes API clients with the appropriate credentials and endpoints. Supported configuration variables include ALPACA_API_KEY, ALPACA_SECRET_KEY, PAPER (trading mode), and optional proxy settings. This enables users to configure the server without modifying code and supports multiple deployment scenarios (local, Docker, cloud).","intents":["Configure API credentials without hardcoding them in source code","Switch between paper and live trading by changing a single environment variable","Deploy the server to different environments (local, Docker, cloud) with environment-specific configuration","Manage multiple Alpaca accounts by running separate server instances with different credentials"],"best_for":["Developers deploying trading servers to multiple environments (dev, staging, production)","Teams managing API credentials securely without storing them in version control","Users running the server locally with .env files for easy configuration"],"limitations":["Credentials are read at server startup; changes require server restart","No built-in credential rotation or expiration management; API keys must be manually updated",".env files are plain text and vulnerable if not properly protected; should not be committed to version control","Claude Desktop config requires manual editing of JSON; no UI for configuration management"],"requires":["ALPACA_API_KEY and ALPACA_SECRET_KEY environment variables or .env file entries","Optional PAPER variable (defaults to True for paper trading)","Python-dotenv library for .env file support"],"input_types":["environment variables (ALPACA_API_KEY, ALPACA_SECRET_KEY, PAPER, etc.)",".env file with KEY=VALUE pairs","claude_desktop_config.json with environment section"],"output_types":["initialized API clients with correct credentials and endpoints","configuration validation messages (success or error)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_11","uri":"capability://automation.workflow.docker.containerization.and.deployment","name":"docker-containerization-and-deployment","description":"Provides a Dockerfile and Docker Compose configuration for containerizing the MCP server and deploying it in isolated environments. The Docker setup installs Python 3.10+, dependencies from requirements.txt, and runs the server as a container process. Docker environment variables can be passed at runtime to configure API credentials and trading mode. This enables deployment to cloud platforms (AWS, GCP, Azure), Kubernetes clusters, or local Docker environments without manual Python installation.","intents":["Deploy the MCP server to cloud platforms (AWS, GCP, Azure) using Docker containers","Run the server in isolated environments without affecting host system Python installation","Use Docker Compose for local development with multiple services (e.g., server + monitoring)","Scale the server horizontally by running multiple container instances"],"best_for":["DevOps teams deploying trading servers to cloud or on-premise infrastructure","Developers using Docker for local development and testing","Teams requiring containerized deployment for security or compliance reasons"],"limitations":["Docker adds overhead (container startup time, resource usage) vs. native Python execution","No built-in orchestration or scaling; requires external tools (Kubernetes, Docker Swarm) for multi-instance deployment","Docker image must be rebuilt and redeployed for code updates; no hot-reload capability","Persistent storage (logs, watchlists) requires volume mounts or external storage; container-local storage is ephemeral"],"requires":["Docker 20.10+ or Docker Desktop","Docker Compose 1.29+ (for Docker Compose deployment)","Dockerfile and docker-compose.yml in the repository","Environment variables passed at container runtime"],"input_types":["Dockerfile with Python 3.10+ base image","docker-compose.yml with service configuration","environment variables passed via -e flag or .env file"],"output_types":["running Docker container with MCP server listening on configured port","container logs with server startup and request handling messages"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_12","uri":"capability://tool.use.integration.tool.discovery.and.schema.documentation","name":"tool-discovery-and-schema-documentation","description":"Implements MCP tool discovery and schema documentation through the FastMCP framework, which automatically generates JSON schemas for all 44+ registered tools. Each tool includes a name, description, input schema (parameters with types and constraints), and output schema. MCP clients (Claude Desktop, Cursor, VSCode) use these schemas to discover available tools, validate parameters, and provide autocomplete suggestions. The server exposes tool metadata through the MCP protocol's tools/list and tools/describe endpoints.","intents":["Discover all available trading and market data tools in the MCP server","Understand tool parameters and constraints through schema documentation","Validate tool parameters before sending requests to avoid rejection errors","Enable IDE autocomplete and type checking for MCP tool calls"],"best_for":["LLM clients (Claude, Cursor) that need to discover and understand available tools","IDE extensions that provide autocomplete and parameter validation for MCP tools","Developers building custom MCP clients that need to introspect tool schemas"],"limitations":["Schema documentation is static and generated at server startup; changes require server restart","Complex parameter types (nested objects, unions) may not be fully captured in JSON schema","No built-in tool versioning or deprecation warnings; breaking changes require manual client updates","Schema validation is performed by the client; server does not enforce schema compliance"],"requires":["FastMCP framework with tool schema generation support","MCP client that supports tools/list and tools/describe endpoints"],"input_types":["MCP protocol request for tools/list or tools/describe"],"output_types":["JSON array of tool objects with name, description, input_schema, output_schema","individual tool schema with parameter types and constraints"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_2","uri":"capability://tool.use.integration.account.and.position.management.tools","name":"account-and-position-management-tools","description":"Exposes Alpaca TradingClient methods as MCP tools for querying and managing account state, including account details (cash, buying power, equity), position tracking (open positions, P&L, Greeks for options), and portfolio metrics. Each tool wraps a specific TradingClient method (e.g., get_account(), get_positions(), get_position(symbol)) and returns structured data formatted for LLM consumption. The server maintains no local state; all queries hit the live Alpaca API, ensuring real-time accuracy.","intents":["Check current account balance, buying power, and portfolio equity at any time","List all open positions with current market values and unrealized P&L","Query details of a specific position including Greeks for options positions","Monitor account status and trading restrictions in real-time"],"best_for":["Traders building LLM-powered portfolio dashboards and monitoring systems","Algorithmic trading agents that need real-time account state for position sizing decisions","Risk management systems that track portfolio metrics and enforce position limits"],"limitations":["Account queries return point-in-time snapshots; no built-in historical tracking or time-series data","Position P&L calculations are based on Alpaca's last-known market prices, which may lag real-time quotes by seconds","Options Greeks are computed by Alpaca's pricing model and may not match third-party models","No support for multi-account queries — each MCP server instance is tied to a single Alpaca account"],"requires":["Alpaca API keys with account read permissions","Active Alpaca trading account (paper or live)","Network connectivity to Alpaca API"],"input_types":["optional symbol parameter (for position-specific queries)","account ID (implicit from API key)"],"output_types":["structured JSON with account details (cash, equity, buying_power, etc.)","position arrays with symbol, quantity, entry_price, current_price, unrealized_pl","options Greeks (delta, gamma, theta, vega, rho) for options positions"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_3","uri":"capability://data.processing.analysis.market.data.query.and.historical.analysis","name":"market-data-query-and-historical-analysis","description":"Provides access to Alpaca's StockHistoricalDataClient for querying historical market data, including bars (OHLCV candles), quotes (bid/ask spreads), and latest prices across multiple timeframes (minute, hour, day, week, month). Tools accept symbol(s), date ranges, and timeframe parameters, returning structured arrays of price data suitable for technical analysis, backtesting, and strategy validation. The server supports batch queries for multiple symbols in a single request, reducing round-trips.","intents":["Retrieve historical price data (OHLCV) for a symbol across custom date ranges and timeframes","Fetch latest bid/ask quotes and spreads for liquidity analysis","Query multiple symbols' historical data in a single batch request","Analyze price trends and volatility using historical bars for strategy development"],"best_for":["Quantitative analysts building backtesting and strategy validation pipelines","Traders analyzing historical price action and technical indicators","LLM agents that need market context to make informed trading decisions"],"limitations":["Historical data is limited to Alpaca's data retention policy (typically 1-2 years for minute bars, longer for daily)","Data is sourced from Alpaca's market data providers and may have gaps during market halts or data feed issues","Batch queries have symbol limits (typically 100-200 symbols per request) — very large portfolios require pagination","Quote data (bid/ask) is delayed by 15-20 minutes for non-premium subscribers; real-time quotes require additional subscription"],"requires":["Alpaca API keys with market data read permissions","StockHistoricalDataClient configured with API credentials","Valid symbol(s) from supported exchanges (NYSE, NASDAQ, etc.)"],"input_types":["symbol or list of symbols (string or array)","start_date and end_date (ISO 8601 format)","timeframe (minute, hour, day, week, month)","optional limit parameter for result size"],"output_types":["bars array with timestamp, open, high, low, close, volume","quotes array with timestamp, bid, ask, bid_size, ask_size","latest price and quote data"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_4","uri":"capability://tool.use.integration.order.placement.and.execution.management","name":"order-placement-and-execution-management","description":"Exposes Alpaca TradingClient order methods as MCP tools for creating, modifying, and canceling orders across stocks, ETFs, crypto, and options. Tools support multiple order types (market, limit, stop, stop-limit, trailing-stop) and time-in-force options (day, gtc, opg, cls). The server translates natural language order descriptions (e.g., 'buy 100 shares of AAPL at market') into structured order objects with proper parameter validation, then submits to Alpaca's order execution engine. All orders are subject to account buying power and position limits.","intents":["Place market or limit orders for stocks, ETFs, and crypto with natural language descriptions","Create stop-loss and trailing-stop orders to automate risk management","Modify or cancel existing orders before execution","Execute complex multi-leg orders (e.g., buy-to-open call spreads) for options strategies"],"best_for":["Traders building LLM-powered trading assistants that execute orders on voice or text commands","Algorithmic trading agents that need to place orders based on strategy signals","Risk management systems that automatically place stop-loss orders based on position thresholds"],"limitations":["Order execution is subject to market hours and trading halts — orders placed outside market hours are queued for next open","Limit orders may not fill if price targets are not reached; no automatic retry or escalation logic","Trailing-stop orders are not supported for all asset classes (e.g., crypto may have restrictions)","No built-in position sizing or risk checks — LLM can place orders that exceed account buying power, resulting in rejection","Order confirmation is asynchronous; tool returns order object but actual fill may occur seconds or minutes later"],"requires":["Alpaca API keys with trading permissions (paper or live)","Sufficient account buying power for the order size","Valid symbol and order parameters (price, quantity, etc.)","Market must be open (or order queued for next open if using day TIF)"],"input_types":["symbol (string)","side (buy or sell)","quantity (integer or float)","order_type (market, limit, stop, stop_limit, trailing_stop)","price (for limit/stop orders)","time_in_force (day, gtc, opg, cls)","optional trail_price or trail_percent (for trailing-stop orders)"],"output_types":["order object with id, status, filled_qty, filled_avg_price, created_at","confirmation message with order details","error message if order is rejected (insufficient buying power, invalid symbol, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_5","uri":"capability://tool.use.integration.options.trading.and.greeks.analysis","name":"options-trading-and-greeks-analysis","description":"Integrates Alpaca's OptionHistoricalDataClient to provide options-specific tools for querying option contracts, retrieving Greeks (delta, gamma, theta, vega, rho), and analyzing options chains. Tools support filtering by symbol, expiration date, strike price, and option type (call/put), returning structured contract data with computed Greeks. The server also exposes options order placement through the TradingClient, enabling buy-to-open, sell-to-close, and spread strategies. Greeks are computed by Alpaca's pricing model and updated in real-time.","intents":["Query available option contracts for a symbol with specific expiration dates and strike prices","Retrieve Greeks (delta, gamma, theta, vega) for options positions to assess risk and decay","Analyze options chains to identify trading opportunities (e.g., high-theta positions, low-delta hedges)","Place options orders (calls, puts, spreads) with natural language descriptions"],"best_for":["Options traders building LLM-powered strategy analysis and execution tools","Quantitative analysts analyzing options Greeks and volatility surfaces","Algorithmic trading agents that execute options strategies based on market conditions"],"limitations":["Greeks are computed by Alpaca's model and may differ from third-party models (Black-Scholes vs. binomial, etc.)","Options data is limited to US equity options; no support for index options, futures options, or international options","Implied volatility (IV) is not directly exposed; must be inferred from Greeks or option prices","Options chains can be large (100+ contracts per symbol); no built-in filtering or pagination for LLM consumption","Spread orders (multi-leg) require manual construction; no native spread order type in Alpaca API"],"requires":["Alpaca API keys with options trading permissions","OptionHistoricalDataClient configured with API credentials","Valid underlying symbol with active options trading","Sufficient account buying power for options premium"],"input_types":["underlying symbol (string)","expiration_date (ISO 8601 format)","strike_price (float, optional for chain queries)","option_type (call or put, optional)","quantity and price (for order placement)"],"output_types":["options contract array with symbol, expiration, strike, type, bid, ask, last_price","Greeks object with delta, gamma, theta, vega, rho values","options chain data (all contracts for a symbol and expiration)","order confirmation for options orders"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_6","uri":"capability://search.retrieval.real.time.market.data.streaming","name":"real-time-market-data-streaming","description":"Provides access to Alpaca's StockDataStream for real-time market data feeds including trades, quotes, and bars. The server initializes a WebSocket connection to Alpaca's data stream endpoint and exposes tools for subscribing to specific symbols and data types. Streamed data is buffered and made available to LLM clients through polling or callback mechanisms. This enables trading agents to react to live market events without polling the REST API repeatedly.","intents":["Subscribe to real-time trade data for a symbol to detect large orders or unusual activity","Monitor live bid/ask quotes for liquidity analysis and order execution timing","Receive real-time bar updates (minute or second-level) for momentum-based trading signals","Build event-driven trading agents that react to market data in sub-second timeframes"],"best_for":["High-frequency and momentum traders who need sub-second market data","Event-driven trading agents that react to specific market conditions","Real-time portfolio monitoring systems that track live P&L and Greeks"],"limitations":["WebSocket connection requires persistent network connectivity; disconnections may cause data loss","Streamed data is buffered in memory; high-volume symbols (e.g., SPY) may consume significant memory","MCP protocol is request-response based; streaming data must be polled or buffered, adding latency vs. native WebSocket clients","Real-time data is available only during market hours; after-hours trading has limited data availability","Data stream subscription limits apply (typically 100-200 symbols per connection); very large watchlists require multiple connections"],"requires":["Alpaca API keys with real-time data permissions","StockDataStream configured with WebSocket URL and credentials","Persistent network connection to Alpaca's data stream endpoint","MCP client capable of polling or handling asynchronous updates"],"input_types":["symbol or list of symbols to subscribe to","data_type (trades, quotes, bars)","optional timeframe for bars (minute, second)"],"output_types":["trade events with timestamp, price, size, exchange","quote events with timestamp, bid, ask, bid_size, ask_size","bar events with timestamp, open, high, low, close, volume"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_7","uri":"capability://tool.use.integration.watchlist.and.asset.management","name":"watchlist-and-asset-management","description":"Exposes Alpaca TradingClient methods for creating, updating, and querying watchlists, as well as querying asset information (symbols, asset classes, tradability status). Tools allow users to organize symbols into named watchlists for portfolio tracking and strategy management. Asset queries return metadata including exchange, asset class (stock, etf, crypto, option), and trading status (active, inactive, delisted). This enables LLM agents to build dynamic watchlists and validate symbols before trading.","intents":["Create and manage watchlists for organizing symbols by strategy or sector","Query asset information to validate symbols and check trading status before placing orders","Retrieve watchlist contents to monitor multiple symbols simultaneously","Update watchlists dynamically based on strategy signals or market conditions"],"best_for":["Portfolio managers building LLM-powered watchlist and monitoring systems","Traders organizing symbols by strategy or sector for easier management","Algorithmic trading agents that validate symbols and check tradability before order placement"],"limitations":["Watchlists are account-specific and not shared across multiple accounts or users","Asset information is static and updated infrequently (daily or less); real-time tradability changes may not be reflected immediately","Watchlist size limits apply (typically 50-100 symbols per watchlist); very large portfolios require multiple watchlists","No built-in watchlist analytics or performance tracking; must query positions separately to calculate watchlist P&L"],"requires":["Alpaca API keys with account management permissions","TradingClient configured with API credentials","Valid symbol(s) from supported exchanges"],"input_types":["watchlist_name (string)","symbol or list of symbols (string or array)","optional asset_class filter (stock, etf, crypto, option)"],"output_types":["watchlist object with name and symbol array","asset object with symbol, asset_class, exchange, tradable, shortable, marginable","confirmation message for watchlist updates"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_8","uri":"capability://data.processing.analysis.corporate.actions.and.market.information.queries","name":"corporate-actions-and-market-information-queries","description":"Provides tools for querying corporate actions (dividends, splits, mergers) and market information (trading calendar, market status, holidays). Tools return structured data about upcoming and historical corporate actions, enabling trading agents to adjust positions before ex-dividend dates or stock splits. Market calendar queries return trading hours and holiday schedules, allowing agents to schedule orders appropriately. This information is sourced from Alpaca's market data providers and updated in real-time.","intents":["Query upcoming dividends and ex-dividend dates to adjust portfolio positions","Check stock split history and upcoming splits to validate position sizing","Retrieve trading calendar to schedule orders during market hours","Check market status (open, closed, pre-market, after-hours) to determine order timing"],"best_for":["Portfolio managers tracking corporate actions and dividend schedules","Algorithmic trading agents that adjust positions based on ex-dividend dates","Trading systems that schedule orders based on market hours and holidays"],"limitations":["Corporate actions data is sourced from Alpaca's providers and may lag official announcements by hours or days","Dividend amounts and split ratios are estimates until official confirmation; actual values may differ","Trading calendar is US-centric; international market hours and holidays are not included","No support for other corporate actions (mergers, bankruptcies, delistings) beyond dividends and splits"],"requires":["Alpaca API keys with market data read permissions","TradingClient configured with API credentials"],"input_types":["symbol (string, for corporate actions queries)","optional date range (start_date, end_date) for historical queries"],"output_types":["corporate actions array with symbol, action_type (dividend, split), ex_date, record_date, amount/ratio","trading calendar with date, open_time, close_time","market status object with current status (open, closed, pre-market, after-hours)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-alpacahq-alpaca-mcp-server__cap_9","uri":"capability://tool.use.integration.mcp.protocol.server.lifecycle.management","name":"mcp-protocol-server-lifecycle-management","description":"Implements the FastMCP framework to handle Model Context Protocol server initialization, tool registration, and request routing. The server starts as a standalone process (via python alpaca_mcp_server.py) or within a Docker container, initializes all Alpaca API clients based on environment configuration, registers 44+ tools with the MCP protocol, and listens for incoming tool calls from MCP clients (Claude Desktop, Cursor, VSCode). The server maintains a single persistent connection per client and routes all requests through the appropriate Alpaca API client.","intents":["Start the MCP server as a standalone process or Docker container","Register all trading and market data tools with the MCP protocol for client discovery","Route incoming tool calls from LLM clients to the appropriate Alpaca API client","Handle server lifecycle events (startup, shutdown, error recovery)"],"best_for":["Developers deploying trading agents in Claude Desktop, Cursor, or VSCode","DevOps teams containerizing the MCP server for cloud or on-premise deployment","Teams integrating Alpaca trading into existing LLM-powered applications"],"limitations":["Server is single-threaded (FastMCP default); high request volume may cause queuing and latency","No built-in load balancing or horizontal scaling; multiple servers require external orchestration","Server process must be running continuously; no automatic restart or health checks built-in","Configuration is environment-variable based; no runtime configuration API or hot-reload"],"requires":["Python 3.10+","FastMCP framework (installed via requirements.txt)","Alpaca API keys in environment variables or .env file","MCP client (Claude Desktop, Cursor, or VSCode with MCP extension)"],"input_types":["environment variables (ALPACA_API_KEY, ALPACA_SECRET_KEY, PAPER, etc.)","MCP protocol messages from client"],"output_types":["MCP tool registry (list of 44+ available tools)","MCP response messages with tool results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Alpaca API keys (paper or live trading)","MCP-compatible LLM client (Claude Desktop, Cursor IDE, VSCode with MCP extension)","Network connectivity to Alpaca API endpoints","PAPER environment variable set in .env, Docker environment, or Claude Desktop config","Alpaca API keys valid for both paper and live trading (if switching modes)","Restart of MCP server process to change trading modes","ALPACA_API_KEY and ALPACA_SECRET_KEY environment variables or .env file entries","Optional PAPER variable (defaults to True for paper trading)","Python-dotenv library for .env file support"],"failure_modes":["Parameter extraction relies on LLM interpretation — ambiguous requests may be misinterpreted (e.g., 'buy some Apple' could mean unclear quantity)","No built-in validation of trading intent — dangerous orders (e.g., liquidating entire portfolio) can be executed if LLM generates them","Latency overhead from MCP protocol serialization and Alpaca API round-trips adds 500ms-2s per operation","Limited to Alpaca's supported asset classes and order types — no support for complex derivatives or multi-leg strategies","Paper trading fills are simulated and may not reflect real market microstructure, slippage, or liquidity constraints","No automatic safeguards prevent accidental live trading if PAPER=False is set — requires explicit environment management","Paper account balances reset daily or on schedule; cannot persist multi-day strategy testing without manual intervention","Market data in paper mode may lag live data by seconds, affecting strategy validation accuracy","Credentials are read at server startup; changes require server restart","No built-in credential rotation or expiration management; API keys must be manually updated","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.42279496658485155,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2026-04-17T08:40:01Z"},"community":{"stars":694,"forks":219,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-alpacahq-alpaca-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-alpacahq-alpaca-mcp-server"}},"signature":"S9RcSTnKipkTHRRaHqCYhdSH8be7JYRrdzetD7YN5u9xi6Ie7G995ddCXX3SxQojCosIYjVYOfoY2xJMVnTtAg==","signedAt":"2026-06-21T15:58:13.433Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-alpacahq-alpaca-mcp-server","artifact":"https://unfragile.ai/mcp-alpacahq-alpaca-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-alpacahq-alpaca-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}