Firecrawl MCP Server ranks higher at 62/100 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 | Firecrawl MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 29/100 | 62/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 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.
Scrapes a single URL and converts HTML content to clean markdown using Firecrawl's content extraction pipeline. The firecrawl_scrape tool accepts a URL and optional parameters (formats, headers, wait time, screenshot capability) and returns structured markdown output with automatic cleanup of boilerplate, navigation, and ads. Implements MCP tool handler pattern that marshals arguments through the @mendable/firecrawl-js client library to Firecrawl's backend processing engine.
Unique: Integrates Firecrawl's proprietary content extraction engine (which uses ML-based boilerplate removal and semantic content identification) through MCP protocol, enabling AI agents to access production-grade web scraping without managing browser automation or parsing logic themselves. The markdown conversion is handled server-side rather than client-side, reducing latency and ensuring consistent output formatting.
vs alternatives: Cleaner markdown output than regex-based scrapers like Cheerio or Puppeteer-only solutions because Firecrawl uses ML models to identify main content; simpler than self-hosted solutions because it's fully managed and requires only an API key.
Scrapes multiple URLs in a single operation using Firecrawl's batch processing pipeline. The firecrawl_batch_scrape tool accepts an array of URLs and shared options, submitting them to Firecrawl's backend which processes them in parallel and returns an array of markdown-converted content objects. Implements batching through the @mendable/firecrawl-js client's batch method, which handles request queuing, parallel execution, and result aggregation without requiring client-side coordination.
Unique: Implements server-side parallel batch processing through Firecrawl's backend rather than client-side loop iteration, reducing network round-trips and enabling true concurrent scraping. The batch operation is atomic from the MCP client perspective — a single tool call returns all results, simplifying agent orchestration logic.
More efficient than sequential scraping loops because Firecrawl handles parallelization server-side; simpler than managing Promise.all() with individual scrape calls because batching is a first-class operation with built-in error handling.
Firecrawl MCP Server scores higher at 62/100 vs AkTools MCP Server for stock and crypto at 29/100.
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Packages the Firecrawl MCP server as a Docker container with environment-based configuration, enabling deployment to containerized infrastructure (Kubernetes, Docker Compose, cloud platforms). The Dockerfile builds a Node.js runtime with the server code and exposes configuration through environment variables, allowing operators to deploy without modifying code. Supports both cloud and self-hosted Firecrawl instances through configuration.
Unique: Provides production-ready Docker packaging with environment-based configuration, enabling zero-code deployment to containerized infrastructure. The Dockerfile handles Node.js runtime setup and dependency installation, reducing deployment complexity.
vs alternatives: Simpler than manual deployment because Docker handles environment setup; more portable than binary distribution because containers run consistently across platforms.
Registers the Firecrawl MCP server in the Smithery registry, enabling one-click installation and discovery through Smithery's MCP client marketplace. The server is published to Smithery with metadata (description, tags, configuration schema) allowing users to discover and install it without manual setup. Smithery handles server distribution, version management, and client integration.
Unique: Leverages Smithery's MCP server registry to enable one-click installation without manual configuration, reducing friction for end users. Smithery handles server discovery, versioning, and client integration, abstracting deployment complexity.
vs alternatives: More user-friendly than manual installation because Smithery handles discovery and setup; more discoverable than GitHub-only distribution because Smithery provides a centralized marketplace.
Supports connecting to self-hosted Firecrawl instances in addition to Firecrawl's cloud service through configurable API endpoint. The FIRECRAWL_API_URL environment variable allows operators to specify a custom Firecrawl endpoint, enabling deployment scenarios where Firecrawl runs on-premises or in a private cloud. The @mendable/firecrawl-js client library handles endpoint abstraction, routing all API calls to the configured endpoint.
Unique: Enables flexible deployment by supporting both cloud and self-hosted Firecrawl instances through simple endpoint configuration, allowing operators to choose deployment model without code changes. The endpoint abstraction is handled by @mendable/firecrawl-js, making self-hosted support transparent to MCP server code.
vs alternatives: More flexible than cloud-only solutions because self-hosted option is available; simpler than maintaining separate server implementations because endpoint configuration is unified.
Discovers all URLs within a website by crawling from a base URL and building a sitemap-like structure. The firecrawl_map tool accepts a base URL and optional parameters (max depth, include patterns, exclude patterns) and returns a hierarchical array of discovered URLs with metadata about page structure. Uses Firecrawl's crawler to traverse internal links up to specified depth, filtering by inclusion/exclusion patterns, and returns the complete URL graph without fetching full page content.
Unique: Provides lightweight URL discovery without content extraction, allowing agents to plan scraping strategy before committing credits to full content fetches. The depth-based crawling with pattern filtering enables selective discovery — agents can discover only URLs matching specific criteria (e.g., /blog/* paths) without exploring entire site.
vs alternatives: More efficient than scraping every page to build a sitemap because it skips content extraction; more reliable than parsing robots.txt or sitemaps.xml because it performs actual crawling and discovers dynamically-linked content.
Crawls an entire website and extracts content from all discovered pages in a single asynchronous operation. The firecrawl_crawl tool accepts a base URL and options (max pages, allowed domains, exclude patterns, scrape options) and returns a crawl ID for polling. The crawler discovers URLs, extracts markdown content from each page, and stores results server-side. Clients poll firecrawl_crawl_status to retrieve results as they complete, implementing an async job pattern rather than blocking until completion.
Unique: Implements server-side asynchronous crawling with job-based result retrieval, decoupling the crawl initiation from result consumption. The MCP server handles polling coordination through firecrawl_crawl_status, allowing AI agents to initiate long-running crawls and check progress without blocking. Firecrawl's backend manages the entire crawl lifecycle including URL discovery, content extraction, and result storage.
vs alternatives: More scalable than sequential scraping because crawling happens server-side in parallel; simpler than managing Puppeteer/Playwright browser pools because Firecrawl abstracts browser automation and handles rate limiting internally.
Polls the status of an in-progress or completed website crawl and retrieves extracted content. The firecrawl_crawl_status tool accepts a crawl ID and returns current progress (pages crawled, pages remaining, completion percentage), status state (running/completed/failed), and paginated results. Implements polling pattern where clients repeatedly call this tool with the same crawl ID to check progress and incrementally retrieve content as pages are processed, supporting streaming-like result consumption.
Unique: Provides non-blocking status and result retrieval for asynchronous crawls, enabling agents to manage long-running operations without blocking. The polling pattern with pagination allows incremental result consumption — agents can start processing results before the entire crawl completes, reducing end-to-end latency for large crawls.
vs alternatives: More flexible than blocking crawl operations because agents can check progress and retrieve partial results; simpler than webhook-based result delivery because polling requires no external infrastructure setup.
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