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| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Scans Solana DEX order books and on-chain transaction patterns to identify large whale accumulation/distribution signals in real-time. Integrates directly with Solana RPC endpoints to fetch mempool data and historical whale wallet movements, then applies proprietary scoring algorithms to surface high-conviction alpha opportunities before retail market participants.
Unique: Directly queries Solana RPC mempool and historical transaction data with proprietary whale behavior scoring (accumulation/distribution phase detection) rather than relying on third-party blockchain indexers, enabling sub-second signal freshness and custom threshold tuning per user
vs alternatives: Faster whale detection than Nansen or Arkham Intelligence for Solana because it queries RPC directly instead of waiting for indexed data, and cheaper than subscription-based whale tracking services via pay-per-call USDC model
Analyzes token price action and volume patterns to detect three types of technical divergences: hidden divergence (continuation signals), breakout divergence (reversal setup), and classic divergence (momentum exhaustion). Uses candlestick OHLCV data from Solana DEX to compute RSI/MACD indicators and identify price-momentum misalignments that precede directional moves.
Unique: Implements three distinct divergence detection algorithms (hidden/breakout/classic) with configurable RSI/MACD thresholds, allowing traders to tune sensitivity per market regime rather than using fixed indicator parameters like most TA tools
vs alternatives: More granular divergence classification than TradingView alerts (which only flag classic divergence), and cheaper than hiring human technical analysts to manually scan charts
Scores tokens on a 0-100 scale based on whale accumulation behavior patterns: large wallet inflows, decreasing sell pressure, increasing buy volume concentration. Analyzes on-chain wallet movements and DEX order flow to determine if whales are actively accumulating positions, outputting a quantified accumulation phase score that feeds into trading decision systems.
Unique: Combines multiple on-chain signals (wallet inflows, order book imbalance, sell pressure decay) into a single 0-100 accumulation score with configurable weighting, rather than binary accumulation/not-accumulation classification used by competitors
vs alternatives: More actionable than Glassnode's accumulation metrics because it's Solana-specific and includes real-time DEX order flow, whereas Glassnode focuses on exchange flows and has multi-day lag
Identifies tokens in neutral consolidation phases where whale activity is balanced (neither accumulating nor distributing) and price action is range-bound. Uses on-chain volume distribution, order book depth analysis, and wallet movement patterns to detect equilibrium periods that often precede directional breakouts.
Unique: Detects neutral phases by analyzing order book depth symmetry and whale wallet movement balance rather than just price volatility metrics, enabling detection of equilibrium states even during high-volume consolidations
vs alternatives: More precise than simple Bollinger Band squeeze detection because it incorporates on-chain whale behavior balance, reducing false positives from technical consolidations that lack whale equilibrium
Scores tokens on a 0-100 scale based on whale distribution behavior: large wallet outflows, increasing sell pressure, decreasing buy volume concentration. Analyzes on-chain wallet movements and DEX order flow to determine if whales are actively exiting positions, outputting a quantified distribution phase score that signals when to reduce exposure.
Unique: Combines multiple on-chain exit signals (wallet outflows, order book imbalance, buy pressure decay) into a single 0-100 distribution score with configurable weighting, enabling traders to distinguish between normal profit-taking and aggressive whale exits
vs alternatives: More actionable than on-chain exchange outflow metrics because it detects whale exits directly from DEX order flow rather than waiting for transfers to centralized exchanges, providing 30-60 minutes earlier warning
Analyzes Bitcoin price action, volatility regime, and trend direction to classify the current macro market regime (bull/bear/consolidation) and outputs a regime score that correlates with altcoin performance. Uses BTC OHLCV data, volatility indicators, and trend analysis to provide macro context for Solana token trading decisions.
Unique: Classifies BTC regime using multi-timeframe trend analysis and volatility regime detection rather than simple moving average crossovers, enabling detection of subtle regime transitions and consolidation periods that MA-based systems miss
vs alternatives: More nuanced than simple BTC dominance metrics because it analyzes BTC price action directly and correlates with altcoin performance, whereas dominance only measures market cap allocation
Analyzes Bitcoin futures market data (funding rates, open interest, liquidation levels) to generate trading signals that indicate directional bias and leverage extremes. Detects when futures markets are overextended (high funding rates, extreme open interest) and generates signals for mean-reversion trades or trend continuation based on market structure.
Unique: Combines funding rate extremes, open interest trends, and liquidation cascade detection into a single signal framework rather than analyzing each metric independently, enabling detection of market structure shifts that precede major moves
vs alternatives: More actionable than raw funding rate feeds because it contextualizes funding extremes with open interest and liquidation levels, whereas most traders only watch funding rates in isolation
Integrates CryptoIZ tools into the Model Context Protocol (MCP) ecosystem via Smithery registry, enabling LLM agents and AI applications to call whale intelligence functions directly. Uses x402 Dexter payment gateway to handle USDC micropayments per API call with gas sponsorship, eliminating need for SOL and enabling serverless, pay-as-you-go pricing model.
Unique: Implements MCP server with native x402 Dexter payment integration, enabling gas-sponsored USDC micropayments directly from LLM tool calls without requiring users to manage SOL wallets or gas fees — a novel approach to monetizing AI-native APIs
vs alternatives: Simpler integration than building custom REST API wrappers because MCP tools are natively callable by Claude and other LLMs, and cheaper than traditional SaaS pricing because x402 enables true pay-per-call without subscription overhead
+2 more capabilities
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 cryptoiz-mcp at 50/100. cryptoiz-mcp leads on adoption, while Firecrawl MCP Server is stronger on quality and ecosystem.
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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.
+5 more capabilities