| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 18 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Discovers and enumerates tokenized yield vaults across Ethereum and Base networks by querying on-chain vault registries and metadata contracts. Implements network-aware discovery that differentiates between mainnet and Layer 2 deployments, returning vault addresses, underlying asset compositions, and current TVL metrics through MCP tool bindings that abstract RPC calls.
Unique: Provides MCP-native vault enumeration that abstracts away RPC complexity and chain-specific contract ABIs, enabling LLM agents to discover vaults without writing Solidity or Web3.js code. Dual-chain support (Ethereum + Base) in a single tool call.
vs alternatives: Simpler than building custom Web3 indexers or querying The Graph subgraphs; faster than manual vault research because it returns structured, queryable vault metadata in a single MCP call.
Retrieves historical and real-time performance metrics for vaults including APY, yield distribution history, fee structures, and asset allocation changes. Queries on-chain vault state and event logs to compute performance indicators, then normalizes data into standardized metrics accessible via MCP tools. Handles multi-token vault compositions and fee deductions in APY calculations.
Unique: Computes normalized APY metrics across multi-token vaults by parsing on-chain events and state, abstracting away complex fee and yield distribution logic. Integrates fee deductions directly into APY calculations rather than reporting gross yield separately.
vs alternatives: More accurate than static APY displays because it recalculates based on actual on-chain events; more accessible than writing custom subgraph queries because metrics are pre-computed and standardized.
Estimates gas costs for vault operations (deposits, withdrawals, approvals) by simulating transactions and querying current gas prices. Returns gas estimates in wei and USD equivalent using current ETH/USD prices. Supports gas price overrides for custom priority levels.
Unique: Simulates vault operations to estimate gas costs and converts to USD using current ETH prices in a single call. Breaks down gas costs by operation step (approval, deposit, etc.) to help callers understand where costs are incurred.
vs alternatives: More accurate than static gas estimates because it simulates actual transactions; more convenient than manual gas calculations because it handles multi-step operations and USD conversion.
Assesses vault risks including smart contract risk, liquidity risk, concentration risk, and governance risk. Scores vaults on multiple risk dimensions and provides a composite risk score. Analyzes vault TVL, asset concentration, governance structure, and audit status to produce risk metrics.
Unique: Computes multi-dimensional risk scores (smart contract, liquidity, concentration, governance) from on-chain data and produces a composite risk score. Enables risk-aware vault filtering without requiring manual risk analysis.
vs alternatives: More comprehensive than simple TVL-based risk assessment because it evaluates multiple risk dimensions; more accessible than building custom risk models because it returns pre-computed risk scores.
Analyzes historical vault performance over multiple time horizons (1 week, 1 month, 3 months, 1 year, etc.) by computing returns, volatility, and Sharpe ratios. Queries historical share prices and yield distributions to compute performance metrics. Supports comparison of vault performance against benchmarks or other vaults.
Unique: Computes multi-horizon performance metrics (returns, volatility, Sharpe ratio) from historical share prices and yield distributions. Enables performance comparison across vaults without requiring manual calculation.
vs alternatives: More comprehensive than simple APY displays because it shows volatility and risk-adjusted returns; more accessible than building custom performance analysis tools because it returns pre-computed metrics.
Simulates vault rebalancing scenarios to show how asset allocation changes would impact returns and fees. Allows users to specify target allocations and simulates the rebalancing process including transaction costs and slippage. Returns projected outcomes and cost analysis for proposed rebalancing.
Unique: Simulates rebalancing transactions and cost impact in a single call, allowing callers to evaluate rebalancing decisions before execution. Breaks down costs by component (gas, slippage) to help optimize rebalancing strategy.
vs alternatives: More transparent than manual rebalancing because it shows projected costs and outcomes; more efficient than trial-and-error rebalancing because it simulates multiple strategies.
Generates recommendations for optimizing yield by analyzing user's current vault positions and suggesting alternative vaults or rebalancing actions. Uses multi-vault comparison, performance analysis, and fee optimization to identify opportunities for improved risk-adjusted returns. Ranks recommendations by potential impact and implementation cost.
Unique: Generates yield optimization recommendations by analyzing user's current positions and comparing against alternative vaults using multi-dimensional metrics (APY, fees, risk, liquidity). Ranks recommendations by projected impact and implementation cost.
vs alternatives: More personalized than generic vault rankings because it considers user's current positions; more actionable than simple performance comparisons because it provides specific recommendations with projected outcomes.
Aggregates vault positions across Ethereum and Base into a unified view with cross-chain portfolio metrics. Queries vault holdings on both chains, converts to common reference currency, and provides consolidated portfolio analytics. Handles chain-specific gas costs and liquidity considerations in recommendations.
Unique: Aggregates vault positions across Ethereum and Base in a single call, normalizing to common currency and providing unified portfolio metrics. Handles chain-specific considerations (gas costs, liquidity) in recommendations.
vs alternatives: More convenient than querying each chain separately because it returns consolidated metrics; more accurate than manual cross-chain aggregation because it handles currency conversion and chain-specific factors.
+10 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 atv at 46/100.
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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.
+5 more capabilities