Firecrawl MCP Server ranks higher at 62/100 vs yahoo-finance-mcp at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | yahoo-finance-mcp | Firecrawl MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 25/100 | 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 |
Fetches current stock prices, bid-ask spreads, and market data from Yahoo Finance API through the Model Context Protocol, enabling LLM agents to query live market data without direct API integration. Implements MCP tool schema binding to expose Yahoo Finance endpoints as callable functions that Claude or other MCP-compatible models can invoke with ticker symbols as parameters.
Unique: Exposes Yahoo Finance as an MCP tool, allowing LLM agents to call financial data retrieval as a native function without requiring the model to handle HTTP requests or API authentication directly. Uses MCP's standardized tool schema to abstract away API complexity.
vs alternatives: Simpler integration than building custom REST API wrappers for each LLM framework — MCP standardization means one server works with Claude, any MCP-compatible model, and multiple agent frameworks without code duplication.
Queries Yahoo Finance historical price data (OHLCV: open, high, low, close, volume) for a given ticker and date range, returning time-series data suitable for technical analysis or backtesting. Implements date parameter validation and pagination to handle large datasets, exposing the data through MCP tool interface for agent-driven historical analysis workflows.
Unique: Integrates historical data retrieval as an MCP tool, allowing agents to autonomously fetch and analyze multi-year price histories without requiring manual data downloads or external data pipeline setup. Abstracts pagination and date validation logic within the MCP server.
vs alternatives: Faster agent iteration than manual CSV imports or direct API calls — agents can request historical data inline during reasoning, enabling dynamic analysis without context switching to external tools.
Retrieves company financial metrics (P/E ratio, market cap, dividend yield, earnings per share, debt-to-equity, etc.) from Yahoo Finance for fundamental analysis. Implements MCP tool binding to expose these metrics as queryable data, enabling agents to perform valuation comparisons and fundamental screening without manual data aggregation.
Unique: Exposes Yahoo Finance fundamentals as MCP tools, allowing agents to query financial metrics inline during analysis without requiring separate data sources or manual metric aggregation. Standardizes metric naming and format across the MCP interface.
vs alternatives: More accessible than building custom SEC filing parsers or maintaining multiple financial data subscriptions — agents get standardized fundamental data through a single MCP interface.
Enables agents to request data for multiple tickers in a single MCP tool invocation or through sequential calls with optimized batching. Implements parameter arrays or loop-friendly tool schemas to allow agents to analyze portfolios or compare multiple securities without individual API calls per ticker. Handles response aggregation and formatting for comparative analysis.
Unique: Implements batch-friendly MCP tool schemas that allow agents to request data for multiple tickers efficiently, reducing round-trip overhead compared to single-ticker tools. Aggregates responses in a format optimized for comparative analysis.
vs alternatives: Reduces agent latency for portfolio analysis compared to sequential single-ticker calls — agents can analyze 10-stock portfolios in one tool invocation instead of 10 separate API calls.
Implements the Model Context Protocol server specification, handling client connection negotiation, tool schema registration, and request routing. Manages the server process lifecycle (startup, shutdown, error handling) and exposes Yahoo Finance capabilities as standardized MCP tools with JSON schema definitions. Handles protocol versioning and capability advertisement to ensure compatibility with MCP clients.
Unique: Implements full MCP server lifecycle management, handling protocol negotiation, tool schema registration, and request routing according to the MCP specification. Abstracts away JSON-RPC complexity from tool developers.
vs alternatives: Standardized MCP implementation means the server works with any MCP-compatible client without custom adapters — Claude Desktop, custom runners, and future MCP tools all connect to the same server without modification.
Implements error handling for Yahoo Finance API failures (rate limits, timeouts, invalid tickers, network errors) with appropriate MCP error responses. Provides fallback strategies such as cached data, partial results, or clear error messages that agents can interpret and act upon. Distinguishes between transient errors (retry-able) and permanent failures (invalid ticker).
Unique: Implements MCP-compliant error responses that distinguish between transient API failures and permanent errors, allowing agents to make intelligent retry decisions. Provides structured error information that agents can parse and act upon.
vs alternatives: Better agent resilience than naive API wrappers that crash on errors — agents receive clear error codes and can implement retry logic or fallback strategies without manual error parsing.
Fetches dividend history, upcoming dividend dates, and earnings information from Yahoo Finance for a given ticker. Returns structured data including dividend per share, ex-dividend dates, earnings dates, and EPS estimates. Enables fundamental analysis and income-focused portfolio evaluation through Claude's reasoning capabilities.
Unique: Exposes dividend and earnings data as MCP tools, enabling Claude to incorporate fundamental factors into investment analysis without requiring separate financial data sources. Structures data to support both historical analysis and forward-looking decision-making.
vs alternatives: More comprehensive than price-only analysis because it includes income and earnings factors; more accessible than building custom fundamental analysis tools because data is pre-fetched and structured.
Retrieves information about stock splits, reverse splits, mergers, and other corporate actions from Yahoo Finance. Enables accurate historical price analysis by accounting for adjustments and helps Claude understand why historical prices may appear discontinuous. Integrates with historical price retrieval to provide context for price anomalies.
Unique: Provides corporate action history as a dedicated MCP tool, enabling Claude to automatically detect and explain price discontinuities in historical data. Integrates with historical price retrieval to provide context for analysis.
vs alternatives: More transparent than pre-adjusted historical prices because it shows the actual corporate actions; more useful than ignoring splits because Claude can explain historical anomalies to users.
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 yahoo-finance-mcp at 25/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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