Firecrawl MCP Server ranks higher at 62/100 vs yfinance-mcp-server2 at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | yfinance-mcp-server2 | 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 |
Exposes yfinance's stock ticker data fetching through the Model Context Protocol, allowing AI agents to query current and historical stock prices by ticker symbol. Implements MCP resource endpoints that wrap yfinance's Ticker.history() and Ticker.info methods, translating HTTP/JSON requests into structured financial data responses with OHLCV (open, high, low, close, volume) fields and metadata.
Unique: Wraps yfinance as an MCP server, enabling direct integration with Claude and other MCP-compatible AI agents without custom API wrappers; uses MCP's resource and tool abstractions to expose ticker data as first-class protocol primitives rather than generic function calls
vs alternatives: Simpler than building a custom REST API wrapper around yfinance; tighter integration with Claude's native MCP support compared to generic HTTP tool calling
Supports querying multiple stock tickers in a single MCP call, aggregating results into a unified response structure. Implements parallel or sequential fetching of yfinance Ticker objects, combining OHLCV data, fundamentals, and metadata across symbols, then normalizing into a consistent JSON schema for downstream processing by AI agents.
Unique: Implements batch ticker fetching as a single MCP tool invocation, reducing round-trip overhead compared to calling single-ticker endpoints repeatedly; normalizes heterogeneous yfinance responses into a consistent schema for agent consumption
vs alternatives: More efficient than agents making N separate API calls for N tickers; cleaner than agents managing their own batching logic outside the MCP boundary
Exposes yfinance's Ticker.history() method through MCP, allowing agents to fetch historical price data at configurable intervals (daily, weekly, monthly). Translates interval parameters into yfinance's period/interval arguments, returns time-indexed DataFrames converted to JSON arrays with timestamp, open, high, low, close, volume, and adjusted close fields.
Unique: Parameterizes yfinance's interval selection (daily/weekly/monthly) as MCP tool arguments, allowing agents to dynamically request different granularities without code changes; converts pandas DataFrames to JSON with explicit timestamp normalization for agent consumption
vs alternatives: More flexible than fixed-interval endpoints; avoids agents needing to manage pandas or numpy dependencies directly
Exposes yfinance's Ticker.info dictionary through MCP, extracting key fundamental metrics (PE ratio, market cap, dividend yield, earnings per share, 52-week high/low, etc.). Implements selective field extraction from yfinance's unstructured info dict, normalizing null/missing values and converting numeric strings to proper types for agent consumption.
Unique: Selectively extracts and normalizes yfinance's unstructured Ticker.info dict into a clean schema, handling type conversions and null values; exposes fundamental metrics as a dedicated MCP tool rather than bundling with price data
vs alternatives: Cleaner than agents parsing raw yfinance dicts; more focused than generic financial data APIs that require separate subscriptions
Implements the Model Context Protocol server specification, registering yfinance capabilities as MCP resources and tools with proper schema definitions. Uses MCP's JSONSchema for input validation, implements request/response serialization, and handles MCP lifecycle (initialization, capability advertisement, error handling). Enables Claude and other MCP clients to discover and invoke yfinance functions with type-safe arguments.
Unique: Implements full MCP server lifecycle (initialization, capability advertisement, request handling) as a Python MCP server, enabling seamless integration with Claude and other MCP clients; uses JSONSchema for declarative tool definitions rather than runtime type checking
vs alternatives: Tighter integration with Claude than generic REST APIs; avoids custom HTTP server boilerplate by leveraging MCP's standardized protocol
Implements input validation for ticker symbols and date parameters, catching invalid tickers early and returning structured error responses via MCP. Validates ticker format (alphanumeric, length constraints), date range logic (start < end), and handles yfinance exceptions (network errors, invalid symbols) by translating them into MCP error responses with descriptive messages for agent consumption.
Unique: Implements input validation at the MCP boundary before invoking yfinance, reducing wasted API calls and providing early feedback; translates yfinance exceptions into MCP-compliant error responses with structured metadata
vs alternatives: Prevents agents from making invalid yfinance calls; cleaner error handling than agents parsing raw exceptions
Handles common ticker symbol variations and aliases (e.g., 'Apple' → 'AAPL', 'BRK.A' → 'BRK-A'), normalizing user input before querying yfinance. Implements a mapping layer for common aliases and case-insensitive matching, allowing agents to accept natural language ticker references and convert them to valid yfinance symbols.
Unique: Implements a ticker normalization layer before yfinance calls, allowing agents to accept natural language or alternative formats; uses a static alias mapping for common variations rather than external symbol resolution services
vs alternatives: Simpler than agents managing their own normalization logic; avoids dependency on external symbol resolution APIs
Provides a runnable MCP server implementation with standard lifecycle hooks (startup, shutdown, error recovery). Implements stdio-based MCP transport for local execution or can be deployed as a subprocess managed by MCP clients (Claude Desktop, custom hosts). Handles graceful shutdown, resource cleanup, and connection state management for reliable agent integration.
Unique: Implements a complete MCP server with lifecycle management, using stdio transport for local execution; handles initialization, capability advertisement, and graceful shutdown without requiring external process managers
vs alternatives: Simpler deployment than custom REST API servers; integrates directly with Claude Desktop without additional infrastructure
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 yfinance-mcp-server2 at 25/100.
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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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