| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 16 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Evaluates derivatives and integrals using SymPy's symbolic engine with fallback to numerical methods (SciPy). Supports both symbolic differentiation/integration for closed-form solutions and numerical approximation for complex functions. Automatically selects the optimal method based on expression complexity and user intent, returning step-by-step symbolic derivations or precise numerical results with configurable precision.
Unique: Dual-mode calculus engine combining SymPy's symbolic manipulation with SciPy's numerical robustness, automatically selecting symbolic derivation when tractable and falling back to adaptive numerical quadrature for integrals that resist closed-form solutions. Exposes both intermediate symbolic steps and final numerical results in a single call.
vs alternatives: Provides symbolic-first approach with numerical fallback (unlike pure numerical libraries like NumPy), while maintaining performance through intelligent method selection rather than attempting all symbolic paths exhaustively.
Performs matrix algebra operations (multiply, inverse, transpose, determinant, trace) and advanced decompositions (eigenvalue, SVD, QR, Cholesky, LU) using NumPy's BLAS/LAPACK bindings for computational efficiency. Handles both dense and sparse matrices, with automatic numerical stability checks and condition number reporting for ill-conditioned systems. Decompositions return both the factorized components and reconstruction verification.
Unique: Wraps NumPy/SciPy's LAPACK bindings with automatic numerical stability diagnostics (condition numbers, reconstruction errors) and returns both factorized components and verification metrics, enabling developers to assess solution reliability without manual conditioning checks.
vs alternatives: Faster than pure Python implementations by leveraging optimized BLAS/LAPACK, and provides stability diagnostics that pure numerical libraries omit, making it suitable for production systems where numerical reliability matters.
Creates pivot tables from data with configurable row/column grouping and aggregation functions (sum, mean, count, min, max, std). Uses Pandas' pivot_table function under the hood, automatically handling missing values and providing multiple aggregation strategies. Returns a 2D table with grouped data and computed aggregates, useful for summarizing and cross-tabulating data.
Unique: Wraps Pandas' pivot_table with configurable row/column grouping and multiple aggregation functions, automatically handling missing values and returning both the pivot table and metadata about grouping/aggregation choices.
vs alternatives: More flexible than manual grouping and aggregation; faster than loop-based summarization through vectorized Pandas operations; supports multiple aggregations simultaneously.
Converts between units (currently focused on angle conversions: degrees ↔ radians) using mathematical constants and conversion formulas. Supports bidirectional conversion with automatic detection of input unit and output unit specification. Extensible architecture allows adding additional unit types (temperature, distance, etc.) without modifying core logic.
Unique: Provides bidirectional angle conversion (degrees ↔ radians) with automatic unit detection and extensible architecture for adding additional unit types. Uses precise mathematical constants (math.pi) for accurate conversion.
vs alternatives: Simpler and more focused than general-purpose unit conversion libraries; integrated into the MCP server for seamless use in mathematical workflows.
Provides multiple rounding strategies (round to nearest, floor, ceiling, truncate) using NumPy's rounding functions. Supports rounding to arbitrary decimal places or significant figures, with configurable tie-breaking behavior (banker's rounding, round-half-up). Returns both the rounded value and metadata about the rounding operation.
Unique: Provides multiple rounding strategies (round, floor, ceil, truncate) with support for both decimal places and significant figures, using NumPy's optimized functions and returning metadata about the rounding operation.
vs alternatives: More flexible than Python's built-in round() function by supporting multiple strategies and significant figures; faster than manual rounding through NumPy vectorization.
Computes percentage operations (percentage of a value, percentage increase/decrease, percentage change) using simple arithmetic with configurable output format (decimal, percentage string). Supports both forward calculations (what is 15% of 250?) and reverse calculations (250 is what percentage of 1000?). Returns both the numerical result and formatted percentage string.
Unique: Supports multiple percentage operation modes (of, increase, decrease, change) with configurable output formatting (decimal or percentage string), returning both numerical results and formatted strings for display.
vs alternatives: More comprehensive than simple percentage formulas by supporting multiple operation types; includes formatting for display without requiring post-processing.
Evaluates mathematical expressions (e.g., 'x**2 + 3*x + 2') with variable substitution using SymPy's expression parser and NumPy for numerical evaluation. Supports both symbolic evaluation (returning expressions) and numerical evaluation (returning floats). Handles complex expressions with multiple variables, functions (sin, cos, exp, log), and constants (pi, e).
Unique: Combines SymPy's expression parsing with NumPy's numerical evaluation, supporting both symbolic and numerical modes. Handles variable substitution transparently and supports a wide range of mathematical functions and constants.
vs alternatives: More flexible than hardcoded formulas by accepting arbitrary expressions; safer than eval() by using SymPy's parser instead of Python's eval(); supports both symbolic and numerical evaluation modes.
Solves systems of linear equations (Ax = b) using NumPy's linear algebra solvers (LAPACK-backed), with automatic selection between direct solvers (LU decomposition) and iterative solvers for large sparse systems. Provides numerical stability diagnostics (condition number, residual norm) to assess solution reliability. Handles both square and overdetermined systems (least-squares).
Unique: Wraps NumPy's LAPACK-backed solvers with automatic method selection (direct vs iterative) and provides numerical stability diagnostics (condition number, residual norm). Supports both square and overdetermined systems with least-squares solutions.
vs alternatives: Faster than manual Gaussian elimination through LAPACK; includes stability diagnostics that pure solvers omit, enabling assessment of solution reliability. Automatic method selection optimizes for both speed and accuracy.
+8 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 Vibe Math at 35/100. Vibe Math leads on ecosystem, while Firecrawl MCP Server is stronger on adoption and quality.
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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