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| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Perform symbolic mathematical operations including equation solving, polynomial manipulation, and algebraic simplification using SymPy's computer algebra system. The MCP server exposes SymPy's symbolic expression API, allowing clients to define variables, construct equations, and request symbolic solutions without numerical approximation. Operations are executed server-side and results returned as symbolic expressions or simplified forms.
Unique: Exposes SymPy's full symbolic algebra engine through MCP protocol, enabling LLM-driven symbolic computation without requiring clients to manage Python environments or dependency installation
vs alternatives: Provides exact symbolic solutions via MCP integration, whereas Wolfram Alpha requires API calls and WolframScript requires local installation; Fermat's MCP approach allows seamless LLM orchestration of symbolic math
Execute numerical computations using NumPy arrays and linear algebra operations, including matrix operations, statistical calculations, and numerical transformations. The server wraps NumPy's vectorized operations and exposes them through MCP function calls, handling array serialization/deserialization and returning results as JSON-compatible numeric structures. Supports batch operations on multi-dimensional arrays.
Unique: Wraps NumPy's vectorized operations through MCP protocol with automatic array serialization, allowing LLMs to orchestrate complex numerical workflows without direct Python execution or environment setup
vs alternatives: Faster than calling external APIs for numerical operations because computations execute locally on the MCP server; more accessible than raw NumPy because it abstracts array management through MCP function signatures
Customizes plot appearance through Matplotlib's styling API, supporting color schemes, line styles, markers, fonts, legends, grid options, and axis formatting. Accepts plot objects and styling specifications, applies Matplotlib formatting functions, and returns styled plots. Supports both programmatic styling and predefined style templates.
Unique: Exposes Matplotlib's styling API through MCP tools with predefined style templates and programmatic customization, enabling LLM agents to apply consistent formatting without manual Matplotlib code
vs alternatives: Provides both template-based and programmatic styling through a single interface, whereas manual Matplotlib styling requires extensive code and knowledge of styling API
Compute derivatives, integrals, limits, and series expansions using SymPy for symbolic calculus and NumPy for numerical differentiation/integration. The server routes requests to appropriate backends based on operation type — symbolic operations use SymPy's calculus module, while numerical integration uses scipy.integrate. Results include both symbolic expressions and numerical evaluations where applicable.
Unique: Hybrid symbolic-numerical calculus engine that automatically selects SymPy or SciPy based on operation feasibility, providing exact symbolic results when possible and falling back to numerical approximation with error bounds
vs alternatives: Combines symbolic and numerical calculus in one MCP interface, whereas separate tools require choosing between WolframAlpha (symbolic, API-dependent) or SciPy (numerical, requires Python coding)
Perform statistical computations including descriptive statistics (mean, median, variance, skewness), correlation analysis, hypothesis testing, and probability distributions using NumPy and SciPy.stats. The server accepts datasets as arrays and returns statistical summaries, correlation matrices, and test results with p-values. Supports both parametric and non-parametric statistical tests.
Unique: Integrates NumPy and SciPy.stats through MCP to provide both descriptive and inferential statistics in a single interface, with automatic selection of parametric vs non-parametric tests based on data characteristics
vs alternatives: More accessible than raw SciPy because MCP abstracts statistical test selection and result formatting; more comprehensive than simple NumPy aggregations because it includes hypothesis testing and distribution modeling
Generate 2D plots including line plots, scatter plots, histograms, bar charts, and heatmaps using Matplotlib. The server accepts plot specifications (data, axes labels, plot type) and returns rendered images as PNG or SVG. Supports customization of colors, markers, legends, and styling. Generated plots are serialized as base64-encoded images or file paths for client consumption.
Unique: Exposes Matplotlib's full plotting API through MCP with automatic image serialization, enabling LLMs to generate publication-quality visualizations without requiring clients to manage Matplotlib state or file I/O
vs alternatives: More flexible than cloud plotting services (Plotly Cloud) because plots generate locally without external API calls; more accessible than raw Matplotlib because MCP abstracts figure management and image encoding
Generate 3D surface plots, scatter plots, and wireframe visualizations using Matplotlib's mplot3d toolkit. The server accepts 3D data specifications and returns rendered 3D plots as images with configurable viewing angles and projections. Supports surface plots from function definitions or data grids, and 3D scatter plots for point cloud visualization.
Unique: Wraps Matplotlib's mplot3d module through MCP with automatic viewing angle configuration and image serialization, allowing LLMs to generate 3D visualizations without managing complex Matplotlib 3D state
vs alternatives: Simpler than Plotly 3D for static 3D visualization because it doesn't require interactive rendering; more accessible than raw mplot3d because MCP abstracts 3D coordinate transformation and camera setup
Expose all mathematical and plotting operations through the Model Context Protocol (MCP) as callable functions with typed schemas. The server implements MCP's tool/function interface, allowing LLM clients to discover available operations, inspect parameter schemas, and invoke computations with automatic argument validation and error handling. Results are returned as structured JSON responses compatible with LLM processing.
Unique: Implements full MCP protocol compliance for mathematical operations, enabling seamless integration with LLM clients through standard tool discovery and invocation mechanisms rather than custom API wrappers
vs alternatives: More standardized than custom REST APIs because it uses MCP protocol; more discoverable than hardcoded function lists because LLMs can introspect available operations and their schemas at runtime
+3 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 Fermat at 33/100.
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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