| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes Proxmox VE infrastructure (nodes, VMs, containers, storage) as MCP tools that Claude and other LLM clients can invoke. Uses the MCP server specification to translate Proxmox REST API responses into structured tool definitions, enabling LLMs to query cluster state without direct API knowledge. Implements read-only access by default with optional credential-based authentication to the Proxmox API endpoint.
Unique: Minimal Docker-based MCP server that directly wraps Proxmox REST API without intermediate abstraction layers, enabling single-command deployment ('one Docker command') while maintaining read-only safety defaults and MCP protocol compliance
vs alternatives: Simpler deployment and lower operational overhead than building custom Proxmox integrations in Python/Go, and safer by default (read-only) compared to exposing raw Proxmox API to LLMs
Automatically generates MCP tool definitions (name, description, input schema, output schema) by introspecting Proxmox REST API endpoints and translating them into LLM-callable tools. Maps Proxmox API parameters to JSON Schema for input validation and structures responses as typed outputs that Claude can parse and act upon. Handles authentication headers and endpoint routing transparently.
Unique: Leverages Proxmox API structure to programmatically generate MCP tool schemas rather than requiring manual tool definition, reducing maintenance burden and keeping tool definitions aligned with API versions
vs alternatives: Avoids hand-written tool definitions that drift from API reality, unlike static MCP server implementations that require code changes for each new endpoint
Manages authentication to Proxmox VE REST API using either API tokens (preferred, fine-grained permissions) or username/password credentials. Stores credentials securely (environment variables or config files) and injects them into all outbound Proxmox API requests as Bearer tokens or Basic Auth headers. Validates credentials at server startup and fails fast if authentication fails.
Unique: Supports both Proxmox API tokens (fine-grained, revocable) and legacy username/password auth, with read-only default configuration that prevents accidental infrastructure modifications through the MCP interface
vs alternatives: More secure than embedding credentials in MCP tool definitions, and supports Proxmox's modern token-based auth unlike older integration approaches that rely solely on user accounts
Packages the Proxmox MCP server as a Docker image with pre-configured entrypoint, environment variable injection, and network binding. Enables deployment via a single 'docker run' command with credential and endpoint configuration passed as environment variables. Includes health checks and graceful shutdown handling for container orchestration systems.
Unique: Advertises 'one Docker command' deployment model, eliminating installation complexity and dependency conflicts by bundling the entire MCP server runtime and Proxmox client libraries into a single container image
vs alternatives: Faster to deploy and more reproducible than manual installation from source, and requires less operational knowledge than building and running the server natively
Enforces read-only access to Proxmox infrastructure by default, preventing Claude or other LLM clients from accidentally (or maliciously) modifying VMs, containers, or cluster state through MCP tools. Provides configuration option to selectively enable write operations (VM power control, container management) for trusted deployments, with audit logging of all state-modifying operations.
Unique: Defaults to read-only access, requiring explicit opt-in for write operations, which is a safer-by-default approach than exposing full API capabilities and relying on LLM judgment to avoid destructive actions
vs alternatives: Prevents accidental infrastructure damage that could occur with unrestricted API access, and provides a clear security boundary that can be audited and enforced at the MCP server level rather than relying on Proxmox RBAC alone
Implements the Model Context Protocol (MCP) specification, enabling the Proxmox server to be discovered and invoked by any MCP-compatible client (Claude Desktop, LLM frameworks, custom applications). Handles MCP message serialization/deserialization, tool invocation routing, and response formatting according to the MCP standard. Supports both stdio and HTTP transport modes for flexible client integration.
Unique: Adheres to the MCP specification, enabling seamless integration with Claude and other MCP-aware clients without requiring custom protocol adapters or client-specific code
vs alternatives: More interoperable than proprietary integration approaches, and enables future compatibility with new MCP clients as the ecosystem grows
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 proxmox-mcp at 24/100.
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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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