| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes ESLint's core linting engine as an MCP resource/tool, allowing Claude and other MCP clients to invoke ESLint rules against code without spawning separate processes. Implements ESLint's plugin architecture through MCP's tool-calling interface, enabling dynamic rule configuration and multi-file linting workflows within a single MCP session.
Unique: First MCP server to expose ESLint as a native tool-calling interface, eliminating subprocess spawning and enabling stateful linting sessions within MCP's request-response model. Leverages ESLint's plugin architecture directly rather than wrapping CLI output.
vs alternatives: Faster and more composable than invoking ESLint CLI via subprocess calls because it keeps the linting engine resident in the MCP process and integrates with Claude's native tool-calling, avoiding serialization overhead and enabling multi-step linting workflows.
Automatically discovers and loads ESLint configuration files (.eslintrc.js, .eslintrc.json, .eslintrc.yml, eslintrc.config.js) from the project hierarchy, parses them, and applies rule sets and parser options to linting operations. Supports ESLint's cascading configuration model where nested .eslintrc files override parent configs, and handles environment-specific overrides (browser, node, es6, etc.).
Unique: Implements ESLint's full cascading configuration resolution algorithm within the MCP server, allowing it to respect project-specific rule sets without requiring users to re-specify configs. Handles extends chains and environment overrides automatically.
vs alternatives: More intelligent than naive CLI wrapping because it understands ESLint's configuration inheritance model and can apply the correct rule set to any file in the project without user intervention, matching the behavior of ESLint's native config resolution.
Accepts a list of file paths or glob patterns and lints them in parallel using Node.js worker threads or async I/O, returning aggregated results with per-file diagnostics. Implements ESLint's caching layer to avoid re-linting unchanged files, and supports filtering results by severity (error, warning) or rule name.
Unique: Implements parallel linting using Node.js async I/O within the MCP server's event loop, avoiding the overhead of spawning separate ESLint CLI processes. Integrates ESLint's built-in caching to skip re-analysis of unchanged files.
vs alternatives: Faster than running ESLint CLI multiple times because it keeps the linting engine warm in memory and parallelizes file processing, while still respecting ESLint's cache invalidation logic.
Invokes ESLint's built-in --fix mechanism to automatically correct violations that have fix implementations (e.g., semicolon insertion, whitespace normalization). For violations without automatic fixes, generates structured suggestions with before/after code snippets and rule documentation links, enabling Claude to propose manual fixes or ask for user confirmation.
Unique: Exposes ESLint's fix engine through MCP's tool interface, allowing Claude to apply fixes as part of a multi-turn conversation. Generates structured fix suggestions for non-auto-fixable rules by parsing rule metadata and documentation.
vs alternatives: More interactive than running ESLint --fix from the CLI because it allows Claude to preview fixes, ask for confirmation, and apply them selectively, enabling a collaborative code improvement workflow.
Exposes metadata for all loaded ESLint rules (name, description, category, fixable, deprecated status) and provides links to official documentation. Allows filtering rules by category (best-practices, errors, style, etc.) and searching by name or keyword, enabling Claude to explain rules to users and recommend relevant rules for specific code patterns.
Unique: Indexes ESLint's rule definitions at server startup and exposes them as searchable MCP resources, allowing Claude to provide in-context rule explanations without external API calls. Includes deprecated rule detection and migration guidance.
vs alternatives: More efficient than having Claude search ESLint's documentation website because rule metadata is pre-indexed and available instantly, and Claude can provide contextual explanations tailored to the user's code.
Dynamically loads ESLint plugins (e.g., eslint-plugin-react, eslint-plugin-vue) from node_modules and registers their rules with the linting engine. Supports plugin configuration options and namespace-prefixed rules (e.g., react/jsx-uses-react). Validates plugin compatibility with the current ESLint version before loading.
Unique: Implements ESLint's plugin loading mechanism within the MCP server, allowing plugins to be discovered and loaded from the project's node_modules without CLI invocation. Includes version compatibility checking.
vs alternatives: More flexible than static ESLint CLI because it allows plugins to be loaded dynamically based on project configuration, and Claude can work with framework-specific rules (React, Vue, etc.) without separate tool invocations.
Supports multiple ESLint parsers (default Espree, @babel/eslint-parser, @typescript-eslint/parser, vue-eslint-parser, etc.) and automatically selects the appropriate parser based on file extension and ESLint configuration. Handles parser options (ecmaVersion, sourceType, parserOptions) and validates parser compatibility with the target file type.
Unique: Implements ESLint's parser resolution algorithm within the MCP server, automatically selecting the correct parser for each file type based on configuration. Supports all major ESLint parsers (Espree, Babel, TypeScript, Vue, etc.).
vs alternatives: More intelligent than a generic linter because it understands ESLint's parser ecosystem and can lint TypeScript, JSX, Vue, and other non-standard JavaScript variants without user configuration, matching the behavior of ESLint CLI.
Filters linting results by severity level (error, warning, off) and aggregates statistics across multiple files. Supports custom severity mappings (e.g., treat warnings as errors) and generates summary reports with violation counts, most-violated rules, and files with the most issues. Enables Claude to provide targeted feedback based on severity thresholds.
Unique: Implements multi-dimensional filtering and aggregation on linting results, allowing Claude to provide context-aware feedback based on severity, rule category, and file impact. Supports custom severity mappings for organizational standards.
vs alternatives: More flexible than ESLint's built-in --max-warnings flag because it allows real-time filtering and aggregation, enabling Claude to tailor feedback to the user's priorities (e.g., show only errors for a quick review, or show all violations for a detailed audit).
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 59/100 vs @eslint/mcp at 38/100.
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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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