| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes clj-kondo linting capabilities through the Model Context Protocol (MCP), allowing AI models and tools to invoke static analysis on Clojure code without direct subprocess management. Implements MCP server transport layer that wraps clj-kondo's analysis engine, translating linting results into structured JSON responses that conform to MCP resource and tool schemas for seamless integration with Claude, other LLMs, and MCP-compatible clients.
Unique: Bridges clj-kondo (a mature Clojure linter) into the MCP ecosystem, enabling AI models to invoke linting as a first-class tool without subprocess management boilerplate. Uses MCP's resource and tool schemas to expose linting as callable functions rather than requiring models to parse raw CLI output.
vs alternatives: Provides standardized MCP integration for Clojure linting, whereas direct clj-kondo CLI usage requires models to handle subprocess spawning and output parsing, and existing Clojure IDE plugins are editor-specific rather than AI-model-agnostic.
Performs on-demand static analysis of Clojure code to detect syntax errors, style violations, and common mistakes using clj-kondo's rule engine. Parses Clojure source text, applies configurable linting rules (unused variables, incorrect function arity, deprecated APIs, etc.), and returns diagnostics with precise line/column positions and severity levels (error, warning, info). Configuration is read from .clj-kondo/config.edn if present, allowing per-project customization.
Unique: Exposes clj-kondo's mature rule engine (covering 100+ linting rules) through MCP, enabling AI models to validate Clojure code with the same rigor as IDE plugins, but in a model-agnostic, protocol-standardized way. Respects project-level .clj-kondo/config.edn for rule customization.
vs alternatives: More comprehensive than regex-based linting and more accessible than requiring IDE integration; clj-kondo itself is the de-facto Clojure linter, so this MCP wrapper provides the industry standard in an AI-friendly format.
Registers clj-kondo linting as a callable MCP tool with a defined JSON schema, allowing MCP clients (like Claude) to discover, invoke, and handle linting requests as first-class tool calls. Implements MCP's tools/list and tools/call handlers, translating tool invocation parameters (code text, file paths) into clj-kondo subprocess calls and marshaling results back as structured JSON responses. Enables natural language requests like 'lint this code' to be routed to the linting engine without explicit model prompting.
Unique: Implements MCP's tools/list and tools/call protocol handlers to expose clj-kondo as a discoverable, invokable tool. Uses JSON schema to describe tool parameters, enabling clients to understand and invoke linting without hardcoded knowledge of clj-kondo's CLI interface.
vs alternatives: Standardizes linting as an MCP tool, making it discoverable and callable by any MCP client; direct clj-kondo CLI usage requires models to know the exact invocation syntax, whereas MCP schema-based discovery is self-documenting and client-agnostic.
Respects project-level .clj-kondo/config.edn configuration files to customize which linting rules are enabled, disabled, or configured with specific parameters. Reads configuration from the project directory, merges it with clj-kondo's defaults, and applies the resulting rule set during analysis. Supports rule-level configuration such as severity overrides, exclusion patterns, and rule-specific options (e.g., max function arity warnings).
Unique: Leverages clj-kondo's native configuration system (.clj-kondo/config.edn) to allow per-project rule customization without modifying the MCP server. Configuration is read at linting time, enabling teams to enforce project-specific standards.
vs alternatives: Provides configuration flexibility comparable to IDE-based linting, whereas hardcoded linting rules would require server code changes to customize; respects the Clojure ecosystem's standard configuration format.
Accepts file paths or directory paths as input and performs linting on multiple Clojure files in a single MCP call. Recursively traverses directories, identifies .clj, .cljs, and .cljc files, and returns aggregated diagnostics for all files with file-level grouping. Enables efficient bulk analysis of codebases without requiring separate tool calls per file.
Unique: Wraps clj-kondo's batch analysis capability in MCP, allowing single tool calls to lint entire directories. Aggregates results with file-level grouping, enabling efficient codebase-wide analysis without per-file MCP overhead.
vs alternatives: More efficient than invoking linting separately for each file; provides codebase-wide analysis in a single MCP call, reducing latency and simplifying client logic compared to manual file enumeration and sequential linting.
Returns linting results as structured JSON with detailed diagnostic objects including file path, line number, column number, rule name, message, and severity level (error, warning, info). Each diagnostic is a discrete object with all metadata needed for programmatic handling, enabling clients to filter, sort, or aggregate violations by severity, rule type, or file. Severity levels align with LSP (Language Server Protocol) conventions for compatibility with IDE tooling.
Unique: Exposes clj-kondo's diagnostic output as structured JSON with LSP-compatible severity levels, enabling programmatic filtering and aggregation. Each diagnostic includes full metadata (file, line, column, rule name, message) for rich client-side handling.
vs alternatives: More structured than raw CLI output; JSON format enables easy parsing and filtering, whereas plain-text linting output requires regex parsing and is fragile to format changes.
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 clj-kondo-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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