Firecrawl MCP Server ranks higher at 62/100 vs Confluence MCP Server at 60/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Confluence MCP Server | Firecrawl MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 60/100 | 62/100 |
| Adoption | 1 | 1 |
| Quality |
| 1 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Enables semantic and keyword-based search across Confluence pages using the Confluence Search API with support for CQL (Confluence Query Language) filters. The implementation wraps the native Confluence search endpoint with parameter marshaling for space filtering, content type restrictions, and result pagination, returning structured page metadata including page IDs, titles, spaces, and excerpt snippets. Supports both Cloud and Server/Data Center deployments with automatic API format adaptation.
Unique: Abstracts Confluence Search API differences between Cloud and Server/Data Center deployments through automatic format adaptation, enabling single-codebase support for both deployment models while exposing CQL filtering capabilities that most MCP servers omit entirely.
vs alternatives: Provides native CQL filtering support and multi-deployment compatibility that generic REST API wrappers lack, enabling more precise documentation retrieval than simple keyword search.
Enables AI agents to create new Confluence pages with full support for parent-child page hierarchies, content formatting (HTML/XHTML), and metadata assignment. The implementation uses the Confluence Content API with automatic body format detection (storage format for Server/Data Center, rich text format for Cloud), parent page resolution, and space assignment. Supports inline attachments, labels, and custom properties through the underlying Confluence API schema.
Unique: Implements automatic body format detection and conversion between Cloud (rich text) and Server/Data Center (storage format) deployments, allowing single tool invocations to work across both platforms without client-side format negotiation.
vs alternatives: Handles hierarchical page creation with parent resolution in a single API call, whereas generic Confluence API clients require manual parent ID lookup and separate calls for page creation.
Registers 72 Confluence and Jira tools with FastMCP framework using dependency injection pattern for client instantiation and configuration. The implementation uses FastMCP's tool decorator system to expose tools with automatic schema generation, parameter validation, and error handling. Supports tool access control through permission decorators and conditional tool availability based on deployment type or authentication method.
Unique: Uses FastMCP's decorator-based tool registration with dependency injection for client instantiation, enabling automatic schema generation and parameter validation without manual tool definition boilerplate.
vs alternatives: Provides automatic tool schema generation and dependency injection, whereas manual MCP implementations require explicit schema definition and client instantiation logic.
Enables creating Jira issues with full support for custom fields, issue types, projects, and field validation. The implementation uses the Jira Create Issue API with automatic field schema discovery, type coercion, and validation error reporting. Supports both Cloud and Server/Data Center with field name mapping and custom field ID resolution.
Unique: Implements automatic field schema discovery and type coercion with custom field ID resolution, enabling issue creation with custom fields without manual field ID lookup or type specification.
vs alternatives: Provides field validation and custom field support in a single API call, whereas generic Jira API clients require manual field schema lookup and separate validation calls.
Enables searching Jira issues using JQL (Jira Query Language) with support for complex filters, sorting, and result pagination. The implementation wraps the Jira Search API with JQL validation, result expansion (issue details, changelog, comments), and automatic pagination handling. Supports both Cloud and Server/Data Center with JQL dialect adaptation.
Unique: Implements JQL query execution with automatic result expansion and pagination handling, enabling single API calls to fetch complex issue sets with full metadata without manual pagination logic.
vs alternatives: Provides JQL search with automatic result expansion and pagination, whereas generic Jira API clients expose raw search endpoints requiring manual pagination and expansion parameter management.
Enables updating Jira issues (fields, status, assignee) and executing workflow transitions with automatic permission checking and field validation. The implementation uses the Jira Update Issue API with support for bulk field updates, transition execution with required field validation, and changelog tracking. Supports both Cloud and Server/Data Center with field name mapping.
Unique: Implements workflow transition execution with automatic required field validation, enabling single API calls to transition issues through workflow states without separate validation calls.
vs alternatives: Provides workflow transition support with field validation, whereas generic Jira API clients expose raw update endpoints requiring manual transition lookup and field validation.
Retrieves full page content from Confluence with automatic format normalization across Cloud and Server/Data Center deployments. The implementation fetches pages via the Content API with expand parameters for body content, metadata, and version history, then normalizes storage format (Server/DC) or rich text format (Cloud) into a consistent representation. Includes support for retrieving page hierarchies, child pages, and version history metadata.
Unique: Implements automatic format normalization between Cloud (rich text) and Server/Data Center (storage format) deployments, exposing a unified content representation to clients regardless of backend platform, eliminating the need for format-specific parsing logic.
vs alternatives: Provides transparent format conversion and version history retrieval in a single call, whereas direct Confluence API clients require manual format detection and separate API calls for version metadata.
Enables navigation and discovery of Confluence space structures, page hierarchies, and content organization through tools that list spaces, retrieve page trees, and resolve parent-child relationships. The implementation uses the Confluence Content API with recursive expansion parameters to fetch page hierarchies, space metadata, and permission information. Supports filtering by space type (global, personal) and pagination for large hierarchies.
Unique: Provides recursive page hierarchy expansion with automatic parent-child relationship resolution, allowing single API calls to fetch multi-level page trees rather than requiring separate calls per hierarchy level.
vs alternatives: Exposes space and page hierarchy traversal as first-class operations, whereas generic Confluence API clients require manual recursive calls and client-side tree construction.
+6 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 Confluence MCP Server at 60/100.
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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