Firecrawl MCP Server ranks higher at 62/100 vs mcp-edgartools-lite at 31/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | mcp-edgartools-lite | Firecrawl MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 31/100 | 62/100 |
| Adoption | 0 | 1 |
| Quality |
| 0 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Resolves company identifiers (ticker symbols or Central Index Key numbers) to SEC EDGAR database records and retrieves metadata about the company including name, industry classification, and filing history. Uses the EDGAR REST API to perform lookups and aggregates company profile information from official SEC sources without requiring manual CIK translation.
Unique: Integrates SEC EDGAR API directly via MCP protocol, eliminating need for separate financial data APIs or manual CIK lookups; handles both ticker and CIK inputs transparently
vs alternatives: Faster than manual EDGAR searches and more cost-effective than commercial financial data APIs for basic company lookup and filing retrieval
Parses SEC 10-K filings (annual reports) and extracts specific sections like business description, risk factors, management discussion & analysis (MD&A), and financial statements using document structure parsing. Implements section-aware extraction that maps EDGAR HTML/text formatting to logical document sections, allowing targeted retrieval without downloading entire multi-hundred-page filings.
Unique: Implements section-aware parsing that maps SEC item numbering (Item 1A for Risk Factors, Item 7 for MD&A) to extraction logic, avoiding full-document downloads and enabling targeted analysis of specific disclosure categories
vs alternatives: More efficient than downloading and manually parsing full 10-K PDFs; more targeted than general document summarization tools that lack SEC filing structure awareness
Extracts targeted sections from SEC 10-Q quarterly reports (unaudited interim financial statements and MD&A) using the same section-aware parsing as 10-K extraction but optimized for quarterly disclosure patterns. Handles condensed financial statements and interim MD&A that differ structurally from annual reports, enabling quarterly performance monitoring without full document review.
Unique: Optimizes section extraction for quarterly filing structure (condensed statements, interim MD&A) rather than treating 10-Qs as mini-10-Ks; handles quarterly-specific item numbering and formatting variations
vs alternatives: More accurate for quarterly analysis than generic 10-K extraction tools; faster than manual quarterly report review for monitoring dashboards
Parses SEC 8-K filings (current reports of material events) and extracts event type, date, and description using item-based parsing that maps SEC Item codes (Item 1.01 for bankruptcy, Item 5.02 for executive changes, etc.) to event categories. Enables rapid identification of material corporate events without reading full 8-K documents, supporting real-time monitoring of significant developments.
Unique: Maps SEC 8-K item codes to event categories (bankruptcy, executive changes, asset sales, etc.), enabling structured event extraction rather than free-text parsing; supports real-time monitoring of material corporate events
vs alternatives: Faster than news-based event detection for official SEC disclosures; more reliable than press release parsing because 8-Ks use standardized item numbering
Extracts insider trading activity (Form 4 filings) including officer/director name, transaction type (purchase/sale), shares transacted, price, and date using SEC filing parsing. Aggregates insider transactions to surface trading patterns and identify significant insider buying or selling activity that may signal management confidence or concerns about company valuation.
Unique: Parses Form 4 filings to extract structured insider transaction data (name, title, transaction type, shares, price) rather than just flagging insider activity; aggregates transactions to identify patterns and significant moves
vs alternatives: More detailed than basic insider trading alerts; provides structured data for quantitative analysis rather than just notifications
Implements request batching and local caching of SEC EDGAR filings to reduce API calls and improve performance when analyzing multiple companies or historical filings. Uses MCP protocol to manage state across requests, storing recently accessed filings in memory and implementing intelligent cache invalidation based on SEC filing update frequency.
Unique: Implements MCP-native caching layer that persists across requests within a session, reducing SEC API calls by 60-80% for typical multi-company analysis workflows; includes intelligent cache invalidation based on filing frequency
vs alternatives: More efficient than naive per-request API calls; simpler than building external cache infrastructure for small-to-medium scale analysis
Correlates data across multiple SEC forms (10-K, 10-Q, 8-K, Form 4) for a single company to construct a timeline of business developments, financial changes, and insider activity. Implements temporal alignment and deduplication logic to surface relationships between events (e.g., linking executive departures from 8-K to compensation changes in proxy statements) and identify material developments that span multiple filings.
Unique: Implements temporal alignment and deduplication across multiple SEC form types to construct unified company timelines; correlates events across 10-K, 10-Q, 8-K, and Form 4 to surface relationships and material developments
vs alternatives: More comprehensive than single-form analysis; enables narrative-driven due diligence that would require manual document review without this capability
Provides a natural language query interface that translates user questions into targeted SEC filing searches and extractions. Uses LLM-based query understanding to map questions like 'What are the main risks?' to specific 10-K sections (Item 1A Risk Factors) and returns extracted content formatted as direct answers rather than raw filing text.
Unique: Translates natural language questions to SEC item-specific queries using LLM understanding, then extracts and formats answers from targeted sections rather than performing full-document search or summarization
vs alternatives: More intuitive than manual SEC filing navigation; more accurate than generic document QA because it understands SEC filing structure and item numbering
+1 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 mcp-edgartools-lite at 31/100. mcp-edgartools-lite leads on ecosystem, while Firecrawl MCP Server is stronger on adoption and quality.
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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