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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes Firestore read, write, update, and delete operations as standardized MCP tools that AI clients can invoke. The FirebaseMcpServer class registers individual tool handlers (firestore_add_document, firestore_get_document, firestore_update_document, firestore_delete_document) that map directly to Firestore SDK methods, with schema-based parameter validation and error handling that converts Firebase exceptions into structured MCP responses. Each tool accepts collection path and document data as parameters, executes the operation against the initialized Firebase instance, and returns typed results (document IDs, success confirmations, or error details).
Unique: Implements Firestore operations as discrete MCP tools with schema-based parameter validation and structured error handling, allowing AI clients to perform database operations through a standardized tool-calling interface rather than direct SDK access. The tool registry pattern (src/index.ts 477-1334) enables fine-grained permission control per operation type.
vs alternatives: Provides safer, more auditable Firestore access than direct SDK exposure because each operation is a registered tool with explicit schema validation, whereas direct Firebase SDK access in AI contexts risks uncontrolled data mutations.
Implements firestore_list_documents and firestore_list_collections tools that traverse Firestore collection hierarchies and return paginated document snapshots. The implementation queries collections using the Firestore SDK, optionally applies client-side filtering based on field predicates passed as parameters, and returns structured arrays of documents with metadata. The tool supports nested collection discovery (listing subcollections within documents) and basic field-based filtering without requiring complex WHERE clause syntax, making it accessible to AI clients that may not be familiar with Firestore query syntax.
Unique: Provides simplified collection listing and field-based filtering as MCP tools, abstracting away Firestore's query syntax complexity. The implementation uses client-side filtering (src/index.ts) rather than server-side WHERE clauses, making it more accessible to AI clients but less performant on large datasets.
vs alternatives: Easier for AI agents to use than raw Firestore queries because it exposes simple field-matching as tool parameters, whereas direct Firestore SDK requires understanding query builder syntax that LLMs may struggle with.
Implements storage_list_files tool that enumerates files in a Firebase Storage bucket with optional path prefix filtering. The tool queries the Storage bucket using the Admin SDK's listFiles() method, optionally filters results by a path prefix (e.g., 'uploads/2024/'), and returns an array of file metadata including name, size, creation date, and content type. The implementation supports pagination through a maxResults parameter, allowing large buckets to be enumerated incrementally. Results are returned as structured objects with file paths and metadata, enabling AI clients to discover and analyze bucket contents.
Unique: Provides bucket enumeration with prefix filtering as an MCP tool, enabling AI clients to discover Storage contents without direct SDK access. The implementation uses Firebase Admin SDK's listFiles() method with optional prefix filtering.
vs alternatives: More discoverable than direct SDK access because it abstracts bucket enumeration into a tool with clear parameters, whereas raw SDK requires understanding pagination tokens and file object structures.
Implements firestore_add_document tool that creates new documents in Firestore collections with either auto-generated or specified document IDs. The tool accepts a collection path and document data, and optionally a document ID. If no ID is provided, Firestore generates a unique ID automatically using its ID generation algorithm. The implementation uses the Firestore SDK's add() method (for auto-ID) or set() method (for specified IDs), both of which are atomic operations. The tool returns the generated or specified document ID and optionally the full document snapshot, enabling AI clients to reference newly created documents.
Unique: Exposes Firestore's document creation with both auto-generated and specified IDs as an MCP tool, allowing AI clients to create documents and receive generated IDs for subsequent operations. The implementation uses Firestore's add() and set() methods appropriately.
vs alternatives: More convenient than direct SDK usage because the tool handles ID generation and returns the ID in the response, whereas raw SDK requires separate calls to get the generated ID.
Exposes Firebase Storage operations (storage_upload_file, storage_download_file, storage_list_files) as MCP tools that handle file I/O through the Storage SDK. The upload tool accepts base64-encoded file content and a destination path, writes to Storage, and returns a public download URL. The download tool retrieves files by path and returns base64-encoded content. The list tool enumerates files in a Storage bucket with optional path prefix filtering. All operations include error handling for authentication failures, missing files, and quota exceeded scenarios, with results formatted as structured MCP responses.
Unique: Implements Storage operations as MCP tools with base64 content encoding, allowing AI clients to handle binary files through text-based tool parameters. The approach trades efficiency for compatibility with text-only MCP transports, enabling file operations in environments where binary protocols aren't available.
vs alternatives: Safer than exposing Storage SDK directly because file operations are mediated through registered tools with explicit parameter validation, whereas direct SDK access could allow uncontrolled file deletion or overwriting.
Exposes Firebase Authentication operations (auth_get_user, auth_list_users) as MCP tools that query the Firebase Auth service. The get_user tool retrieves a specific user's profile by UID or email, returning user metadata (creation date, last sign-in, email verification status, custom claims). The list_users tool enumerates all users in the project with pagination support. Both tools return sanitized user data (no password hashes or sensitive credentials) and include error handling for missing users or permission issues. The implementation uses the Firebase Admin SDK's Auth module to access user records.
Unique: Provides read-only access to Firebase Auth user metadata through MCP tools, sanitizing sensitive fields and exposing only user profile information. The implementation uses the Firebase Admin SDK's Auth module (src/index.ts) to query user records without exposing credential management capabilities.
vs alternatives: Safer than exposing Auth SDK directly because it restricts operations to read-only queries and sanitizes responses, whereas direct SDK access could allow credential modification or user deletion.
Implements a transport layer that supports both HTTP and STDIO protocols for MCP communication, allowing the Firebase MCP server to integrate with different AI client architectures. The server initializes with a configurable transport mechanism (via environment variable or constructor parameter), handles protocol-specific serialization/deserialization, and manages connection lifecycle. HTTP transport exposes the MCP server on a specified port with standard HTTP request/response handling, while STDIO transport reads from stdin and writes to stdout, enabling integration with CLI-based AI tools and local development environments. The transport abstraction is handled by the MCP SDK, with the Firebase server providing configuration and tool registration.
Unique: Provides dual-transport support (HTTP and STDIO) through MCP SDK abstraction, allowing the same Firebase tool registry to serve both network-based clients (Claude Desktop, Cursor) and local CLI tools. The transport selection is environment-driven, enabling deployment flexibility without code changes.
vs alternatives: More flexible than single-transport implementations because it supports both network and local communication patterns, whereas Firebase SDK alone requires direct code integration without protocol abstraction.
Handles Firebase project initialization by reading service account credentials from environment variables or configuration files and initializing the Firebase Admin SDK. The FirebaseMcpServer constructor accepts a Firebase config object or reads from GOOGLE_APPLICATION_CREDENTIALS environment variable, validates the configuration, and initializes Firestore, Storage, and Auth service instances. The implementation follows Firebase Admin SDK patterns, creating singleton service instances that are reused across all tool handlers. Error handling includes validation of credential format, project ID verification, and graceful failure if Firebase services are unavailable.
Unique: Implements Firebase initialization through environment-driven configuration, allowing credential management without code changes. The approach uses Firebase Admin SDK's standard initialization patterns (src/index.ts 96-124) with support for both explicit config objects and GOOGLE_APPLICATION_CREDENTIALS environment variable.
vs alternatives: More secure than hardcoding credentials because it externalizes credential management to environment variables, whereas embedding credentials in code or configuration files creates security risks.
+4 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 59/100 vs firebase-mcp at 32/100. firebase-mcp 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