| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 15 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
Establishes bidirectional communication between LLM clients (Claude Desktop, VS Code Copilot, Cursor IDE) and MongoDB instances through the Model Context Protocol using either stdio or HTTP transports. The server implements a four-layer architecture separating transport handling, server orchestration, tool execution, and external service integration, enabling seamless tool invocation without custom client-side integration code.
Unique: Official MongoDB implementation of MCP with dual transport support (stdio and HTTP) and four-layer architecture that cleanly separates transport concerns from tool execution, enabling deployment flexibility without client-side code changes
vs alternatives: As the official MongoDB MCP server, it provides tighter integration with MongoDB's native APIs and Atlas infrastructure than third-party MCP implementations, with built-in support for vector search and Atlas-specific operations
Executes parameterized MongoDB find() queries against collections with support for filtering, projection, sorting, and pagination. The implementation uses the MongoDB Node.js driver's native find() API with automatic cursor management, enabling efficient streaming of large result sets through the MCP resource export mechanism to avoid protocol message size limits.
Unique: Integrates MongoDB's native cursor streaming with MCP resource export mechanism, automatically offloading large result sets to prevent protocol message size violations while maintaining transparent access patterns
vs alternatives: Handles result set size constraints more elegantly than REST API wrappers by leveraging MCP's resource URI scheme, enabling seamless access to large collections without client-side pagination logic
MongoDB MCP Server scores higher at 59/100 vs firebase-mcp at 32/100. firebase-mcp leads on ecosystem, while MongoDB MCP Server is stronger on adoption and quality.
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Manages MongoDB Atlas Vector Search indexes for semantic search operations, including index creation with embedding field specifications and vector search query execution. The implementation integrates with the aggregation pipeline's $vectorSearch stage, enabling LLMs to build RAG systems that combine vector similarity search with traditional MongoDB queries.
Unique: Integrates MongoDB Atlas Vector Search index management and querying into MCP tools, enabling LLMs to autonomously build and query semantic search indexes without manual Atlas UI interactions, with full aggregation pipeline integration
vs alternatives: Provides end-to-end vector search capabilities through MCP tools, eliminating the need for separate vector database clients or custom embedding management code, enabling RAG systems built entirely through natural language prompts
Exports large query results to MCP resources (accessible via exported-data:// URIs) to circumvent protocol message size limits. The implementation stores result sets in memory or temporary storage and exposes them through MCP's resource mechanism, enabling LLMs to retrieve large datasets through separate resource access calls without overwhelming the tool response channel.
Unique: Leverages MCP's resource URI scheme to transparently handle result sets exceeding protocol message limits, enabling seamless access to large MongoDB collections without client-side pagination logic or message fragmentation
vs alternatives: Provides a cleaner abstraction for large result handling than REST API pagination by using MCP's native resource mechanism, eliminating the need for custom pagination logic in LLM prompts
Exposes server configuration and connection diagnostics through MCP resources (config:// and debug://mongodb URIs). The implementation provides current configuration with secrets redacted and last connectivity attempt information, enabling LLMs to diagnose connection issues and verify server setup without direct log access.
Unique: Provides secure configuration inspection through MCP resources with automatic secret redaction, enabling LLMs to diagnose issues without exposing sensitive credentials in tool responses
vs alternatives: Offers safer configuration debugging than direct log access by automatically redacting secrets and providing structured diagnostic information through MCP resources
Manages database and collection context across multiple tool invocations through session-based state management. The implementation maintains per-session configuration including current database and collection selections, enabling LLMs to work with multiple databases and collections without repeating context in every tool call.
Unique: Implements session-based context management that isolates database and collection selections per LLM session, enabling multi-database workflows without explicit context parameters in every tool call
vs alternatives: Reduces prompt engineering overhead by maintaining implicit context across tool calls, enabling more natural LLM interactions with MongoDB without verbose parameter passing
Implements a type-safe tool framework in TypeScript with automatic parameter validation and schema generation. The framework uses TypeScript interfaces to define tool parameters, automatically generates JSON schemas for MCP protocol compliance, and validates inputs at runtime, enabling type-safe tool development without manual schema management.
Unique: Provides a TypeScript-first tool framework that automatically generates MCP schemas from type definitions, eliminating manual schema management and enabling type-safe tool development with minimal boilerplate
vs alternatives: Reduces schema maintenance burden compared to manual JSON schema definitions by deriving schemas from TypeScript types, enabling developers to focus on tool logic rather than schema synchronization
Executes MongoDB aggregation pipelines with support for all standard stages ($match, $group, $project, $sort, etc.) and specialized stages like $vectorSearch for semantic search operations. The implementation passes pipeline definitions directly to MongoDB's aggregate() method, enabling complex multi-stage transformations and vector similarity searches on Atlas Vector Search indexes without intermediate result materialization.
Unique: Native support for $vectorSearch stage enables semantic search directly within aggregation pipelines, allowing LLMs to compose complex retrieval workflows combining vector similarity with traditional filtering and transformations in a single operation
vs alternatives: Eliminates the need for separate vector search clients or post-processing logic by embedding vector operations into MongoDB's aggregation framework, reducing latency and simplifying LLM prompt engineering for RAG systems
+7 more capabilities