| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Exposes geographic data APIs through the Model Context Protocol, enabling Claude and other MCP-compatible assistants to query location information, coordinates, and place names via standardized tool schemas. Implements MCP resource and tool handlers that translate natural language requests into structured geographic queries, returning GeoJSON-compatible location objects with latitude, longitude, and administrative boundaries.
Unique: Implements geographic lookup as a first-class MCP tool with bidirectional geocoding (address→coordinates and coordinates→address) rather than wrapping a generic HTTP client, enabling Claude to reason about location context natively within conversation flow
vs alternatives: Tighter integration with MCP protocol than generic API wrapper tools, allowing Claude to invoke geographic queries with natural language without explicit JSON formatting or API key management by the user
Provides real-time currency exchange rates through MCP tool handlers that accept source and target currency codes, returning current bid/ask spreads and conversion rates. Implements caching at the MCP server level to reduce redundant API calls within a conversation session, with configurable TTL for rate freshness. Supports multi-currency queries and historical rate context when available from the upstream Apogeio API.
Unique: Implements server-side rate caching with configurable TTL within the MCP server process, reducing API calls for repeated currency queries in the same conversation without requiring Claude to manage cache state
vs alternatives: More efficient than calling a generic HTTP tool for each rate lookup because the MCP server maintains session-level cache, reducing latency and API quota consumption compared to stateless tool wrappers
Exposes IP geolocation capabilities through MCP tools that accept IPv4 or IPv6 addresses and return geographic location (country, city, coordinates), autonomous system number (ASN), ISP name, and connection type. Uses the Apogeio IP geolocation API backend with structured response parsing, enabling Claude to understand network origin and infrastructure context. Supports batch IP lookups within a single tool invocation for efficiency.
Unique: Combines three data dimensions (geographic location, ASN, ISP) in a single MCP tool response rather than requiring separate API calls, enabling Claude to correlate network infrastructure with location in a single reasoning step
vs alternatives: More comprehensive than standalone IP lookup tools because it includes ASN and ISP resolution alongside geolocation, providing network context that generic geolocation APIs omit
Implements a fully-compliant MCP server that registers geographic, exchange rate, and IP geolocation tools using the MCP tool schema specification. Handles MCP protocol handshake, resource discovery, and tool invocation lifecycle. Built with proper error handling, request validation, and response serialization to ensure compatibility with Claude Desktop, Cursor, and other MCP hosts. Supports both stdio and HTTP transport modes for flexible deployment.
Unique: Provides a reference implementation of MCP server architecture with proper lifecycle management, error handling, and transport abstraction, rather than a minimal proof-of-concept
vs alternatives: More production-ready than example MCP servers because it includes proper validation, error recovery, and support for both stdio and HTTP transports, reducing integration friction for Claude Desktop and Cursor users
Registers the apogeoapi-mcp server with the Smithery MCP package registry, enabling one-click installation in Claude Desktop and Cursor via the registry UI. Implements standardized package metadata (name, version, description, dependencies) and configuration schema that Smithery uses for automated server discovery and installation. Supports automatic updates through Smithery's version management system.
Unique: Leverages Smithery's standardized package format and auto-discovery mechanism, enabling one-click installation without manual configuration files or environment variable setup
vs alternatives: Simpler distribution than manual MCP server setup because Smithery handles dependency resolution, version management, and configuration schema validation automatically
Implements MCP protocol compliance that works across Claude Desktop, Cursor, and other MCP-compatible AI assistants through abstraction of client-specific transport and capability negotiation. Detects client capabilities during handshake and adapts tool schemas and response formats accordingly. Handles version negotiation and graceful degradation for older MCP protocol versions.
Unique: Implements client capability detection and graceful degradation rather than assuming a single client implementation, enabling the same server binary to work across Claude Desktop, Cursor, and future MCP hosts
vs alternatives: More portable than client-specific MCP servers because it negotiates capabilities at runtime rather than hardcoding assumptions about client features
Implements comprehensive input validation and error handling for all MCP tool invocations, including type checking, parameter bounds validation, and API error translation. Returns structured error responses with actionable error messages and retry guidance. Validates IP addresses, currency codes, and geographic coordinates before forwarding to upstream APIs, reducing unnecessary API calls and providing immediate feedback to Claude.
Unique: Performs pre-flight validation before calling upstream APIs (e.g., IP address format check, currency code validation) rather than relying solely on API error responses, reducing latency and API quota waste
vs alternatives: More efficient than naive API forwarding because it catches invalid inputs locally before making network calls, improving user experience and reducing API costs
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 62/100 vs apogeoapi-mcp at 46/100. apogeoapi-mcp leads on adoption, while MongoDB MCP Server is stronger on quality and ecosystem.
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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