MongoDB MCP Server ranks higher at 62/100 vs yfinance-mcp-server at 25/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | yfinance-mcp-server | MongoDB MCP Server |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 25/100 | 62/100 |
| Adoption | 0 | 1 |
| Quality |
| 0 |
| 1 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Exposes yfinance's stock ticker data fetching through MCP server tools, allowing Claude and other MCP clients to query current and historical stock prices by ticker symbol. Implements MCP tool schema binding that translates natural language requests into yfinance API calls, handling ticker validation and price data serialization back to the client as structured JSON responses.
Unique: Bridges yfinance (Python financial data library) directly into MCP protocol as callable tools, eliminating the need for custom REST API wrappers or direct library imports in client code. Uses MCP's tool schema system to expose yfinance methods as first-class client capabilities.
vs alternatives: Simpler than building a custom REST API wrapper around yfinance; tighter integration with Claude and MCP ecosystem than calling yfinance directly from Python scripts
Retrieves multi-period historical OHLCV (open, high, low, close, volume) data for a given ticker and date range, aggregating yfinance responses into structured time-series format. Handles date range validation, period granularity selection (daily, weekly, monthly), and formats output as JSON arrays or CSV-compatible structures suitable for analysis or downstream processing.
Unique: Exposes yfinance's period-based data fetching (daily, weekly, monthly) as MCP tools with automatic date range validation and format conversion, allowing clients to request historical data without managing yfinance's pandas DataFrame output directly.
vs alternatives: More flexible than static data exports; allows dynamic date range queries within MCP conversations vs. pre-computed CSV files
Fetches company-level metadata and fundamental metrics (market cap, P/E ratio, dividend yield, sector, industry, 52-week high/low) from yfinance's Ticker object, exposing these as MCP tools. Implements lazy-loading of ticker info to minimize network requests, caching metadata within a single MCP session, and serializing complex objects (e.g., company info dictionaries) into JSON-safe formats.
Unique: Wraps yfinance's Ticker.info dictionary (which returns inconsistent, nested JSON) into a normalized MCP tool schema with optional field filtering, allowing clients to request specific fundamentals without handling yfinance's raw data structure.
vs alternatives: Simpler than parsing yfinance's raw info dict in client code; more complete than REST APIs that only expose price data
Retrieves historical dividend payments and earnings dates for a ticker using yfinance's dividends and earnings attributes, formatting them as time-indexed JSON arrays. Handles missing data gracefully (some tickers have no dividend history), validates date ranges, and provides both raw dividend amounts and calculated metrics like dividend yield and payout frequency.
Unique: Exposes yfinance's dividends and earnings Series objects as queryable MCP tools with automatic date filtering and yield calculation, avoiding the need for clients to manipulate pandas Series directly.
vs alternatives: More accessible than raw yfinance API for dividend queries; integrated into MCP workflow vs. separate dividend data source
Retrieves historical stock splits and corporate actions (reverse splits, mergers, spinoffs) from yfinance's splits attribute, providing adjusted share counts and split ratios. Implements date-indexed lookup allowing clients to understand historical share count changes and their impact on price comparisons across split events.
Unique: Surfaces yfinance's splits Series as a queryable MCP tool with automatic ratio calculation and date indexing, enabling agents to understand and adjust for historical corporate actions without manual data wrangling.
vs alternatives: More transparent than pre-adjusted price data; integrated into MCP workflow vs. requiring external corporate action databases
Enables MCP clients to request data for multiple tickers in a single logical operation by composing individual ticker tools into batch queries, handling parallel or sequential fetching depending on MCP client implementation. Implements error handling per ticker (one failure doesn't block others) and aggregates results into a unified response structure suitable for comparative analysis.
Unique: Leverages MCP's tool-calling protocol to enable batch queries without implementing a custom batch endpoint; clients compose multiple ticker tools into a single logical batch operation, with error handling per ticker.
vs alternatives: More flexible than a single batch endpoint; allows clients to mix and match tools (price, fundamentals, dividends) per ticker without predefined batch schemas
Implements the Model Context Protocol (MCP) server specification, automatically generating tool schemas for all yfinance capabilities and exposing them via MCP's tool-calling interface. Handles MCP request/response serialization, tool discovery, and parameter validation according to MCP spec, allowing any MCP-compatible client (Claude, custom agents) to discover and invoke yfinance tools without prior knowledge of their signatures.
Unique: Implements full MCP server specification with automatic tool schema generation from yfinance methods, enabling zero-configuration integration with MCP clients; uses MCP's standardized tool discovery and invocation protocol rather than custom REST or gRPC APIs.
vs alternatives: More standardized than custom REST wrappers; tighter integration with Claude and MCP ecosystem than direct yfinance imports
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 yfinance-mcp-server at 25/100.
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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