| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 18 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Discovers and enumerates tokenized yield vaults across Ethereum and Base networks by querying on-chain vault registries and metadata contracts. Implements network-aware discovery that differentiates between mainnet and Layer 2 deployments, returning vault addresses, underlying asset compositions, and current TVL metrics through MCP tool bindings that abstract RPC calls.
Unique: Provides MCP-native vault enumeration that abstracts away RPC complexity and chain-specific contract ABIs, enabling LLM agents to discover vaults without writing Solidity or Web3.js code. Dual-chain support (Ethereum + Base) in a single tool call.
vs alternatives: Simpler than building custom Web3 indexers or querying The Graph subgraphs; faster than manual vault research because it returns structured, queryable vault metadata in a single MCP call.
Retrieves historical and real-time performance metrics for vaults including APY, yield distribution history, fee structures, and asset allocation changes. Queries on-chain vault state and event logs to compute performance indicators, then normalizes data into standardized metrics accessible via MCP tools. Handles multi-token vault compositions and fee deductions in APY calculations.
Unique: Computes normalized APY metrics across multi-token vaults by parsing on-chain events and state, abstracting away complex fee and yield distribution logic. Integrates fee deductions directly into APY calculations rather than reporting gross yield separately.
vs alternatives: More accurate than static APY displays because it recalculates based on actual on-chain events; more accessible than writing custom subgraph queries because metrics are pre-computed and standardized.
Estimates gas costs for vault operations (deposits, withdrawals, approvals) by simulating transactions and querying current gas prices. Returns gas estimates in wei and USD equivalent using current ETH/USD prices. Supports gas price overrides for custom priority levels.
Unique: Simulates vault operations to estimate gas costs and converts to USD using current ETH prices in a single call. Breaks down gas costs by operation step (approval, deposit, etc.) to help callers understand where costs are incurred.
vs alternatives: More accurate than static gas estimates because it simulates actual transactions; more convenient than manual gas calculations because it handles multi-step operations and USD conversion.
Assesses vault risks including smart contract risk, liquidity risk, concentration risk, and governance risk. Scores vaults on multiple risk dimensions and provides a composite risk score. Analyzes vault TVL, asset concentration, governance structure, and audit status to produce risk metrics.
Unique: Computes multi-dimensional risk scores (smart contract, liquidity, concentration, governance) from on-chain data and produces a composite risk score. Enables risk-aware vault filtering without requiring manual risk analysis.
vs alternatives: More comprehensive than simple TVL-based risk assessment because it evaluates multiple risk dimensions; more accessible than building custom risk models because it returns pre-computed risk scores.
Analyzes historical vault performance over multiple time horizons (1 week, 1 month, 3 months, 1 year, etc.) by computing returns, volatility, and Sharpe ratios. Queries historical share prices and yield distributions to compute performance metrics. Supports comparison of vault performance against benchmarks or other vaults.
Unique: Computes multi-horizon performance metrics (returns, volatility, Sharpe ratio) from historical share prices and yield distributions. Enables performance comparison across vaults without requiring manual calculation.
vs alternatives: More comprehensive than simple APY displays because it shows volatility and risk-adjusted returns; more accessible than building custom performance analysis tools because it returns pre-computed metrics.
Simulates vault rebalancing scenarios to show how asset allocation changes would impact returns and fees. Allows users to specify target allocations and simulates the rebalancing process including transaction costs and slippage. Returns projected outcomes and cost analysis for proposed rebalancing.
Unique: Simulates rebalancing transactions and cost impact in a single call, allowing callers to evaluate rebalancing decisions before execution. Breaks down costs by component (gas, slippage) to help optimize rebalancing strategy.
vs alternatives: More transparent than manual rebalancing because it shows projected costs and outcomes; more efficient than trial-and-error rebalancing because it simulates multiple strategies.
Generates recommendations for optimizing yield by analyzing user's current vault positions and suggesting alternative vaults or rebalancing actions. Uses multi-vault comparison, performance analysis, and fee optimization to identify opportunities for improved risk-adjusted returns. Ranks recommendations by potential impact and implementation cost.
Unique: Generates yield optimization recommendations by analyzing user's current positions and comparing against alternative vaults using multi-dimensional metrics (APY, fees, risk, liquidity). Ranks recommendations by projected impact and implementation cost.
vs alternatives: More personalized than generic vault rankings because it considers user's current positions; more actionable than simple performance comparisons because it provides specific recommendations with projected outcomes.
Aggregates vault positions across Ethereum and Base into a unified view with cross-chain portfolio metrics. Queries vault holdings on both chains, converts to common reference currency, and provides consolidated portfolio analytics. Handles chain-specific gas costs and liquidity considerations in recommendations.
Unique: Aggregates vault positions across Ethereum and Base in a single call, normalizing to common currency and providing unified portfolio metrics. Handles chain-specific considerations (gas costs, liquidity) in recommendations.
vs alternatives: More convenient than querying each chain separately because it returns consolidated metrics; more accurate than manual cross-chain aggregation because it handles currency conversion and chain-specific factors.
+10 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 62/100 vs atv at 46/100.
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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