{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mongodb-mcp-server","slug":"mongodb-mcp-server","name":"MongoDB MCP Server","type":"mcp","url":"https://github.com/mongodb-js/mongodb-mcp-server","page_url":"https://unfragile.ai/mongodb-mcp-server","categories":["mcp-servers","documentation"],"tags":["mongodb","database","nosql","official"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mongodb-mcp-server__cap_0","uri":"capability://tool.use.integration.mcp.standardized.mongodb.connection.bridging.with.dual.transport.support","name":"mcp-standardized mongodb connection bridging with dual transport support","description":"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.","intents":["Connect an LLM assistant to a MongoDB database without writing custom API wrappers","Enable Claude or Copilot to query and modify MongoDB data through natural language","Deploy a standardized database interface that works across multiple LLM clients"],"best_for":["AI application developers integrating MongoDB with LLM-powered assistants","Teams using Claude Desktop, VS Code Copilot, or Cursor IDE who need database access","Organizations standardizing on MCP for AI-database integration"],"limitations":["Requires Node.js 20.19.0 or higher; no Python or other runtime support","Transport layer adds latency overhead compared to direct MongoDB driver calls","Stdio transport limited to single-threaded request handling; HTTP transport requires additional configuration"],"requires":["Node.js ^20.19.0 || ^22.12.0 || >= 23.0.0","MongoDB connection string (URI format) for database operations","MCP-compatible client (Claude Desktop, VS Code with Copilot, Cursor IDE, or custom MCP client)"],"input_types":["MCP tool call requests with JSON-serialized parameters","MongoDB connection strings","Query filters and aggregation pipeline definitions"],"output_types":["MCP tool response objects with structured results","JSON-serialized document collections","Error messages with diagnostic context"],"categories":["tool-use-integration","mcp-servers"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_1","uri":"capability://data.processing.analysis.document.query.execution.with.mongodb.find.operations.and.result.streaming","name":"document query execution with mongodb find operations and result streaming","description":"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.","intents":["Query a MongoDB collection with filters and retrieve matching documents","Paginate through large result sets without loading all documents into memory","Project specific fields from documents to reduce payload size"],"best_for":["Developers building LLM agents that need to search MongoDB collections","Applications requiring filtered document retrieval through natural language queries","Systems handling large datasets where streaming results prevents memory exhaustion"],"limitations":["Result sets exceeding protocol message limits are exported to resources, requiring separate retrieval calls","No built-in full-text search; requires MongoDB text indexes for text queries","Projection and sorting are applied server-side but complex transformations require aggregation pipelines","Pagination requires explicit skip/limit parameters; no cursor-based pagination API"],"requires":["Active MongoDB connection with read permissions on target collection","Collection must exist in the connected database","Filter syntax must be valid MongoDB query operators (e.g., {$gt, $regex, $in})"],"input_types":["collection name (string)","filter object (MongoDB query syntax)","projection object (field inclusion/exclusion)","sort specification (field ordering)","skip and limit integers for pagination"],"output_types":["array of documents matching filter","document count","exported resource URI for large result sets"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_10","uri":"capability://data.processing.analysis.vector.embedding.storage.and.semantic.search.index.management","name":"vector embedding storage and semantic search index management","description":"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.","intents":["Create vector search indexes on embedding fields for semantic search","Execute semantic search queries to find documents similar to a query embedding","Combine vector search with traditional filters in aggregation pipelines","Build RAG systems that retrieve relevant documents based on semantic similarity"],"best_for":["RAG application developers building LLM-powered search systems","Teams implementing semantic search on document collections","AI applications that need to combine vector similarity with traditional filtering"],"limitations":["Vector search requires MongoDB Atlas; not available on self-hosted MongoDB","Embeddings must be pre-computed and stored in documents; no built-in embedding generation","Vector search indexes are expensive to create and maintain; not suitable for frequently changing embeddings","Query performance depends on embedding dimensionality and index configuration; tuning required for large collections"],"requires":["MongoDB Atlas cluster with Vector Search support","Embedding vectors pre-computed and stored in document fields","Vector search index created on target field with proper dimensionality specification"],"input_types":["collection name (string)","embedding field name (string)","embedding vector (array of floats)","number of results to return","optional filter for pre-filtering documents"],"output_types":["array of documents with similarity scores","vector search index creation status","error messages if index creation or search fails"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_11","uri":"capability://data.processing.analysis.large.result.set.export.and.resource.based.data.retrieval","name":"large result set export and resource-based data retrieval","description":"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.","intents":["Retrieve large query results that exceed MCP message size limits","Stream large collections without loading entire results into memory","Access exported data through resource URIs for multi-step processing"],"best_for":["Applications querying large MongoDB collections through LLM agents","Systems that need to handle result sets exceeding typical message size limits (>1MB)","Workflows requiring multi-step processing of large datasets"],"limitations":["Exported resources are temporary; no persistence across server restarts","Resource access requires separate MCP calls; adds latency for multi-step workflows","No built-in pagination of exported resources; entire result set must be retrieved","Memory usage scales with result set size; large exports may exhaust server memory"],"requires":["Active MongoDB connection","Query that produces results exceeding message size limits","MCP client that supports resource URI access"],"input_types":["query results (from find or aggregation operations)","export name (string identifier)"],"output_types":["exported-data:// resource URI","resource content (JSON-serialized documents)","resource metadata (size, format)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_12","uri":"capability://tool.use.integration.server.configuration.inspection.and.diagnostic.debugging","name":"server configuration inspection and diagnostic debugging","description":"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.","intents":["Verify MongoDB connection configuration is correct","Diagnose connection failures and authentication issues","Inspect server configuration to understand current settings","Debug transport and protocol issues"],"best_for":["Developers troubleshooting MongoDB connection issues","DevOps teams diagnosing server configuration problems","Support teams helping users resolve connectivity issues"],"limitations":["Configuration is read-only; cannot modify settings through MCP","Diagnostic information is limited to last connection attempt; no historical logs","Secrets are redacted; cannot verify exact credential values","No real-time connection monitoring; diagnostics reflect last known state"],"requires":["Active MCP server instance","MCP client that supports resource URI access"],"input_types":[],"output_types":["configuration object with secrets redacted","connection diagnostic information","last error message if connection failed","transport layer details"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_13","uri":"capability://memory.knowledge.multi.database.and.multi.collection.context.management.with.session.isolation","name":"multi-database and multi-collection context management with session isolation","description":"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.","intents":["Switch between multiple databases and collections in a single LLM conversation","Maintain context across multiple tool invocations without explicit database/collection parameters","Isolate context between different LLM sessions to prevent cross-contamination"],"best_for":["LLM agents performing multi-database operations in single conversation","Applications with multiple concurrent LLM sessions accessing different databases","Workflows requiring context persistence across multiple tool calls"],"limitations":["Session state is in-memory; lost on server restart","No built-in session persistence; requires external state store for durability","Context switching requires explicit tool calls; no implicit context inference","No automatic context cleanup; sessions must be explicitly closed to free resources"],"requires":["Active MCP server with session management enabled","Session ID or context identifier from MCP client"],"input_types":["database name (string)","collection name (string)"],"output_types":["context confirmation","current database and collection names"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_14","uri":"capability://code.generation.editing.typescript.first.tool.framework.with.type.safe.parameter.validation","name":"typescript-first tool framework with type-safe parameter validation","description":"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.","intents":["Develop new MongoDB tools with type safety and automatic validation","Generate MCP-compliant tool schemas from TypeScript interfaces","Validate tool parameters before execution to prevent runtime errors"],"best_for":["TypeScript developers extending MongoDB MCP server with custom tools","Teams building type-safe MCP servers with minimal boilerplate","Projects requiring automatic schema generation from type definitions"],"limitations":["TypeScript-only; no Python or other language support","Schema generation requires explicit type annotations; implicit types not supported","Validation is synchronous; no async validation hooks","No built-in OpenAPI/GraphQL schema generation; MCP schemas only"],"requires":["TypeScript 4.5+","Node.js 20.19.0 or higher","Understanding of TypeScript interfaces and decorators"],"input_types":["TypeScript interface definitions","tool implementation functions"],"output_types":["MCP tool schema (JSON)","validated parameters (typed objects)","validation error messages"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_2","uri":"capability://data.processing.analysis.aggregation.pipeline.execution.with.vector.search.and.stage.composition","name":"aggregation pipeline execution with vector search and stage composition","description":"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.","intents":["Run complex multi-stage data transformations using aggregation pipelines","Perform semantic search on vector embeddings stored in MongoDB Atlas","Compute aggregations (counts, sums, averages) across document collections","Join data from multiple collections using $lookup stages"],"best_for":["Data engineers building LLM-powered analytics and reporting systems","RAG (Retrieval-Augmented Generation) applications using vector embeddings","Teams performing complex data transformations through natural language prompts"],"limitations":["Vector search ($vectorSearch) requires MongoDB Atlas with Vector Search indexes; not available on self-hosted MongoDB","Pipeline stages are executed server-side; no client-side stage inspection or optimization","Large intermediate results in multi-stage pipelines can consume significant server memory","No built-in query optimization hints; complex pipelines may require manual stage reordering for performance"],"requires":["Active MongoDB connection with read permissions on target collection","Valid aggregation pipeline array with MongoDB stage syntax","For vector search: MongoDB Atlas cluster with Vector Search index created on target field","Vector embeddings must already exist in documents for $vectorSearch to function"],"input_types":["collection name (string)","aggregation pipeline array (MongoDB stage objects)","vector query (for $vectorSearch stage, typically embedding array)"],"output_types":["array of transformed/aggregated documents","aggregation result objects (counts, sums, grouped data)","exported resource URI for large result sets"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_3","uri":"capability://data.processing.analysis.document.crud.operations.with.insert.update.replace.and.delete.capabilities","name":"document crud operations with insert, update, replace, and delete capabilities","description":"Provides atomic document creation, modification, and deletion operations through MongoDB's insertOne, updateOne, replaceOne, and deleteOne methods. The implementation wraps these operations with validation, error handling, and transaction support where applicable, enabling LLMs to safely modify database state with automatic conflict detection and rollback on failure.","intents":["Insert new documents into a collection from LLM-generated content","Update specific fields in existing documents based on natural language instructions","Replace entire documents with new versions","Delete documents matching specific criteria"],"best_for":["LLM agents that need to persist generated content or user data to MongoDB","Applications building document-based workflows with AI-driven state transitions","Systems requiring atomic write operations with automatic error recovery"],"limitations":["Single-document operations only; batch inserts/updates require multiple tool calls","No built-in transaction support across multiple collections in single operation","Update operations require explicit field paths; no automatic schema inference for nested updates","Delete operations are permanent; no soft-delete or audit trail built-in"],"requires":["Active MongoDB connection with write permissions on target collection","Collection must exist or be auto-created by MongoDB","Document must conform to collection schema if schema validation is enabled","For updates: valid MongoDB update operators (e.g., $set, $inc, $push)"],"input_types":["collection name (string)","document object (for insert/replace)","filter object (for update/delete)","update specification (for update operations)"],"output_types":["inserted document with _id field","update result (matched count, modified count)","delete result (deleted count)","error messages with validation details"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_4","uri":"capability://data.processing.analysis.index.creation.and.management.with.performance.optimization.specifications","name":"index creation and management with performance optimization specifications","description":"Creates and manages MongoDB indexes (single-field, compound, text, geospatial, and vector search indexes) with support for index options like uniqueness, TTL, and sparse indexing. The implementation uses MongoDB's createIndex() API with full option support, enabling LLMs to optimize query performance by creating appropriate indexes based on query patterns and data characteristics.","intents":["Create indexes on frequently queried fields to improve query performance","Set up unique indexes to enforce data integrity constraints","Create TTL indexes for automatic document expiration","Build vector search indexes for semantic search capabilities"],"best_for":["Database administrators and developers optimizing MongoDB performance through LLM-assisted index planning","RAG systems that need to create vector search indexes for embedding collections","Applications with evolving query patterns that benefit from dynamic index creation"],"limitations":["Index creation is a blocking operation that locks the collection; large collections may experience downtime","No automatic index recommendation engine; LLM must understand query patterns to suggest appropriate indexes","Vector search indexes require MongoDB Atlas; not available on self-hosted MongoDB","Index creation cannot be rolled back within a transaction; requires manual deletion if created incorrectly"],"requires":["Active MongoDB connection with admin or index creation permissions","Collection must exist before index creation","For vector search indexes: MongoDB Atlas cluster with Vector Search support","Valid index specification with MongoDB index syntax and options"],"input_types":["collection name (string)","index key specification (field names and sort order)","index options (unique, sparse, ttl, background, etc.)"],"output_types":["index name (string)","index creation status","error messages if index creation fails"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_5","uri":"capability://data.processing.analysis.collection.schema.inspection.and.metadata.retrieval.with.field.analysis","name":"collection schema inspection and metadata retrieval with field analysis","description":"Retrieves collection metadata including field names, types, and sample documents through MongoDB's listCollections() and aggregation-based schema analysis. The implementation uses $sample and $group stages to infer schema structure from actual documents, providing LLMs with collection structure information without requiring explicit schema definitions.","intents":["Discover available collections and their field structures in a database","Understand data types and field patterns before writing queries","Inspect sample documents to understand document structure and nesting"],"best_for":["LLM agents exploring unfamiliar MongoDB databases before generating queries","Developers building schema-aware query generators","Systems that need to dynamically adapt to varying document structures"],"limitations":["Schema inference from samples may miss rare fields or type variations in large collections","No explicit schema validation information; inferred schema reflects actual data, not intended schema","Sampling large collections adds latency; no caching of schema information","Nested field analysis is limited; deeply nested structures may not be fully represented"],"requires":["Active MongoDB connection with read permissions on target database","Collection must exist in the database"],"input_types":["database name (string)","collection name (string)","optional sample size parameter"],"output_types":["array of collection names","field names and inferred types","sample documents","field frequency statistics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_6","uri":"capability://automation.workflow.mongodb.atlas.cluster.lifecycle.management.with.provisioning.and.configuration","name":"mongodb atlas cluster lifecycle management with provisioning and configuration","description":"Manages MongoDB Atlas cluster operations including creation, deletion, scaling, and configuration through the Atlas API. The implementation authenticates with Atlas API credentials and exposes tools for provisioning free/paid clusters, modifying cluster tiers, and managing cluster settings, enabling LLMs to autonomously manage cloud database infrastructure.","intents":["Create new MongoDB Atlas clusters programmatically for development or production","Scale existing clusters up or down based on capacity requirements","Delete unused clusters to reduce cloud costs","Configure cluster settings like backup policies and network access"],"best_for":["DevOps engineers and platform teams automating MongoDB Atlas infrastructure","SaaS applications that provision per-customer MongoDB clusters","Development teams that need to create ephemeral test clusters on-demand"],"limitations":["Requires Atlas API credentials with appropriate permissions; no fine-grained role support","Cluster creation is asynchronous; tool returns immediately but cluster takes minutes to provision","No built-in cost estimation; scaling operations may incur unexpected charges","Free tier clusters have limitations (shared resources, no backup); not suitable for production"],"requires":["MongoDB Atlas account with API access enabled","Atlas API key and secret (Organization or Project level)","Appropriate permissions in Atlas organization/project"],"input_types":["cluster name (string)","cluster tier (M0, M2, M5, etc.)","cloud provider and region","cluster configuration options"],"output_types":["cluster ID and connection string","cluster status (CREATING, RUNNING, DELETING)","cluster configuration details","error messages if provisioning fails"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_7","uri":"capability://automation.workflow.atlas.user.and.access.management.with.role.based.permission.configuration","name":"atlas user and access management with role-based permission configuration","description":"Manages MongoDB Atlas database users, API keys, and access control through the Atlas API. The implementation supports creating/deleting database users, managing API keys, and configuring role-based access control (RBAC), enabling LLMs to autonomously manage authentication and authorization for Atlas clusters.","intents":["Create database users with specific roles for application access","Rotate API keys for security compliance","Grant or revoke database access permissions","Manage organization and project-level access control"],"best_for":["Platform teams automating user provisioning for multi-tenant systems","Security-conscious organizations requiring programmatic access control","DevOps pipelines that need to create temporary credentials for CI/CD"],"limitations":["User creation requires database name specification; no wildcard role assignment across all databases","API key rotation requires manual credential updates in applications; no automatic propagation","RBAC is limited to predefined MongoDB roles; no custom role creation through MCP","No audit logging of access control changes; requires Atlas audit logs for compliance"],"requires":["MongoDB Atlas account with user/API key management permissions","Atlas API credentials with appropriate permissions","Target cluster must exist before creating database users"],"input_types":["username (string)","password (string, auto-generated if not provided)","role names (e.g., 'readWrite', 'read', 'admin')","database names for role scope"],"output_types":["created user credentials","API key ID and secret","role assignment confirmation","error messages if user creation fails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_8","uri":"capability://data.processing.analysis.atlas.cluster.monitoring.and.performance.metrics.retrieval","name":"atlas cluster monitoring and performance metrics retrieval","description":"Retrieves MongoDB Atlas cluster monitoring data including CPU usage, memory consumption, network I/O, and operation metrics through the Atlas API. The implementation queries Atlas monitoring endpoints to provide real-time performance visibility, enabling LLMs to analyze cluster health and identify performance bottlenecks.","intents":["Monitor cluster CPU, memory, and disk usage to detect resource constraints","Analyze operation metrics (queries, inserts, updates) to understand workload patterns","Identify slow queries and performance bottlenecks","Trigger scaling decisions based on resource utilization trends"],"best_for":["DevOps teams building automated cluster scaling and alerting systems","Database administrators analyzing performance trends through natural language queries","SaaS platforms monitoring per-customer cluster health"],"limitations":["Monitoring data is aggregated at cluster level; no per-collection or per-index metrics","Historical data retention depends on Atlas plan; free tier has limited retention","Metrics are delayed by 1-5 minutes; not suitable for real-time alerting","No built-in anomaly detection; LLM must interpret raw metrics to identify issues"],"requires":["MongoDB Atlas account with monitoring API access","Atlas API credentials with read permissions","Target cluster must be running and have monitoring enabled"],"input_types":["cluster ID (string)","metric names (e.g., 'CPU', 'MEMORY', 'NETWORK_IN')","time range (start and end timestamps)"],"output_types":["metric values with timestamps","aggregated statistics (min, max, average)","performance alerts if thresholds exceeded","error messages if metrics unavailable"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__cap_9","uri":"capability://automation.workflow.atlas.local.deployment.provisioning.and.management.via.docker","name":"atlas local deployment provisioning and management via docker","description":"Manages local MongoDB Atlas deployments using Docker containers through the Atlas Local API. The implementation provisions containerized MongoDB instances with Atlas features (vector search, transactions) for local development and testing, enabling developers to test Atlas-specific functionality without cloud infrastructure.","intents":["Create local MongoDB Atlas deployments for development without cloud costs","Test vector search and other Atlas features locally before cloud deployment","Provision ephemeral test environments for CI/CD pipelines","Debug Atlas-specific issues in local development environments"],"best_for":["Developers building Atlas-dependent applications who need local testing","CI/CD pipelines requiring containerized MongoDB for integration tests","Teams evaluating Atlas features before committing to cloud infrastructure"],"limitations":["Requires Docker daemon running locally; not available in serverless environments","Local deployments have resource constraints; not suitable for load testing","Atlas Local features may lag behind cloud Atlas; version mismatches possible","No automatic data persistence; containers are ephemeral unless explicitly configured"],"requires":["Docker daemon running and accessible to Node.js process","Docker image for MongoDB Atlas Local (auto-pulled if not present)","Sufficient disk space for container images and data"],"input_types":["deployment name (string)","MongoDB version","port mapping configuration","volume mount specifications"],"output_types":["deployment ID and connection string","container ID","deployment status (CREATING, RUNNING, STOPPED)","error messages if provisioning fails"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mongodb-mcp-server__headline","uri":"capability://tool.use.integration.mcp.server.for.mongodb.and.atlas.operations","name":"mcp server for mongodb and atlas operations","description":"The MongoDB MCP Server is a Model Context Protocol server that enables AI assistants to interact with MongoDB databases and Atlas infrastructure through a standardized protocol, facilitating database operations and management.","intents":["best MCP server for MongoDB","MCP server for AI database interactions","MongoDB server for document operations","MCP framework for Atlas management","MCP server for LLM integration"],"best_for":["AI-assisted database management","MongoDB operations"],"limitations":[],"requires":["Node.js"],"input_types":["database queries","document updates"],"output_types":["query results","operation confirmations"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":77,"verified":false,"data_access_risk":"high","permissions":["Node.js ^20.19.0 || ^22.12.0 || >= 23.0.0","MongoDB connection string (URI format) for database operations","MCP-compatible client (Claude Desktop, VS Code with Copilot, Cursor IDE, or custom MCP client)","Active MongoDB connection with read permissions on target collection","Collection must exist in the connected database","Filter syntax must be valid MongoDB query operators (e.g., {$gt, $regex, $in})","MongoDB Atlas cluster with Vector Search support","Embedding vectors pre-computed and stored in document fields","Vector search index created on target field with proper dimensionality specification","Active MongoDB connection"],"failure_modes":["Requires Node.js 20.19.0 or higher; no Python or other runtime support","Transport layer adds latency overhead compared to direct MongoDB driver calls","Stdio transport limited to single-threaded request handling; HTTP transport requires additional configuration","Result sets exceeding protocol message limits are exported to resources, requiring separate retrieval calls","No built-in full-text search; requires MongoDB text indexes for text queries","Projection and sorting are applied server-side but complex transformations require aggregation pipelines","Pagination requires explicit skip/limit parameters; no cursor-based pagination API","Vector search requires MongoDB Atlas; not available on self-hosted MongoDB","Embeddings must be pre-computed and stored in documents; no built-in embedding generation","Vector search indexes are expensive to create and maintain; not suitable for frequently changing embeddings","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.62,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mongodb-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mongodb-mcp-server"}},"signature":"+qcJ1RqqK2PNToP4EFBeO9t+5ogxLLqaYO/YUchDTaJvoDTnME0EiAQcT8oFEUVX82bXEoObod2X6nw9uV5SAw==","signedAt":"2026-06-23T14:23:28.837Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mongodb-mcp-server","artifact":"https://unfragile.ai/mongodb-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mongodb-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}