{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mongodb-lens","slug":"mongodb-lens","name":"MongoDB Lens","type":"mcp","url":"https://github.com/furey/mongodb-lens","page_url":"https://unfragile.ai/mongodb-lens","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mongodb-lens__cap_0","uri":"capability://tool.use.integration.mongodb.query.execution.via.mcp.protocol","name":"mongodb query execution via mcp protocol","description":"Executes MongoDB queries (find, insert, update, delete, aggregate) through the Model Context Protocol, translating natural language or structured requests from Claude/LLMs into native MongoDB driver calls. Implements MCP resource and tool handlers that map incoming requests to pymongo or native MongoDB driver operations, managing connection pooling and query result serialization back to the LLM context.","intents":["I want Claude to query my MongoDB database directly without writing boilerplate connection code","I need to build an AI agent that can read and modify MongoDB data as part of a workflow","I want to expose MongoDB operations as tools that Claude can call autonomously"],"best_for":["AI engineers building Claude-powered database agents","Teams integrating MongoDB with MCP-compatible LLM applications","Developers prototyping AI-driven database operations without custom API layers"],"limitations":["No built-in query optimization or cost estimation — complex aggregations may timeout on large datasets","Requires explicit connection string management; no automatic failover or replica set handling","Limited to synchronous query execution; no streaming results for large result sets","No transaction support across multiple operations in a single MCP call"],"requires":["MongoDB instance (local, Atlas, or self-hosted) with network accessibility","Python 3.8+ with pymongo driver installed","MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)","Valid MongoDB connection string (mongodb://, mongodb+srv://, or local URI)"],"input_types":["MongoDB query objects (JSON/dict format)","Natural language requests (via LLM interpretation)","Aggregation pipeline definitions","Filter, projection, and sort specifications"],"output_types":["JSON-serialized query results","Document arrays","Aggregation pipeline output","Operation acknowledgments (insert/update/delete counts)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_1","uri":"capability://memory.knowledge.database.schema.introspection.and.metadata.exposure","name":"database schema introspection and metadata exposure","description":"Automatically discovers and exposes MongoDB database schema information (collections, indexes, field types, validation rules) as MCP resources, allowing LLMs to understand database structure without manual documentation. Queries MongoDB system catalogs (system.indexes, schema validation metadata) and constructs a queryable schema representation that Claude can reference when formulating queries.","intents":["I want Claude to know what collections and fields exist in my MongoDB database","I need the AI to understand field types and constraints before generating queries","I want to expose database structure as context without embedding full documentation"],"best_for":["Teams with evolving MongoDB schemas who want AI agents to adapt automatically","Developers building self-documenting database agents","Organizations wanting to reduce hallucinated field names in LLM-generated queries"],"limitations":["Schema introspection adds startup latency (typically 500ms-2s depending on collection count)","Does not infer implicit schema from document samples; relies on explicit MongoDB schema validation","No tracking of schema changes over time; snapshot-based only","Limited to MongoDB 3.6+ for reliable schema validation metadata"],"requires":["MongoDB instance with read access to system.indexes and collection metadata","Python 3.8+ with pymongo","MCP client with resource discovery support"],"input_types":["Database name and connection context"],"output_types":["JSON schema definitions","Collection metadata (index definitions, validation rules)","Field type information"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_10","uri":"capability://automation.workflow.change.stream.monitoring.and.real.time.event.streaming","name":"change stream monitoring and real-time event streaming","description":"Implements MongoDB change streams as MCP resources, allowing Claude to monitor database changes in real-time and react to insert, update, delete, and replace operations. Handles change stream lifecycle (open, filter, close) and provides event notifications that Claude can use to trigger downstream actions or maintain synchronized state.","intents":["I want Claude to be notified when documents are inserted or updated in MongoDB","I need to build a reactive system where Claude responds to database changes","I want to monitor specific collections for changes and trigger actions based on change events"],"best_for":["Real-time applications where Claude needs to react to database changes","Event-driven architectures where MongoDB is the event source","Systems building AI-powered change detection and response"],"limitations":["Change streams require MongoDB 3.6+ with replica sets; not available on standalone instances","Change stream events are retained for only 24 hours (configurable); old events cannot be replayed","No built-in filtering at the stream level; all changes must be received and filtered by Claude","Change stream monitoring adds latency (typically 100-500ms) before Claude receives notifications"],"requires":["MongoDB 3.6+ with replica set or sharded cluster","Python 3.8+ with pymongo 3.6+","Read permissions on target collections"],"input_types":["Change stream filter specifications (optional)","Resume token for resuming from a specific point"],"output_types":["Change event objects (insert, update, delete, replace)","Full document data (if requested)","Operation metadata (timestamp, session ID)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_2","uri":"capability://data.processing.analysis.aggregation.pipeline.construction.and.execution","name":"aggregation pipeline construction and execution","description":"Provides MCP tools for building and executing MongoDB aggregation pipelines, translating high-level analytical requests into multi-stage pipeline definitions. Handles stage composition ($match, $group, $project, $sort, $limit), result streaming, and error handling for complex data transformations that go beyond simple CRUD operations.","intents":["I want Claude to perform complex analytics queries like grouping, counting, and filtering in a single operation","I need to build reports by aggregating data across multiple documents","I want the AI to optimize query performance by using aggregation pipelines instead of client-side filtering"],"best_for":["Data analysts using Claude for exploratory analytics on MongoDB","AI agents performing multi-step data transformations","Teams building reporting systems powered by LLM-generated aggregations"],"limitations":["No automatic query optimization; complex pipelines may require manual stage reordering for performance","Limited to MongoDB aggregation syntax; custom operators or extensions not supported","Result size capped by available memory; no built-in pagination for large aggregation outputs","Debugging complex pipelines requires understanding MongoDB aggregation semantics"],"requires":["MongoDB 3.2+ for basic aggregation support (4.2+ for advanced features like $facet)","Python 3.8+ with pymongo","Understanding of MongoDB aggregation pipeline syntax"],"input_types":["Aggregation pipeline stage definitions (JSON)","Natural language analytical requests (interpreted by LLM)"],"output_types":["Aggregated result documents","Summary statistics","Grouped and transformed data"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_3","uri":"capability://automation.workflow.connection.pooling.and.session.management","name":"connection pooling and session management","description":"Manages MongoDB connection lifecycle through MCP, maintaining a persistent connection pool that persists across multiple LLM requests within a single conversation. Implements session reuse, automatic reconnection on failure, and proper resource cleanup to avoid connection exhaustion when Claude makes multiple sequential database calls.","intents":["I want multiple Claude queries to reuse the same MongoDB connection without reconnecting","I need reliable connection handling that recovers from transient network failures","I want to avoid connection pool exhaustion when building multi-step database workflows"],"best_for":["Long-running Claude conversations with multiple database operations","Production deployments requiring connection efficiency","Teams building stateful AI agents with persistent database access"],"limitations":["Connection pool size is fixed at initialization; no dynamic scaling based on load","No built-in connection monitoring or metrics; requires external observability for pool health","Idle connections may be closed by MongoDB after inactivity timeout (default 30 minutes)","No support for connection-level authentication rotation without server restart"],"requires":["MongoDB instance accessible from MCP server network","Python 3.8+ with pymongo","Network connectivity with appropriate firewall rules"],"input_types":["Connection string and authentication credentials"],"output_types":["Connection status and pool metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_4","uri":"capability://data.processing.analysis.bulk.write.operations.and.batch.processing","name":"bulk write operations and batch processing","description":"Supports bulk insert, update, and delete operations through MCP, allowing Claude to perform multiple database modifications in a single atomic or ordered batch. Implements bulk write API wrappers that translate batch operation requests into MongoDB bulk write commands, with error handling for partial failures and detailed operation counts.","intents":["I want Claude to insert or update multiple documents efficiently in one operation","I need to perform batch deletions without making individual delete calls","I want detailed feedback on how many documents were modified in a bulk operation"],"best_for":["Data migration and ETL workflows powered by Claude","Bulk data import scenarios where performance matters","AI agents performing batch corrections or updates across large document sets"],"limitations":["No automatic batching of individual operations; Claude must explicitly request bulk mode","Ordered bulk writes stop on first error; unordered writes continue but require error inspection","No built-in transaction support across bulk operations and other queries","Large bulk operations (>100k documents) may timeout or consume excessive memory"],"requires":["MongoDB 2.6+ for bulk write API support","Python 3.8+ with pymongo","Write permissions on target collections"],"input_types":["Array of insert/update/delete operation specifications","Bulk write request objects with ordered/unordered mode"],"output_types":["Bulk write result with inserted/modified/deleted counts","Error details for failed operations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_5","uri":"capability://automation.workflow.index.management.and.query.optimization.hints","name":"index management and query optimization hints","description":"Provides MCP tools for creating, listing, and deleting MongoDB indexes, and allows Claude to apply query hints to optimize execution plans. Exposes index creation with configurable options (unique, sparse, TTL) and enables query hints that instruct MongoDB to use specific indexes, helping Claude learn which indexes improve query performance.","intents":["I want Claude to suggest or create indexes to improve query performance","I need to understand what indexes exist on my collections","I want the AI to apply query hints when it knows a specific index will be faster"],"best_for":["Performance-conscious teams building AI-driven database optimization","Developers learning MongoDB indexing through Claude-assisted exploration","Systems where query patterns are dynamic and index strategy needs to adapt"],"limitations":["No automatic index recommendation based on query patterns; Claude must explicitly request index creation","Index creation on large collections blocks writes (unless background index creation is used)","No analysis of index cardinality or selectivity; Claude cannot predict index effectiveness","Query hints may be ignored if MongoDB's query planner determines a better execution path"],"requires":["MongoDB 2.0+ for basic indexing (4.2+ for advanced index features)","Python 3.8+ with pymongo","Admin or write permissions on target collections"],"input_types":["Index specification (field names, direction, options)","Query hint specifications"],"output_types":["Index creation confirmation","List of existing indexes with metadata","Query execution plan information"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_6","uri":"capability://safety.moderation.document.validation.and.schema.enforcement","name":"document validation and schema enforcement","description":"Leverages MongoDB's schema validation feature to enforce document structure constraints, exposing validation rules as MCP resources and allowing Claude to understand what documents are valid before insertion. Reads and applies JSON Schema validation rules, providing feedback when Claude attempts to insert documents that violate schema constraints.","intents":["I want Claude to know what fields are required and what types they must be","I need validation errors to prevent invalid documents from being inserted","I want to enforce data quality through schema validation that Claude respects"],"best_for":["Teams with strict data quality requirements using Claude for data entry","Systems where schema enforcement prevents downstream errors","Organizations wanting to use MongoDB schema validation as a guardrail for AI-generated data"],"limitations":["Schema validation only works on insert/update; existing invalid documents are not automatically fixed","Validation rules are MongoDB-specific JSON Schema dialect; not all JSON Schema features are supported","No automatic schema inference; validation rules must be explicitly defined","Validation errors may be cryptic; Claude needs clear error messages to understand why documents failed"],"requires":["MongoDB 3.6+ for schema validation support","Python 3.8+ with pymongo","Pre-defined validation rules on target collections"],"input_types":["Document objects to validate","Schema validation rules (JSON Schema format)"],"output_types":["Validation success/failure status","Detailed validation error messages"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_7","uri":"capability://automation.workflow.transaction.support.for.multi.document.acid.operations","name":"transaction support for multi-document acid operations","description":"Exposes MongoDB multi-document transactions through MCP, enabling Claude to execute sequences of operations (read, write, delete) that are guaranteed to be atomic and isolated. Implements transaction lifecycle management (begin, commit, abort) with proper error handling for transaction conflicts and automatic rollback on failure.","intents":["I want Claude to perform multiple related database changes that must all succeed or all fail","I need to transfer data between collections atomically without intermediate inconsistency","I want to ensure that concurrent Claude operations don't interfere with each other"],"best_for":["Financial or critical data systems where consistency is non-negotiable","Multi-step workflows where partial failures would corrupt data","Teams building AI agents that modify related documents across collections"],"limitations":["Transactions require MongoDB 4.0+ with replica set or sharded cluster; not available on standalone instances","Transaction timeout is fixed (default 60 seconds); long-running transactions may abort","No automatic retry on transaction conflicts; Claude must handle ConflictError and retry logic","Transactions add latency (typically 50-200ms overhead) compared to non-transactional operations"],"requires":["MongoDB 4.0+ with replica set or sharded cluster deployment","Python 3.8+ with pymongo 3.7+","Write permissions on all collections involved in the transaction"],"input_types":["Sequence of database operations (reads, writes, deletes)","Transaction configuration (timeout, read preference)"],"output_types":["Transaction commit confirmation","Rollback status on failure","Conflict error details"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_8","uri":"capability://search.retrieval.text.search.and.full.text.indexing","name":"text search and full-text indexing","description":"Exposes MongoDB text search capabilities through MCP, allowing Claude to perform full-text searches across indexed fields using MongoDB's text search syntax. Supports text index creation with language-specific stemming and stop words, and enables natural language search queries that MongoDB translates into efficient index lookups.","intents":["I want Claude to search for documents by keyword or phrase without writing complex regex patterns","I need language-aware search that understands stemming and synonyms","I want to build a search feature where Claude can find relevant documents by natural language description"],"best_for":["Content management systems where Claude assists with document discovery","Search-driven applications powered by AI","Teams building semantic search on top of MongoDB text indexes"],"limitations":["Text search requires explicit text index creation; no automatic indexing of all text fields","Limited to MongoDB's text search syntax; no support for fuzzy matching or phonetic search","Text search results are not ranked by relevance score by default; requires explicit scoring","Only one text index per collection; cannot search across multiple text-indexed fields simultaneously"],"requires":["MongoDB 2.4+ for text search support","Python 3.8+ with pymongo","Text indexes created on target fields"],"input_types":["Search query string","Language specification for stemming"],"output_types":["Matching documents","Relevance scores (if requested)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb-lens__cap_9","uri":"capability://search.retrieval.geospatial.query.execution.and.location.based.filtering","name":"geospatial query execution and location-based filtering","description":"Exposes MongoDB geospatial query capabilities through MCP, enabling Claude to perform location-based searches using 2dsphere or 2d indexes. Supports queries like finding documents within a radius, along a route, or within a polygon, with automatic coordinate validation and distance calculation.","intents":["I want Claude to find documents near a specific geographic location","I need to query documents within a geographic boundary or polygon","I want to calculate distances between locations and filter by proximity"],"best_for":["Location-based applications (maps, delivery, local search) powered by Claude","Geospatial analytics where Claude identifies location patterns","Teams building AI-assisted location discovery and filtering"],"limitations":["Geospatial queries require explicit 2dsphere or 2d index creation; no automatic indexing","Limited to WGS84 coordinate system (latitude/longitude); no support for other projections","Distance calculations assume spherical Earth; not accurate for very short distances or complex terrain","Polygon queries may be slow on large datasets without proper index optimization"],"requires":["MongoDB 2.4+ for geospatial support (2.6+ for 2dsphere indexes)","Python 3.8+ with pymongo","Geospatial indexes created on location fields"],"input_types":["GeoJSON point, polygon, or linestring objects","Latitude/longitude coordinates","Distance radius in meters"],"output_types":["Matching documents with location data","Calculated distances from query point"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["MongoDB instance (local, Atlas, or self-hosted) with network accessibility","Python 3.8+ with pymongo driver installed","MCP client implementation (Claude Desktop, custom MCP host, or compatible LLM framework)","Valid MongoDB connection string (mongodb://, mongodb+srv://, or local URI)","MongoDB instance with read access to system.indexes and collection metadata","Python 3.8+ with pymongo","MCP client with resource discovery support","MongoDB 3.6+ with replica set or sharded cluster","Python 3.8+ with pymongo 3.6+","Read permissions on target collections"],"failure_modes":["No built-in query optimization or cost estimation — complex aggregations may timeout on large datasets","Requires explicit connection string management; no automatic failover or replica set handling","Limited to synchronous query execution; no streaming results for large result sets","No transaction support across multiple operations in a single MCP call","Schema introspection adds startup latency (typically 500ms-2s depending on collection count)","Does not infer implicit schema from document samples; relies on explicit MongoDB schema validation","No tracking of schema changes over time; snapshot-based only","Limited to MongoDB 3.6+ for reliable schema validation metadata","Change streams require MongoDB 3.6+ with replica sets; not available on standalone instances","Change stream events are retained for only 24 hours (configurable); old events cannot be replayed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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-lens","compare_url":"https://unfragile.ai/compare?artifact=mongodb-lens"}},"signature":"4BzCwYbGkX1rxTUrkiyVza/zH58AtREWNraaKZZaW1XdKefD5tj5qHPDk+2+jY6cFuFbf2SmUwpUWfbipnVCAw==","signedAt":"2026-06-21T01:25:04.429Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mongodb-lens","artifact":"https://unfragile.ai/mongodb-lens","verify":"https://unfragile.ai/api/v1/verify?slug=mongodb-lens","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"}}