{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-mongodb-js-mongodb-mcp-server","slug":"mcp-mongodb-js-mongodb-mcp-server","name":"mongodb-mcp-server","type":"mcp","url":"https://github.com/mongodb-js/mongodb-mcp-server","page_url":"https://unfragile.ai/mcp-mongodb-js-mongodb-mcp-server","categories":["mcp-servers"],"tags":["mcp","mcp-server","mongodb","mongodb-atlas","mongodb-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_0","uri":"capability://tool.use.integration.mongodb.connection.management.via.mcp.protocol","name":"mongodb connection management via mcp protocol","description":"Establishes and maintains persistent connections to MongoDB instances and MongoDB Atlas clusters through the Model Context Protocol, handling authentication (connection strings, credentials), connection pooling, and lifecycle management. Implements MCP server transport layer to expose MongoDB as a resource accessible to LLM clients and agents without direct database access.","intents":["Connect an AI agent to a MongoDB database without exposing credentials to the agent directly","Enable Claude or other LLM clients to query MongoDB through a standardized MCP interface","Manage multiple MongoDB Atlas cluster connections from a single MCP server instance"],"best_for":["AI agents and LLM applications requiring structured database access","Teams building multi-tenant SaaS with MongoDB backends","Developers integrating MongoDB into Claude projects or MCP-compatible clients"],"limitations":["Connection pooling is managed by the underlying MongoDB Node.js driver; custom pool sizing requires driver-level configuration","No built-in connection encryption beyond MongoDB's native TLS support","Single MCP server instance may become a bottleneck under high concurrent agent requests"],"requires":["Node.js 18+","MongoDB 4.0+ or MongoDB Atlas cluster","Valid MongoDB connection string (mongodb:// or mongodb+srv://)","Network access to MongoDB instance or Atlas cluster"],"input_types":["connection string","authentication credentials","MCP protocol messages"],"output_types":["connection status","MCP resource handles","error messages"],"categories":["tool-use-integration","database-connectivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_1","uri":"capability://data.processing.analysis.database.query.execution.with.schema.awareness","name":"database query execution with schema awareness","description":"Executes MongoDB queries (find, aggregate, insert, update, delete) through MCP tools that accept query parameters and return structured results. Implements query validation and schema introspection to provide type information about collections, enabling LLMs to construct valid queries without trial-and-error. Uses MongoDB's aggregation pipeline and query language natively.","intents":["Execute arbitrary MongoDB queries from an LLM agent with automatic result formatting","Discover collection schemas and field types to help agents construct valid queries","Run aggregation pipelines to transform and analyze data within MongoDB"],"best_for":["LLM agents performing data analysis and reporting on MongoDB datasets","Developers building AI-powered database explorers or query assistants","Teams needing agents to perform CRUD operations on MongoDB collections"],"limitations":["Query complexity is limited by MongoDB's aggregation pipeline capabilities; complex multi-stage transformations may require client-side processing","No query optimization or explain plan analysis built into the MCP interface","Result sets are limited by MCP message size constraints; large result sets require pagination or streaming (if supported)","No built-in query caching; repeated identical queries hit the database each time"],"requires":["Active MongoDB connection via MCP server","Read/write permissions on target collections","Knowledge of MongoDB query syntax (BSON, aggregation pipeline syntax)"],"input_types":["MongoDB query objects (JSON/BSON format)","aggregation pipeline stages","filter expressions"],"output_types":["query results (JSON documents)","collection metadata","schema information","operation status (success/failure)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_10","uri":"capability://search.retrieval.geospatial.query.execution","name":"geospatial query execution","description":"Provides MCP tools to execute geospatial queries on MongoDB collections with 2dsphere or 2d indexes. Implements MongoDB's geospatial operators ($near, $geoWithin, $geoIntersects) enabling agents to find documents based on geographic proximity or containment. Supports GeoJSON format for location data.","intents":["Find MongoDB documents near a geographic location","Query documents within a geographic boundary or polygon","Build location-based features into LLM agents"],"best_for":["LLM agents building location-based features (maps, nearby searches, etc.)","Developers creating geographic data applications","Teams needing agents to perform spatial queries on location data"],"limitations":["Geospatial queries are limited to 2D and spherical (3D) geometry; no support for complex 3D spatial operations","Query performance depends on index quality; poorly indexed geospatial queries can be slow on large datasets","Distance calculations assume spherical Earth; flat-earth projections not supported","No support for complex geographic operations like polygon intersection or buffer zones"],"requires":["Active MongoDB connection","Geospatial index (2dsphere or 2d) on target collection","Location data in GeoJSON format","Read permissions on target collection"],"input_types":["GeoJSON point (for proximity queries)","GeoJSON polygon (for containment queries)","distance in meters (for $near queries)","query options (maxDistance, minDistance, etc.)"],"output_types":["matching documents","distance information","geospatial query results"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_11","uri":"capability://data.processing.analysis.faceted.search.and.aggregation.based.analytics","name":"faceted search and aggregation-based analytics","description":"Provides MCP tools to perform faceted search and analytics using MongoDB's aggregation framework. Agents can request facets (counts by category, range, etc.) alongside search results, and execute complex analytics queries that group, filter, and transform data. Implements multi-facet aggregation pipelines for exploratory data analysis.","intents":["Build faceted search interfaces where agents can explore data by multiple dimensions","Perform analytics queries that group and aggregate data for reporting","Enable agents to discover data patterns through multi-dimensional analysis"],"best_for":["LLM agents building analytics and reporting features","Developers creating data exploration tools","Teams needing agents to perform multi-dimensional analysis on MongoDB data"],"limitations":["Faceted search performance degrades with high cardinality fields; agents may need to limit facet depth","Complex aggregation pipelines can consume significant server resources; no built-in query cost estimation","Facet results are not cached; repeated facet queries hit the database each time","No support for approximate facet counts; all counts are exact, which can be slow on large datasets"],"requires":["Active MongoDB connection","Read permissions on target collection","Knowledge of MongoDB aggregation pipeline syntax"],"input_types":["search filter (optional)","facet definitions (field names, ranges, etc.)","aggregation pipeline stages"],"output_types":["facet counts and values","aggregation results","analytics data"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_12","uri":"capability://data.processing.analysis.data.export.and.format.conversion","name":"data export and format conversion","description":"Provides MCP tools to export MongoDB query results in multiple formats (JSON, CSV, BSON) and handle large result sets through pagination or streaming. Implements result formatting and serialization, enabling agents to extract data for external processing or reporting. Supports configurable field selection and transformation during export.","intents":["Export MongoDB query results in formats suitable for external tools (CSV, JSON)","Generate reports from MongoDB data in agent-friendly formats","Handle large result sets through pagination without loading entire datasets into memory"],"best_for":["LLM agents generating reports or exporting data","Developers building data extraction pipelines","Teams needing agents to format MongoDB data for external systems"],"limitations":["Large exports may exceed MCP message size limits; agents must implement pagination","CSV export requires flattening nested documents; complex nested structures may lose information","No built-in compression; large exports consume significant bandwidth","Format conversion is performed on the MCP server; CPU-intensive exports may impact server performance"],"requires":["Active MongoDB connection","Read permissions on target collection","Query that returns results to export"],"input_types":["MongoDB query","export format (JSON, CSV, BSON)","field selection (optional)","pagination parameters (skip, limit)"],"output_types":["formatted data (JSON, CSV, BSON)","pagination metadata","export status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_2","uri":"capability://data.processing.analysis.collection.and.database.introspection","name":"collection and database introspection","description":"Provides MCP tools to list databases, collections, and indexes, and retrieve schema information including field names, types, and validation rules. Implements MongoDB's introspection APIs (listDatabases, listCollections, getIndexes) and potentially uses schema inference or validation metadata to expose structure to LLM clients. Enables agents to discover available data without prior knowledge of the database structure.","intents":["Discover what databases and collections exist in a MongoDB instance","Understand the schema and field types of a collection before querying","List indexes to help agents understand query optimization opportunities"],"best_for":["AI agents exploring unfamiliar MongoDB databases","Developers building database documentation or discovery tools","Teams onboarding new agents to existing MongoDB infrastructure"],"limitations":["Schema inference is limited to MongoDB's built-in validation schemas; unvalidated collections may have inconsistent field types","Index information is read-only; agents cannot create or modify indexes through this capability","Large databases with thousands of collections may have slow introspection queries","No support for MongoDB's schema validation rules beyond basic type information"],"requires":["Active MongoDB connection","Read permissions on system.indexes and system.namespaces collections"],"input_types":["database name (optional)","collection name (optional)"],"output_types":["list of databases","list of collections","field schema (names, types, validation rules)","index definitions"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_3","uri":"capability://data.processing.analysis.aggregation.pipeline.construction.and.execution","name":"aggregation pipeline construction and execution","description":"Provides a dedicated MCP tool for constructing and executing MongoDB aggregation pipelines, which are multi-stage data transformation workflows. Accepts pipeline stages (match, group, project, sort, limit, etc.) as structured input and executes them server-side, returning transformed results. Implements validation of pipeline syntax and stage compatibility before execution.","intents":["Build complex data transformations and analytics queries without client-side processing","Aggregate and group data across multiple documents in a single operation","Perform server-side filtering, sorting, and limiting to reduce data transfer"],"best_for":["LLM agents performing analytics and reporting on large MongoDB datasets","Developers building data transformation pipelines that agents can construct dynamically","Teams needing agents to perform complex multi-stage data operations"],"limitations":["Pipeline stages are limited to MongoDB's built-in operators; custom transformations require client-side processing","Large intermediate result sets in multi-stage pipelines may cause memory pressure on the MongoDB server","No support for cross-collection joins beyond $lookup, which has performance implications","Pipeline execution time is not exposed; agents cannot optimize based on performance metrics"],"requires":["Active MongoDB connection","Knowledge of MongoDB aggregation pipeline syntax","Read permissions on target collections"],"input_types":["aggregation pipeline (array of stage objects)","collection name"],"output_types":["transformed documents (JSON)","aggregation results","error messages for invalid pipelines"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_4","uri":"capability://data.processing.analysis.document.insertion.and.bulk.write.operations","name":"document insertion and bulk write operations","description":"Provides MCP tools for inserting single documents, inserting multiple documents in bulk, and performing bulk write operations (mixed insert/update/delete). Implements validation of document structure before insertion and handles MongoDB's write concern and error handling. Supports ordered and unordered bulk operations with configurable behavior on partial failures.","intents":["Insert new documents into MongoDB collections from LLM-generated data","Perform bulk inserts of multiple documents in a single operation for efficiency","Execute mixed bulk write operations (insert, update, delete) in a single transaction"],"best_for":["AI agents generating and persisting data (e.g., creating records from user input)","Developers building data ingestion pipelines powered by LLMs","Teams needing agents to perform batch operations on MongoDB"],"limitations":["Bulk operations are not transactional across multiple collections; single-collection bulk writes only","No support for MongoDB transactions in this capability; multi-document ACID transactions require custom implementation","Write concern is fixed at the MCP server level; agents cannot adjust durability guarantees per operation","No built-in deduplication; agents must handle duplicate detection themselves"],"requires":["Active MongoDB connection","Write permissions on target collection","Valid document structure (BSON-compatible JSON)"],"input_types":["single document (JSON object)","array of documents (JSON array)","bulk write operations (array of insert/update/delete commands)"],"output_types":["insertion results (inserted IDs, count)","bulk operation results (matched, modified, deleted counts)","error messages for failed operations"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_5","uri":"capability://data.processing.analysis.document.update.and.deletion.with.query.filters","name":"document update and deletion with query filters","description":"Provides MCP tools for updating documents (updateOne, updateMany) and deleting documents (deleteOne, deleteMany) using MongoDB query filters and update operators. Implements MongoDB's update operators ($set, $inc, $push, etc.) and supports both replacement and partial updates. Handles write concern and returns operation results including matched and modified counts.","intents":["Update existing documents in MongoDB based on query criteria","Perform partial updates using MongoDB's update operators without replacing entire documents","Delete documents matching specific criteria"],"best_for":["LLM agents modifying existing data based on user requests or analysis","Developers building data management tools where agents can update records","Teams needing agents to perform selective deletions or archiving"],"limitations":["Update operations are not transactional; concurrent updates may lead to race conditions","No support for conditional updates based on document state; agents cannot implement optimistic locking","Deletion is permanent; no soft-delete or audit trail built-in","Update operators are limited to MongoDB's native set; custom update logic requires client-side processing"],"requires":["Active MongoDB connection","Write permissions on target collection","Valid MongoDB query filter and update operator syntax"],"input_types":["query filter (MongoDB query object)","update operators ($set, $inc, $push, etc.)","replacement document (for full replacement)"],"output_types":["update results (matched count, modified count)","deletion results (deleted count)","error messages"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_6","uri":"capability://data.processing.analysis.index.management.and.query.optimization.hints","name":"index management and query optimization hints","description":"Provides MCP tools to list existing indexes on collections and potentially create or drop indexes. Implements MongoDB's index APIs (createIndex, dropIndex, getIndexes) and may expose query plan information to help agents understand index usage. Enables agents to optimize queries by understanding or creating appropriate indexes.","intents":["Discover existing indexes on a collection to understand query optimization","Create indexes on frequently queried fields to improve agent query performance","Drop unused indexes to reduce write overhead"],"best_for":["Developers building self-optimizing database agents","Teams where agents need to manage database performance","AI-powered database administration tools"],"limitations":["Index creation is a blocking operation; large collections may cause write delays during index creation","No automatic index recommendation engine; agents must manually decide which fields to index","Index creation/deletion requires admin permissions; may not be available in all MongoDB Atlas tiers","No explain plan analysis built-in; agents cannot see query execution details"],"requires":["Active MongoDB connection","Admin or write permissions on target collection (for index creation/deletion)","Knowledge of MongoDB index types and options"],"input_types":["collection name","field names (for index creation)","index options (unique, sparse, TTL, etc.)"],"output_types":["list of indexes","index creation results","index deletion results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_7","uri":"capability://data.processing.analysis.transaction.support.for.multi.document.operations","name":"transaction support for multi-document operations","description":"Provides MCP tools to execute MongoDB transactions, which ensure ACID guarantees across multiple documents and collections. Implements MongoDB's session-based transaction API (startSession, startTransaction, commitTransaction, abortTransaction) and handles transaction state management. Enables agents to perform complex multi-step operations with rollback capability.","intents":["Execute multi-document operations with ACID guarantees to maintain data consistency","Perform complex workflows that require rollback if any step fails","Ensure data integrity when agents modify related documents across collections"],"best_for":["LLM agents performing financial or critical data operations requiring ACID guarantees","Developers building complex data workflows with rollback requirements","Teams needing agents to maintain data consistency across multiple collections"],"limitations":["Transactions require MongoDB 4.0+ with replica sets or sharded clusters; not available on standalone instances","Transaction overhead adds latency; not suitable for high-frequency operations","No automatic retry logic; agents must handle transaction conflicts and retry themselves","Transaction timeout is fixed at the server level; agents cannot adjust per-operation"],"requires":["MongoDB 4.0+ with replica set or sharded cluster","Active MongoDB connection with transaction support","Write permissions on all collections involved in the transaction"],"input_types":["sequence of operations (insert, update, delete)","transaction options (read concern, write concern, read preference)"],"output_types":["transaction results (success/failure)","operation results within transaction","error messages with rollback information"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_8","uri":"capability://automation.workflow.change.stream.monitoring.for.real.time.data.updates","name":"change stream monitoring for real-time data updates","description":"Provides MCP tools to establish change streams on collections or databases, enabling agents to monitor real-time data modifications. Implements MongoDB's change stream API with support for filtering change events and resuming from specific points. Agents can subscribe to insert, update, delete, and replace events and react to data changes in real-time.","intents":["Monitor MongoDB collections for real-time changes and trigger agent actions","Build event-driven workflows where agents react to data modifications","Maintain synchronized state between MongoDB and external systems"],"best_for":["LLM agents building real-time data pipelines or event-driven workflows","Developers creating reactive systems that respond to MongoDB changes","Teams needing agents to synchronize data across multiple systems"],"limitations":["Change streams require MongoDB 3.6+ with replica sets; not available on standalone instances","Change stream events are not persisted indefinitely; agents must resume within the oplog window (typically 24 hours)","No built-in filtering at the stream level; complex filtering requires client-side processing","MCP protocol may not support long-lived streaming connections; implementation may require polling or WebSocket extensions"],"requires":["MongoDB 3.6+ with replica set or sharded cluster","Active MongoDB connection with change stream support","Read permissions on target collection or database"],"input_types":["collection or database name","change stream filter (optional)","resume token (optional, for resuming from a specific point)"],"output_types":["change events (insert, update, delete, replace)","event metadata (timestamp, operation type, document ID)","resume tokens for fault tolerance"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-mongodb-js-mongodb-mcp-server__cap_9","uri":"capability://search.retrieval.text.search.and.full.text.indexing","name":"text search and full-text indexing","description":"Provides MCP tools to perform full-text search on MongoDB collections using text indexes and the $text query operator. Implements MongoDB's text search capabilities including language support, stemming, and relevance scoring. Agents can search across multiple fields and retrieve results ranked by relevance.","intents":["Search MongoDB collections for documents matching text queries","Build full-text search capabilities into LLM agents without external search engines","Retrieve documents ranked by relevance to a search query"],"best_for":["LLM agents building search functionality into applications","Developers creating document search features without external search infrastructure","Teams needing agents to find relevant documents from large collections"],"limitations":["Text search is limited to MongoDB's built-in text search; no support for advanced features like fuzzy matching or phonetic search","Only one text index per collection; agents cannot search across multiple text indexes","Text search performance degrades on large collections; not suitable for real-time search on millions of documents","Language support is limited to MongoDB's built-in languages; custom language support requires external tools"],"requires":["Active MongoDB connection","Text index created on target collection","Read permissions on target collection"],"input_types":["search query (text string)","search options (language, case sensitivity, etc.)"],"output_types":["matching documents","relevance scores","search result metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","MongoDB 4.0+ or MongoDB Atlas cluster","Valid MongoDB connection string (mongodb:// or mongodb+srv://)","Network access to MongoDB instance or Atlas cluster","Active MongoDB connection via MCP server","Read/write permissions on target collections","Knowledge of MongoDB query syntax (BSON, aggregation pipeline syntax)","Active MongoDB connection","Geospatial index (2dsphere or 2d) on target collection","Location data in GeoJSON format"],"failure_modes":["Connection pooling is managed by the underlying MongoDB Node.js driver; custom pool sizing requires driver-level configuration","No built-in connection encryption beyond MongoDB's native TLS support","Single MCP server instance may become a bottleneck under high concurrent agent requests","Query complexity is limited by MongoDB's aggregation pipeline capabilities; complex multi-stage transformations may require client-side processing","No query optimization or explain plan analysis built into the MCP interface","Result sets are limited by MCP message size constraints; large result sets require pagination or streaming (if supported)","No built-in query caching; repeated identical queries hit the database each time","Geospatial queries are limited to 2D and spherical (3D) geometry; no support for complex 3D spatial operations","Query performance depends on index quality; poorly indexed geospatial queries can be slow on large datasets","Distance calculations assume spherical Earth; flat-earth projections not supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4505517557046344,"quality":0.35,"ecosystem":0.55,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-05-03T12:53:28Z"},"community":{"stars":1013,"forks":226,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-mongodb-js-mongodb-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=mcp-mongodb-js-mongodb-mcp-server"}},"signature":"vBicGFd0zhlj+uAXaeBQLkb53WFCw18RxkxR7aNTzao6rdc9S1SZXdLZuiJfPa4xUq+1muHZCpoM6ZTw/ondCA==","signedAt":"2026-06-20T00:12:55.290Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-mongodb-js-mongodb-mcp-server","artifact":"https://unfragile.ai/mcp-mongodb-js-mongodb-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-mongodb-js-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"}}