{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mongodb","slug":"mongodb","name":"MongoDB","type":"mcp","url":"https://github.com/kiliczsh/mcp-mongo-server","page_url":"https://unfragile.ai/mongodb","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mongodb__cap_0","uri":"capability://tool.use.integration.mcp.compliant.mongodb.tool.registration.and.schema.based.function.calling","name":"mcp-compliant mongodb tool registration and schema-based function calling","description":"Registers MongoDB operations as MCP tools with JSON schema definitions, enabling LLM clients (Claude Desktop, Windsurf, Cursor) to discover and invoke database operations through standardized function-calling interfaces. The server exposes tools via MCP's tool registry with full schema validation, allowing LLMs to understand parameter requirements and constraints before execution without custom integration code.","intents":["Enable Claude or other LLM agents to query MongoDB without writing database client code","Expose MongoDB operations as discoverable tools in IDE integrations like Cursor or Windsurf","Allow LLMs to understand MongoDB schema and operation constraints through introspection"],"best_for":["AI agent developers building autonomous database-querying systems","Teams integrating MongoDB with Claude Desktop or Cursor IDE plugins","LLM application builders who want standardized database access without custom SDKs"],"limitations":["Tool discovery is static at server startup — schema changes require server restart","No built-in rate limiting or quota enforcement at the MCP layer","LLM must correctly interpret schema to generate valid queries; malformed requests still reach MongoDB"],"requires":["MCP-compatible client (Claude Desktop, Windsurf, Cursor, or custom MCP client)","Node.js 18+ runtime","MongoDB 4.0+ instance with network connectivity"],"input_types":["JSON schema definitions (auto-generated from operation signatures)","Tool invocation requests from MCP clients"],"output_types":["MCP tool list with JSON schemas","Operation results as JSON-serializable objects"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_1","uri":"capability://data.processing.analysis.configurable.objectid.string.to.native.conversion.with.three.conversion.modes","name":"configurable objectid string-to-native conversion with three conversion modes","description":"Automatically converts between MongoDB ObjectId binary format and JSON-serializable strings using three pluggable strategies: 'auto' (converts fields named _id or *_id based on heuristics), 'none' (no conversion), and 'force' (converts all string ID fields). This bridges the impedance mismatch between MongoDB's native ObjectId type and JSON serialization, enabling LLMs to work with IDs as strings while maintaining database integrity.","intents":["Accept string IDs from LLM-generated queries and automatically convert them to MongoDB ObjectIds","Return ObjectIds as strings in JSON responses so LLMs can reason about and manipulate IDs","Configure ID conversion behavior per deployment without code changes"],"best_for":["Teams deploying MongoDB with LLM agents that generate string-based queries","Applications requiring flexible ID handling across multiple collection schemas","Developers who want to avoid manual ObjectId serialization in LLM prompts"],"limitations":["'auto' mode uses heuristic field name matching and may incorrectly identify non-ID string fields as IDs","'force' mode converts all string fields that look like ObjectIds, risking false positives on legitimate string data","Conversion happens at query boundary — nested ObjectIds in aggregation pipelines may not convert correctly"],"requires":["MongoDB driver with ObjectId support (included in Node.js driver)","Configuration via environment variable or CLI flag at server startup"],"input_types":["JSON query objects with string ID values","MongoDB query filters and update documents"],"output_types":["MongoDB-native ObjectId objects (internal)","JSON-serialized string representations of ObjectIds in responses"],"categories":["data-processing-analysis","type-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_10","uri":"capability://automation.workflow.index.creation.with.field.specification.and.options","name":"index creation with field specification and options","description":"Creates MongoDB indexes on specified fields with support for index options (unique, sparse, TTL, etc.). The server accepts a field specification and options object, creates the index, and returns confirmation. This operation is blocked in read-only mode and requires explicit write permissions.","intents":["Allow LLM agents to optimize query performance by creating indexes on frequently-queried fields","Implement index management as part of data pipeline automation","Enable LLMs to suggest and create indexes based on query patterns"],"best_for":["LLM agents managing MongoDB performance optimization","Applications building self-tuning data pipelines","Teams using LLMs to automate database maintenance"],"limitations":["Blocked in read-only mode — no write operations allowed","No index analysis or recommendation engine — LLMs must know which fields to index","Index creation is synchronous and blocks the server on large collections","No support for partial indexes or complex index types (e.g., text search, geospatial)"],"requires":["MongoDB 4.0+","Write permission on the collection","Read-only mode must be disabled"],"input_types":["Field specification object (e.g., {fieldName: 1} for ascending, -1 for descending)","Index options object (unique, sparse, expireAfterSeconds, etc.)"],"output_types":["Index name (auto-generated or specified)","Confirmation of index creation success"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_11","uri":"capability://memory.knowledge.mcp.resource.based.collection.schema.context.provisioning","name":"mcp resource-based collection schema context provisioning","description":"Provides collection schemas as MCP resources (not just tools), allowing LLM clients to request schema information on-demand through the MCP resource protocol. The server exposes each collection as a resource with a URI like mongodb://collection/collectionName, enabling clients to fetch and cache schema information separately from tool invocations.","intents":["Allow LLM clients to fetch collection schemas as context without executing queries","Enable schema caching in LLM clients to reduce server load","Provide structured schema information through MCP's resource protocol"],"best_for":["MCP clients that support resource fetching (Claude, Cursor, Windsurf)","Applications needing to cache schema information across multiple queries","Teams building LLM agents that reference schemas frequently"],"limitations":["Resource protocol support is optional in MCP clients — not all clients may fetch resources","Schema resources are static snapshots — changes require explicit refresh","No support for versioning or change tracking of schemas"],"requires":["MCP-compatible client with resource fetching support","MongoDB 4.0+"],"input_types":["Resource URI (mongodb://collection/collectionName)"],"output_types":["JSON schema object with field names, types, and cardinality"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_12","uri":"capability://tool.use.integration.uri.based.mongodb.connection.management.with.authentication","name":"uri-based mongodb connection management with authentication","description":"Manages MongoDB connections using standard MongoDB connection URIs (mongodb://host:port or mongodb+srv://), supporting authentication credentials, replica sets, and connection options. The server parses the URI at startup, establishes a persistent connection pool, and reuses connections across all operations. Connection configuration is provided via environment variable or CLI argument.","intents":["Connect to MongoDB instances with various authentication methods (username/password, X.509, SCRAM)","Support MongoDB Atlas and self-hosted deployments through standard URI syntax","Configure connection pooling and retry behavior without code changes"],"best_for":["Teams deploying MCP MongoDB Server to production with authentication requirements","Applications connecting to MongoDB Atlas or other managed MongoDB services","Developers needing flexible connection configuration across environments"],"limitations":["Connection URI is set at server startup — cannot change databases or credentials without restarting","No support for dynamic connection switching or multi-database scenarios","Credentials in URI are stored in environment variables or CLI args, requiring careful secret management"],"requires":["Valid MongoDB connection URI (mongodb:// or mongodb+srv://)","Network connectivity to MongoDB instance","Authentication credentials if required by MongoDB instance"],"input_types":["MongoDB connection URI (string)"],"output_types":["Persistent MongoDB client connection (internal)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_2","uri":"capability://safety.moderation.read.only.mode.with.operation.level.access.control","name":"read-only mode with operation-level access control","description":"Enforces read-only access to MongoDB by blocking write operations (insert, update, delete, createIndex) at the tool registration layer while permitting all read operations (find, aggregate, count, listCollections, serverInfo). This is configured globally via environment variable or CLI flag and prevents accidental or malicious data modification through LLM-generated queries.","intents":["Safely expose MongoDB to untrusted LLM agents without risking data corruption","Allow non-technical users to query production databases through Claude without write permissions","Implement least-privilege access for LLM tools in regulated environments"],"best_for":["Production deployments exposing MongoDB to LLM agents","Teams building read-only analytics or reporting interfaces powered by LLMs","Regulated industries requiring strict access control for database operations"],"limitations":["Read-only mode is all-or-nothing — no fine-grained per-collection or per-field access control","Does not prevent information leakage through read operations (e.g., LLM could enumerate all documents)","No audit logging of operations — requires external monitoring to track what was queried"],"requires":["Environment variable MCP_MONGODB_READONLY=true OR CLI flag --read-only","Server restart to change read-only status"],"input_types":["MongoDB query operations (all types)"],"output_types":["Read operation results (permitted)","Error responses for write operations (blocked)"],"categories":["safety-moderation","access-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_3","uri":"capability://search.retrieval.mongodb.find.query.execution.with.filtering.projection.and.pagination","name":"mongodb find query execution with filtering, projection, and pagination","description":"Executes MongoDB find() queries with support for filter documents, field projection (inclusion/exclusion), sorting, skip, and limit parameters. The server translates LLM-generated query objects into native MongoDB find() calls, handling cursor management and result serialization. Supports complex filter syntax including operators ($eq, $gt, $in, etc.) and nested field queries.","intents":["Execute flexible MongoDB queries from LLM-generated filter objects","Retrieve specific fields from documents using projection to reduce response size","Implement pagination through skip/limit for large result sets"],"best_for":["LLM agents querying collections with complex filter logic","Applications needing flexible field selection without predefined schemas","Developers building search or filtering interfaces powered by LLMs"],"limitations":["No automatic index optimization — LLMs may generate inefficient queries that scan entire collections","Result set is fully materialized in memory before serialization, risking OOM on large queries","No query timeout enforcement — runaway queries can block the server"],"requires":["MongoDB 4.0+","Valid MongoDB connection URI","Collection must exist in the database"],"input_types":["JSON filter objects (MongoDB query syntax)","Projection objects (field inclusion/exclusion)","Sort, skip, limit parameters as integers"],"output_types":["Array of JSON documents matching the query","Empty array if no matches"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_4","uri":"capability://data.processing.analysis.mongodb.aggregation.pipeline.execution.with.stage.composition","name":"mongodb aggregation pipeline execution with stage composition","description":"Executes MongoDB aggregation pipelines by accepting an array of stage objects ($match, $group, $project, $sort, $limit, etc.) and passing them directly to the aggregation framework. The server handles cursor iteration and result streaming, enabling LLMs to compose complex multi-stage transformations without writing imperative code.","intents":["Execute complex data transformations (grouping, counting, reshaping) through LLM-generated pipelines","Perform server-side aggregations to reduce data transfer and computation on the client","Enable LLMs to build analytical queries combining multiple stages"],"best_for":["Analytics and reporting systems powered by LLMs","Applications requiring complex data transformations without client-side processing","Teams building BI tools that accept natural language queries"],"limitations":["Pipeline syntax is complex and error-prone for LLMs to generate correctly without examples","No validation of stage syntax before execution — invalid stages fail at MongoDB runtime","Large intermediate result sets between stages can consume significant memory"],"requires":["MongoDB 3.2+ (basic aggregation) or 4.2+ (for advanced stages like $facet, $bucket)","Valid MongoDB connection URI"],"input_types":["Array of aggregation stage objects (MongoDB aggregation syntax)"],"output_types":["Array of transformed/aggregated documents","Results of final stage (e.g., grouped counts, reshaped objects)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_5","uri":"capability://memory.knowledge.collection.schema.inference.and.field.type.detection","name":"collection schema inference and field type detection","description":"Analyzes a MongoDB collection by sampling documents and inferring field names, types, and cardinality to build a schema representation. This schema is provided to LLMs as context so they understand the data structure before generating queries. The inference uses statistical sampling rather than scanning the entire collection, making it efficient for large datasets.","intents":["Provide LLMs with collection schema context so they generate valid queries without trial-and-error","Automatically discover field names and types without manual schema definition","Help LLMs understand data structure for complex nested documents"],"best_for":["LLM agents querying unfamiliar or dynamically-structured collections","Teams with schemaless MongoDB collections that need LLM-friendly documentation","Developers building exploratory query interfaces"],"limitations":["Schema inference is probabilistic — sampling may miss rare fields or type variations","Nested object structures are flattened or truncated, losing deep structural information","Type inference is heuristic-based and may misclassify fields (e.g., numeric strings as numbers)","Schema becomes stale if collection structure changes; requires manual refresh"],"requires":["MongoDB 4.0+","Collection must have at least one document","Read permission on the collection"],"input_types":["Collection name (string)"],"output_types":["Schema object with field names, inferred types, and cardinality statistics","JSON representation suitable for LLM context windows"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_6","uri":"capability://data.processing.analysis.document.count.queries.with.filter.support","name":"document count queries with filter support","description":"Executes MongoDB count operations with optional filter documents to return the number of documents matching a query. This is implemented as a dedicated tool (not just a find() with limit) to provide efficient counting without materializing result sets, leveraging MongoDB's optimized count operations.","intents":["Quickly determine how many documents match a filter without retrieving them","Implement pagination by counting total results before applying skip/limit","Build LLM prompts that include result cardinality (e.g., 'found 1,234 matching documents')"],"best_for":["LLM agents building paginated result sets","Analytics queries that need result cardinality","Applications requiring fast count operations on large collections"],"limitations":["Count operations on sharded clusters may be approximate if using countDocuments with skip/limit","No support for complex aggregation-based counting (e.g., distinct counts)","Filter syntax is limited to MongoDB query operators; cannot use aggregation stages"],"requires":["MongoDB 4.0+","Read permission on the collection"],"input_types":["Collection name (string)","Filter object (optional, MongoDB query syntax)"],"output_types":["Integer count of matching documents"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_7","uri":"capability://memory.knowledge.database.and.collection.metadata.introspection","name":"database and collection metadata introspection","description":"Provides two metadata operations: listCollections (returns all collection names in the database) and serverInfo (returns MongoDB server version, storage engine, and configuration details). These enable LLMs to discover available data sources and understand database capabilities before executing queries.","intents":["Allow LLMs to discover which collections exist in the database","Provide server version and capability information for query planning","Enable dynamic collection selection in multi-collection queries"],"best_for":["LLM agents exploring unfamiliar MongoDB instances","Applications building dynamic query interfaces","Teams needing to verify database connectivity and version compatibility"],"limitations":["listCollections does not return collection schemas or sizes — only names","serverInfo is read-only and does not reflect real-time resource usage","No support for listing databases (only collections in current database)"],"requires":["MongoDB 3.0+ (listCollections) or 2.6+ (serverInfo)","Read permission on the database"],"input_types":["None (introspection operations take no parameters)"],"output_types":["Array of collection names (listCollections)","Server metadata object with version, storage engine, etc. (serverInfo)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_8","uri":"capability://automation.workflow.document.insertion.with.automatic.objectid.generation","name":"document insertion with automatic objectid generation","description":"Inserts one or more documents into a MongoDB collection, with automatic _id field generation if not provided. The server accepts document objects, optionally converts string IDs to ObjectIds based on the configured conversion mode, and returns the inserted document IDs. This operation is blocked in read-only mode.","intents":["Allow LLM agents to create new records in MongoDB","Insert documents with LLM-generated content (e.g., summaries, extracted data)","Bulk insert multiple documents in a single operation"],"best_for":["LLM agents that need to persist generated content or analysis results","Applications building data ingestion pipelines powered by LLMs","Teams using LLMs to transform and load data into MongoDB"],"limitations":["Blocked in read-only mode — no write operations allowed","No transaction support for multi-document inserts across collections","No schema validation — LLMs can insert documents with unexpected field structures","Bulk insert may fail partially, leaving some documents inserted and others rejected"],"requires":["MongoDB 4.0+","Write permission on the collection","Read-only mode must be disabled"],"input_types":["Document object or array of document objects (JSON)","Optional _id field (auto-generated if omitted)"],"output_types":["Inserted document IDs (as strings after ObjectId conversion)","Acknowledgment of insertion success"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongodb__cap_9","uri":"capability://automation.workflow.document.update.with.filter.based.matching.and.field.modification","name":"document update with filter-based matching and field modification","description":"Updates one or more documents matching a filter using MongoDB update operators ($set, $inc, $push, etc.). The server accepts a filter object and an update object, executes the update operation, and returns the count of matched and modified documents. This operation is blocked in read-only mode.","intents":["Allow LLM agents to modify existing records based on query filters","Implement field updates generated by LLMs (e.g., status changes, data corrections)","Support bulk updates across multiple documents matching criteria"],"best_for":["LLM agents that need to modify data based on analysis or decisions","Applications building data correction or enrichment pipelines","Teams using LLMs to automate data maintenance tasks"],"limitations":["Blocked in read-only mode — no write operations allowed","No transaction support for updates across collections","Update operators are complex and error-prone for LLMs to generate ($set vs $inc vs $push)","No pre-update validation — LLMs can generate invalid update documents that fail at MongoDB runtime"],"requires":["MongoDB 4.0+","Write permission on the collection","Read-only mode must be disabled"],"input_types":["Filter object (MongoDB query syntax)","Update object (MongoDB update operators: $set, $inc, $push, etc.)","Optional upsert flag (boolean)"],"output_types":["Count of matched documents","Count of modified documents","Upserted document ID (if upsert=true and no documents matched)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["MCP-compatible client (Claude Desktop, Windsurf, Cursor, or custom MCP client)","Node.js 18+ runtime","MongoDB 4.0+ instance with network connectivity","MongoDB driver with ObjectId support (included in Node.js driver)","Configuration via environment variable or CLI flag at server startup","MongoDB 4.0+","Write permission on the collection","Read-only mode must be disabled","MCP-compatible client with resource fetching support","Valid MongoDB connection URI (mongodb:// or mongodb+srv://)"],"failure_modes":["Tool discovery is static at server startup — schema changes require server restart","No built-in rate limiting or quota enforcement at the MCP layer","LLM must correctly interpret schema to generate valid queries; malformed requests still reach MongoDB","'auto' mode uses heuristic field name matching and may incorrectly identify non-ID string fields as IDs","'force' mode converts all string fields that look like ObjectIds, risking false positives on legitimate string data","Conversion happens at query boundary — nested ObjectIds in aggregation pipelines may not convert correctly","Blocked in read-only mode — no write operations allowed","No index analysis or recommendation engine — LLMs must know which fields to index","Index creation is synchronous and blocks the server on large collections","No support for partial indexes or complex index types (e.g., text search, geospatial)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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","compare_url":"https://unfragile.ai/compare?artifact=mongodb"}},"signature":"TohasV/Aere9CpO0LduCdFCnXEBU3oBIHRhAdfpyZ7wc1OQZC08ahuOdjyrrPPjT2krjrKsZiXp46wmoFM3ZCQ==","signedAt":"2026-06-21T07:11:41.611Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mongodb","artifact":"https://unfragile.ai/mongodb","verify":"https://unfragile.ai/api/v1/verify?slug=mongodb","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"}}