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The MCP server acts as a middleware that receives CallTool requests, extracts query parameters, and executes them against the MongoDB driver, returning structured results back to the LLM for interpretation.","intents":["Query a MongoDB collection without writing complex query syntax","Let an LLM autonomously fetch, filter, and analyze database records","Enable natural language data exploration across multiple collections"],"best_for":["LLM application developers building database-aware agents","Teams integrating MongoDB with Claude Desktop or other MCP-compatible clients","Developers wanting to expose database operations to LLMs without custom API layers"],"limitations":["No query optimization or cost estimation — LLM may generate inefficient queries with large result sets","Requires LLM to understand MongoDB query syntax semantics; complex aggregation pipelines not directly supported","No built-in query validation or injection prevention beyond MongoDB driver's native protections","Single-document operations only (updateOne, deleteOne) — bulk operations not exposed"],"requires":["Node.js 14+ runtime","MongoDB 4.0+ database instance with network access","MCP-compatible LLM client (Claude Desktop 0.1.0+)","Valid MongoDB connection string (mongodb:// or mongodb+srv://)"],"input_types":["natural language query descriptions from LLM","structured tool parameters (collection name, filter, projection, update documents)"],"output_types":["JSON documents matching query results","operation status (success/failure with error messages)","document counts and modification counts"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_1","uri":"capability://data.processing.analysis.automatic.mongodb.schema.inference.and.inspection","name":"automatic mongodb schema inference and inspection","description":"Analyzes MongoDB collections to infer and expose their schema structure to LLMs by sampling documents and extracting field names, types, and cardinality information. 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The tool enables LLMs to remove records based on filter criteria, with safeguards to prevent accidental bulk deletions (only deletes one document per invocation). This allows LLMs to clean up data or remove obsolete records.","intents":["Delete specific documents from MongoDB collections based on filter criteria","Enable LLMs to clean up or remove obsolete records","Allow data correction workflows that require record removal"],"best_for":["LLM agents performing data cleanup or maintenance","Systems enabling natural language data deletion","Teams building autonomous database management workflows"],"limitations":["Single document deletion only — no bulk delete support (prevents accidental data loss)","No soft delete or archival support — deletion is permanent","No undo capability — deleted documents cannot be recovered","No audit trail of deletions","Filter must match exactly one document — ambiguous filters may delete unexpected records"],"requires":["MongoDB 4.0+ with write access to collections","Valid MongoDB filter syntax to identify document"],"input_types":["collection name (string)","filter object (JSON with MongoDB operators)"],"output_types":["number of deleted documents (0 or 1)","operation status (success/failure)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_2","uri":"capability://tool.use.integration.index.management.and.optimization.discovery","name":"index management and optimization discovery","description":"Exposes MongoDB index operations (createIndex, dropIndex, listIndexes) as MCP tools, allowing LLMs to inspect existing indexes, create new ones for query optimization, and remove unused indexes. The implementation wraps MongoDB's native index APIs and provides structured tool interfaces that LLMs can invoke to analyze and optimize database performance based on query patterns.","intents":["List all indexes on a collection to understand current optimization state","Create indexes on frequently queried fields to improve query performance","Remove redundant or unused indexes to reduce storage overhead","Help LLMs suggest index creation based on observed query patterns"],"best_for":["Database administrators using LLM agents for performance optimization","Developers building autonomous database tuning systems","Teams needing to expose index management to non-technical users via LLM interface"],"limitations":["No index usage statistics or query plan analysis — LLM cannot see which indexes are actually used","No support for partial indexes, text indexes, or geospatial indexes","Index creation is synchronous and may block on large collections","No validation that proposed indexes will improve query performance"],"requires":["MongoDB 4.0+ with write access to target collections","Appropriate database permissions (createIndex, dropIndex)"],"input_types":["collection name (string)","field specification for index creation (e.g., {field: 1} for ascending)","index name (string, optional)"],"output_types":["list of existing indexes with field specifications and options","confirmation of index creation/deletion with index name","error messages if index operations fail"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_3","uri":"capability://tool.use.integration.mcp.protocol.server.with.stdio.transport","name":"mcp protocol server with stdio transport","description":"Implements a Model Context Protocol (MCP) server using the MCP SDK that communicates with LLM clients via stdio (standard input/output) transport. The server initializes with metadata, registers tool handlers for ListTools and CallTool requests, and manages the request-response lifecycle. This architecture enables seamless integration with MCP-compatible clients like Claude Desktop without requiring HTTP servers or custom protocol implementations.","intents":["Enable Claude Desktop and other MCP clients to discover MongoDB tools","Handle tool invocation requests from LLMs and return structured results","Manage the MCP protocol handshake and request routing automatically"],"best_for":["Developers integrating MongoDB with Claude Desktop","Teams building MCP servers for database access","LLM application developers needing standardized database tool interfaces"],"limitations":["Stdio transport only — no HTTP or WebSocket support for remote clients","Single connection per server instance — does not support concurrent client connections","No built-in authentication or authorization — relies on MongoDB connection string security","Server must be running as a subprocess of the MCP client"],"requires":["Node.js 14+ runtime","MCP SDK (@modelcontextprotocol/sdk)","MCP-compatible client (Claude Desktop 0.1.0+)","Server must be configured in client's MCP settings"],"input_types":["MCP ListTools requests (no parameters)","MCP CallTool requests with tool name and arguments"],"output_types":["MCP ListTools response with available tools and schemas","MCP CallTool response with tool execution results or errors"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_4","uri":"capability://tool.use.integration.mongodb.connection.management.and.client.pooling","name":"mongodb connection management and client pooling","description":"Manages MongoDB database connections by parsing connection strings from command-line arguments, establishing connections using the MongoDB Node.js driver, and maintaining a client instance for the server's lifetime. The client module (src/mongodb/client.ts) handles connection initialization, error handling, and provides a reusable connection pool that all tools share, ensuring efficient resource utilization and preventing connection exhaustion.","intents":["Establish and maintain a persistent connection to MongoDB","Reuse a single connection pool across multiple tool invocations","Handle connection failures and provide meaningful error messages"],"best_for":["Developers deploying mongo-mcp servers in production","Teams needing reliable MongoDB connectivity from LLM agents","Systems requiring connection pooling for high-frequency database access"],"limitations":["Connection string must be provided at server startup — no dynamic connection switching","No support for connection authentication beyond URI-embedded credentials","Connection pool size is determined by MongoDB driver defaults — not configurable","No connection health checks or automatic reconnection on network failures"],"requires":["MongoDB 4.0+ instance with network accessibility","Valid MongoDB connection string (mongodb:// or mongodb+srv://)","Network connectivity from server to MongoDB host","MongoDB Node.js driver 4.0+"],"input_types":["MongoDB connection string (passed as command-line argument)"],"output_types":["MongoDB client instance ready for operations","connection status and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_5","uri":"capability://tool.use.integration.tool.registry.and.dynamic.tool.discovery","name":"tool registry and dynamic tool discovery","description":"Implements a ToolRegistry that dynamically registers MongoDB operations as discoverable tools with JSON schema definitions. The registry maintains metadata for each tool (name, description, input schema) and exposes them through the MCP ListTools handler, allowing LLM clients to discover available operations and their parameters before invoking them. This enables LLMs to understand tool capabilities and construct valid invocations.","intents":["Allow LLM clients to discover available MongoDB operations at runtime","Provide JSON schema definitions for tool parameters to guide LLM tool construction","Enable LLMs to understand tool descriptions and choose appropriate operations"],"best_for":["LLM application developers building database-aware agents","Teams needing self-documenting tool interfaces for LLMs","Developers extending mongo-mcp with custom tools"],"limitations":["Tool schemas are static — no runtime schema generation based on database state","No support for conditional tool availability based on permissions or database state","Tool descriptions are hardcoded — no automatic documentation generation","Registry does not validate tool invocations against schemas at runtime"],"requires":["MCP SDK for tool registration and schema definition","JSON schema definitions for each tool's parameters"],"input_types":["tool name (string)","tool parameters matching registered schema"],"output_types":["list of available tools with names, descriptions, and JSON schemas","tool execution results"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_6","uri":"capability://data.processing.analysis.collection.listing.and.database.introspection","name":"collection listing and database introspection","description":"Exposes a listCollections tool that queries MongoDB's system metadata to enumerate all collections in the connected database. This tool provides LLMs with visibility into available collections without requiring manual documentation, enabling data exploration and helping LLMs select appropriate collections for queries. The implementation wraps MongoDB's native listCollections API.","intents":["Discover all available collections in a MongoDB database","Enable LLMs to explore database structure autonomously","Help LLMs select the correct collection for a query"],"best_for":["Developers building data exploration agents","Teams needing to expose database structure to LLMs","Systems requiring automated collection discovery"],"limitations":["Returns only collection names — no collection statistics or metadata","Does not filter collections by permissions or visibility","No support for views or system collections","Requires read access to the database's system.namespaces collection"],"requires":["MongoDB 4.0+ with read access to database","Appropriate database permissions"],"input_types":["database name (implicit from connection string)"],"output_types":["array of collection names (strings)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_7","uri":"capability://data.processing.analysis.document.filtering.and.projection.with.mongodb.query.syntax","name":"document filtering and projection with mongodb query syntax","description":"Implements the find tool that accepts MongoDB filter and projection objects, allowing LLMs to query documents with complex filtering criteria and field selection. The tool translates LLM-provided filter objects into MongoDB query syntax, executes the query against the specified collection, and returns matching documents with only requested fields. This enables sophisticated data retrieval without exposing raw MongoDB query language to the LLM.","intents":["Query documents with complex filter conditions (equality, comparison, logical operators)","Select specific fields from documents using projection","Retrieve paginated results with limit and skip parameters","Enable LLMs to construct data-driven decisions based on filtered results"],"best_for":["LLM agents performing data analysis and exploration","Teams building database-driven decision systems","Developers needing flexible query capabilities without exposing MongoDB syntax"],"limitations":["No support for aggregation pipelines — only simple find queries","Filter objects must be valid MongoDB query syntax — LLM must understand operators like $eq, $gt, $in","No query optimization or explain plan analysis","Result sets are not streamed — entire result set loaded into memory","No support for text search or full-text indexing"],"requires":["MongoDB 4.0+ with read access to collections","Valid MongoDB filter syntax (JSON objects with operators)"],"input_types":["collection name (string)","filter object (JSON with MongoDB operators)","projection object (JSON with field inclusion/exclusion)","limit (integer, optional)","skip (integer, optional)"],"output_types":["array of matching documents (JSON)","document count"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_8","uri":"capability://data.processing.analysis.document.insertion.with.validation.and.error.handling","name":"document insertion with validation and error handling","description":"Implements the insertOne tool that accepts a document object and inserts it into a specified collection, returning the inserted document's ID and operation status. The tool validates document structure against MongoDB constraints, handles insertion errors (duplicate keys, validation failures), and provides meaningful error messages to the LLM. This enables LLMs to create new records while maintaining data integrity.","intents":["Insert new documents into MongoDB collections from LLM-generated data","Create records based on LLM reasoning or user input","Enable LLMs to persist data and maintain state across conversations"],"best_for":["LLM agents that need to persist data or create records","Systems building data entry workflows through natural language","Teams enabling LLMs to modify databases autonomously"],"limitations":["Single document insertion only — no bulk insert support","No support for transactions or multi-document ACID guarantees","Duplicate key errors require LLM to handle and retry with modified data","No validation against MongoDB schema validation rules before insertion","No support for default values or computed fields"],"requires":["MongoDB 4.0+ with write access to collections","Valid MongoDB document (JSON object)"],"input_types":["collection name (string)","document object (JSON)"],"output_types":["inserted document ID (_id field)","operation status (success/failure)","error messages if insertion fails"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mongo__cap_9","uri":"capability://data.processing.analysis.document.update.with.field.level.modifications","name":"document update with field-level modifications","description":"Implements the updateOne tool that accepts a filter to identify a document and an update object specifying field modifications (using MongoDB update operators like $set, $inc, $push). The tool executes the update operation and returns the number of matched and modified documents, enabling LLMs to modify existing records with fine-grained control over which fields change.","intents":["Update specific fields in existing documents without replacing entire records","Increment counters or append values to arrays using MongoDB operators","Enable LLMs to modify data based on analysis or user requests","Maintain document history by selectively updating fields"],"best_for":["LLM agents performing data corrections or enrichment","Systems enabling natural language data modification","Teams building autonomous database maintenance workflows"],"limitations":["Single document update only — no bulk updates","Update operators must be valid MongoDB syntax ($set, $inc, etc.) — LLM must understand operator semantics","No support for conditional updates or complex update logic","No transaction support — updates are not atomic across multiple documents","No audit trail or change tracking built-in"],"requires":["MongoDB 4.0+ with write access to collections","Valid MongoDB filter syntax to identify document","Valid MongoDB update operator syntax"],"input_types":["collection name (string)","filter object (JSON with MongoDB operators)","update object (JSON with update operators like $set, $inc, $push)"],"output_types":["number of matched documents","number of modified documents","operation status (success/failure)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ runtime","MongoDB 4.0+ database instance with network access","MCP-compatible LLM client (Claude Desktop 0.1.0+)","Valid MongoDB connection string (mongodb:// or mongodb+srv://)","MongoDB 4.0+ with read access to target collections","Collection must contain at least one document for schema inference","MongoDB 4.0+ with write access to collections","Valid MongoDB filter syntax to identify document","MongoDB 4.0+ with write access to target collections","Appropriate database permissions (createIndex, dropIndex)"],"failure_modes":["No query optimization or cost estimation — LLM may generate inefficient queries with large result sets","Requires LLM to understand MongoDB query syntax semantics; complex aggregation pipelines not directly supported","No built-in query validation or injection prevention beyond MongoDB driver's native protections","Single-document operations only (updateOne, deleteOne) — bulk operations not exposed","Schema inference is probabilistic — sampling documents may miss rare fields or type variations","No support for complex types like nested objects or arrays with heterogeneous elements","Inference cost scales with collection size; large collections may timeout or require sampling limits","Does not capture MongoDB schema validation rules, indexes, or constraints","Single document deletion only — no bulk delete support (prevents accidental data loss)","No soft delete or archival support — deletion is permanent","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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=mongo","compare_url":"https://unfragile.ai/compare?artifact=mongo"}},"signature":"DhVYwB3EkkWxdPGlj7bz13gXdIC6sg5XI3qQ4cPHMBGTfJRHmx+1q4YNj97f2nhEN5zQKXxoPEpv314Wdy7+Dg==","signedAt":"2026-06-21T22:04:35.261Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mongo","artifact":"https://unfragile.ai/mongo","verify":"https://unfragile.ai/api/v1/verify?slug=mongo","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"}}