AI Agent for Box MCP
Enhance your workflow with the Box MCP Server integration. Effortlessly connect AI-driven automation and data extraction to your Box cloud storage. Instantly search, manage, and analyze files and folders with robust Box API tools. Unlock advanced AI-powered querying, metadata handling, and document generation for seamless digital operations.
AI-Powered Box File Management
Unlock powerful file management and search capabilities within Box. Leverage advanced tools for locating, reading, uploading, and organizing files and folders. Enhance productivity with instant, AI-assisted file discovery and seamless content handling.
- Box File Search.
- Instantly search for files and folders using advanced filtering options, including file extensions and folder locations.
- Upload & Download.
- Seamlessly upload files from your local system or content, and download files from Box directly.
- Folder Management.
- Create, update, and organize folders with ease for optimized file structure and accessibility.
- Content Preview.
- Read and preview file content with a single click for quick assessment and action.
AI Query & Data Extraction
Leverage Box AI to query documents, extract structured data, and analyze content across single or multiple files and hubs. Automate document understanding and data workflows with powerful natural language AI tools.
- Ask Box AI.
- Use natural language to ask questions about files and hubs, and receive intelligent responses powered by Box AI.
- AI Data Extraction.
- Extract key data and fields from your files using AI-driven extraction tools for both structured and freeform formats.
- Aggregate Analysis.
- Analyze multiple files and aggregate insights with multi-file AI querying.
Advanced Metadata & Document Generation
Automate metadata management and document generation at scale. Create, update, or delete metadata templates and instances. Generate documents in batch using Box Doc Gen for streamlined workflows and compliance.
- Metadata Automation.
- Create and manage metadata templates and instances to classify, enrich, and search your Box content.
- Batch Document Generation.
- Generate documents at scale using templates and input data for efficient content creation and compliance.
- Template-Based Extraction.
- Extract structured data from documents using predefined templates for reliable business processes.
MCP INTEGRATION
Available Box MCP Integration Tools
The following tools are available as part of the Box MCP integration:
- box_who_am_i
Get current user information and check connection status to the Box account.
- box_authorize_app_tool
Initiate the Box application authorization process and receive authorization status.
- box_search_tool
Search for files in Box with flexible query, filters, and folder scoping options.
- box_read_tool
Read the text content of a Box file by providing its file ID.
- box_search_folder_by_name
Locate a Box folder by its name and retrieve the folder ID.
- box_search_folder_by_name_tool
Locate folders in Box by name, returning matching names and IDs.
- box_ask_ai_tool
Ask Box AI a question about a specific file and receive an AI-generated response.
- box_hubs_ask_ai_tool
Query Box AI about a hub using its ID and a custom prompt.
- box_ai_extract_data
Extract specific data fields from a file using Box AI, returning results in JSON.
- box_list_folder_content_by_folder_id
List the contents of a Box folder by folder ID, with optional recursion.
- box_manage_folder_tool
Create, update, or delete folders in Box with support for folder hierarchy and details.
- box_ai_ask_file_single_tool
Query Box AI about a single file, optionally using a specific AI agent.
- box_ai_ask_file_multi_tool
Query Box AI about multiple files at once, aggregating insights in the response.
- box_ai_ask_hub_tool
Ask Box AI about a hub, with support for custom prompts and AI agents.
- box_ai_extract_freeform_tool
Extract data from files using Box AI with a freeform prompt for flexible output.
- box_ai_extract_structured_using_fields_tool
Extract structured data from files using AI by specifying fields to capture.
- box_ai_extract_structured_using_template_tool
Extract structured data from files using AI with a designated template.
- box_ai_extract_structured_enhanced_using_fields_tool
Extract enhanced structured data from files using AI and specified fields.
- box_ai_extract_structured_enhanced_using_template_tool
Extract enhanced structured data from files using AI and a template for processing.
- box_upload_file_from_path_tool
Upload a file to Box from a local filesystem path, assigning to a folder.
- box_upload_file_from_content_tool
Upload content as a new file to Box, supporting text or binary data and base64 encoding.
- box_download_file_tool
Download a file from Box, with options to save locally or return content.
- box_metadata_template_create_tool
Create a new metadata template for use with files and folders in Box.
- box_metadata_template_get_by_key_tool
Retrieve a metadata template by its unique key.
- box_metadata_template_get_by_name_tool
Retrieve a metadata template by its display name.
- box_metadata_set_instance_on_file_tool
Set a metadata instance on a file using a specified template and metadata values.
- box_metadata_get_instance_on_file_tool
Get a metadata instance assigned to a file for a given template.
- box_metadata_update_instance_on_file_tool
Update the metadata instance on a file, with optional removal of non-included data.
- box_metadata_delete_instance_on_file_tool
Delete a metadata instance from a file based on a template key.
- box_docgen_create_batch_tool
Generate documents in batch using a Box Doc Gen template and local input data.
- box_docgen_get_job_tool
Fetch information about a specific Box Doc Gen job by its ID.
- box_docgen_list_jobs_tool
List all Box Doc Gen jobs associated with the current user, with pagination support.
- box_docgen_list_jobs_by_batch_tool
List Box Doc Gen jobs belonging to a specific batch, with filtering options.
- box_docgen_template_create_tool
Create a new template for Box Doc Gen document generation workflows.
Connect Your Box MCP Server with FlowHunt AI
Connect your Box MCP Server to a FlowHunt AI Agent. Book a personalized demo or try FlowHunt free today!
What is MCP Servers
MCP Servers is a platform that provides the largest collection of Model Context Protocol (MCP) servers, enabling seamless integration and interaction between AI agents and various online content and data sources. The platform offers a curated directory of public MCP servers, which can be used by AI applications to enhance their capabilities, retrieve information, automate workflows, and connect to specialized tools. MCP Servers streamlines the discovery, connection, and use of these servers, making it easier for developers and businesses to leverage the power of AI and automation in their products and services. The site also highlights integrations with popular AI tools, such as Claude and Copilot Studio, to facilitate powerful, context-aware interactions.
Capabilities
What we can do with MCP Servers
MCP Servers allows users and developers to discover, connect, and utilize a variety of MCP servers to supercharge their AI agents and digital workflows. Through the platform, you can search for servers by capability, integrate with third-party AI tools, and connect your applications to new data sources and automation endpoints.
- Server Discovery
- Easily browse and search a large catalog of public MCP servers tailored for specific tasks or data needs.
- AI Integration
- Connect AI agents like Claude, Copilot Studio, or Cursor to new data sources and services using MCP protocol.
- Workflow Automation
- Automate complex business processes by connecting multiple MCP servers in a single workflow.
- Custom Server Submission
- Add your own server to the directory, making it accessible to AI agents worldwide.
- Real-Time Interaction
- Enable real-time, context-aware AI interactions by leveraging up-to-date server data and capabilities.
How AI Agents Benefit from MCP Servers
AI agents gain tremendous flexibility and expanded functionality by integrating with MCP Servers. They can access a wide array of specialized data sources, trigger automation endpoints, and enhance their contextual understanding, resulting in richer, more powerful user experiences. This empowers AI agents to perform research, automate repetitive tasks, and deliver contextually relevant responses across different platforms and use cases—all by leveraging the open, extensible MCP ecosystem.