Brightsy AI provides comprehensive Model Context Protocol (MCP) integration through multiple deployment options, enabling seamless integration with AI platforms like Claude Desktop, Cursor, Aider, and other MCP-compatible applications.
What is MCP?
Model Context Protocol (MCP) is an open standard that enables AI applications to connect to external data sources and tools through a client-server architecture.
MCP Architecture
MCP Clients: AI applications (Cursor, Claude Desktop, etc.) that want to use external tools
MCP Servers: Services that provide tools and resources to clients
Protocol: JSON-RPC 2.0 over stdio (local) or HTTP (remote) transports
Brightsy MCP Integration
Brightsy provides both MCP client and server implementations:
As an MCP Client: Brightsy Desktop can connect to any MCP server
As an MCP Server: Brightsy MCP server provides tools for accessing Brightsy platform features
Full Platform Access: AI agents can interact with records, files, agents, sites, and more through natural language
This enables AI assistants to have deep, contextual understanding of your Brightsy workspace and perform complex operations autonomously.
MCP Integration Options
Brightsy offers flexible MCP integration through clients and servers. MCP uses a client-server architecture where clients connect to servers to access tools and resources.
1. Hosted MCP Server (HTTP) — Recommended for Cursor
Best for: Full Brightsy platform access in Cursor and other stdio MCP clients — no global install required.
HTTP Transport: Hosted at https://mcp.brightsy.ai/mcp
OAuth 2.1: Secure authentication with auto-discovery on first tool use
Cloud Tools: Agents, records/CMS, files, scenarios, models, and more
2. Standalone stdio MCP Server
Best for: Local filesystem and terminal tools (local_read_file, local_run_command, etc.) alongside or instead of hosted MCP.
NPM Package: @brightsy/mcp-server
Local Tools: Filesystem, terminal, and CLI discovery (no auth required)
Different tool surface: Cloud tools on hosted MCP are richer than the stdio package's cloud bridge
: Comprehensive platform documentation for AI agents
Documentation Resources
Flexible Deployment: Can run locally or in any Node.js environment
3. Brightsy Desktop (Bundled MCP Server + Client)
Best for: Complete MCP experience with local development tools and cloud platform access.
Bundled MCP Server: Brightsy MCP server (@brightsy/mcp-server) included with desktop app
MCP Client: Connects to and manages MCP server connections
Local + Cloud Tools: Filesystem, terminal, CMS, agents, and more
Workspace Context: Provides workspace information to MCP servers
Authentication Sharing: Uses desktop app OAuth credentials for cloud tools
Tool Discovery: Automatically discovers tools from connected servers
Brightsy Desktop MCP
Brightsy Desktop provides the most comprehensive MCP experience with the Brightsy MCP server bundled and full client capabilities. The desktop app includes both MCP client functionality (to connect to servers) and the Brightsy MCP server itself (to provide tools), making setup seamless.
Log in: Open Brightsy Desktop and authenticate with your account
Configure AI Tool: The Brightsy MCP server is bundled with the desktop app - just configure your AI tool to connect to it
Using Desktop with MCP Servers
The desktop app includes the Brightsy MCP server as a bundled dependency, providing seamless access to both local development tools and cloud platform features.
Option A: Desktop MCP Server (Recommended)
The Brightsy MCP server is pre-installed with the desktop app and automatically reads allowed directories from your desktop configuration. Simply configure your AI tools to connect to it:
MCP Server Discovery: Automatically discovers and connects to configured MCP servers
Workspace Integration: Passes workspace context to MCP servers for file and terminal operations
Authentication Sharing: Uses desktop app credentials when connecting to authenticated MCP servers
Multi-Server Support: Can connect to multiple MCP servers simultaneously
Tool Management: Loads and manages tools from all connected MCP servers
Error Handling: Provides user-friendly error messages for MCP server issues
Note: The Brightsy MCP server is bundled with the desktop app, but you still need to configure your AI tools to connect to it. The desktop app provides both client and server capabilities for a complete MCP experience.
Standalone MCP Server
For standalone deployments or custom configurations, use the @brightsy/mcp-server npm package.
The standalone server supports multiple authentication methods:
OAuth 2.1 with PKCE: Automatic browser-based authentication
API Key: For server-to-server scenarios
CLI Login: Use brightsy login to authenticate
Environment Variables
BRIGHTSY_ALLOWED_DIRS: Comma-separated paths for local file access (defaults to current directory)
BRIGHTSY_API_KEY: API key for authentication (alternative to OAuth)
BRIGHTSY_ACCOUNT_ID: Account ID when using API key authentication
Complete Cursor Configuration Guide
Cursor speaks MCP over stdio. Brightsy's hosted MCP server runs over HTTP. Use mcp-remote as a bridge — add servers to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project).
Recommended: Brightsy
Add Brightsy to connect Cursor to Brightsy's hosted MCP server:
Hosted Brightsy provides the full cloud tool surface with OAuth. Stdio brightsy-workspace adds local_read_file, local_run_command, and other workspace-only tools.
Note: When using Brightsy Desktop, most configuration is automatically detected from the desktop app's settings and shared config file. Manual environment variables are only needed for advanced customization or standalone MCP server usage.
Configuration Properties
Property
Description
Type
command
Executable command
string
args
Command arguments array
string[]
env
Environment variables object
object
timeout
Connection timeout (ms)
number
initializationOptions
MCP initialization options
object
Hosted MCP Server
Brightsy exposes the full hosted tool surface — agents, CMS, files, automation, and more. OAuth is required for cloud tools. Filesystem/terminal tools (local_*) are only available through the stdio @brightsy/mcp-server package, not hosted MCP.
Available Tools by Category
Workspace Tools (stdio package only — no auth)
These tools are available via brightsy-mcp (stdio), not via Brightsy:
Filesystem Operations:
local_read_file: Read file contents from allowed directories
local_write_file: Write or update files in allowed directories
local_list_files: List directory contents
local_search_files: Search for files by pattern
local_grep: Search file contents with regex
Terminal & Process Management:
local_run_command: Execute shell commands with timeout/buffer options
start_managed_process: Start long-running processes with monitoring
chat: Chat with an agent (supports multi-turn conversations)
Direct AI Model Access:
list_models: List available AI models with capabilities
complete: Direct model completion without agent wrapper
Content Management (Records & CMS):
get_record_types: List all record type schemas
get_record_type: Get specific record type schema
create_record_type: Create new record type
update_record_type: Modify record type
delete_record_type: Remove record type
list_records: Query records with filtering and pagination
get_record: Get single record
create_record: Create new record
update_record: Modify record
delete_record: Remove record
publish_record: Publish draft record to live
File Management:
list_files: List files in storage
upload_file: Upload file to storage
get_upload_url: Get signed upload URL
delete_file: Remove file
get_signed_url: Get signed download URL
Automation:
list_scenarios: List automation scenarios
get_scenario: Get scenario details
create_scenario: Create new scenario
update_scenario: Modify scenario
delete_scenario: Remove scenario
trigger_scenario: Execute scenario manually
list_schedules: List scheduled tasks
get_schedule: Get schedule details
create_schedule: Create new schedule
update_schedule: Modify schedule
delete_schedule: Remove schedule
API Management:
list_apikeys: List API keys
create_apikey: Create new API key
delete_apikey: Remove API key
list_webhooks: List webhooks
create_webhook: Create new webhook
update_webhook: Modify webhook
delete_webhook: Remove webhook
Chat Management Tools
clear_chat: End conversation and clear history
get_chat_history: Retrieve conversation history (when supported)
AI Image Tools
analyze_image: Analyze image content
generate_image: Generate new images
suggest_image_edits: Get image editing suggestions
Setup Instructions
Choose the MCP integration that best fits your needs:
Option 1: Hosted MCP via mcp-remote (Recommended for Cursor)
For Cursor and other stdio-only clients, use mcp-remote to connect to Brightsy's hosted MCP server. OAuth is handled automatically on first use — no global Brightsy install required.
Configure your AI tool (~/.cursor/mcp.json or .cursor/mcp.json):
With Brightsy, you get the full hosted tool surface after OAuth:
You: List all my Brightsy agents
Claude: [calls list_agents tool via Brightsy]
Claude: You have 3 agents available:
1. Customer Support Agent (agent-123) - Handles customer inquiries
2. Data Analysis Agent (agent-456) - Performs data analysis tasks
3. Content Writer Agent (agent-789) - Creates marketing content
You: Create a new blog post about React 19
Claude: [calls create_record tool with record_type='blog-post']
Claude: Created blog post with ID: post-123
Title: "Exploring React 19 Features"
Content: "React 19 introduces..."
Desktop / stdio MCP Examples
With Brightsy Desktop or the stdio brightsy-mcp package, you also get local workspace tools:
You: Read the package.json file in my current project
Claude: [calls local_read_file tool]
Claude: Here's your package.json:
{
"name": "my-project",
"version": "1.0.0",
...
}
You: Run npm test in my project
Claude: [calls local_run_command tool]
Claude: Running npm test...
✓ All tests passed
You: Create a new blog post about React 19
Claude: [calls create_record tool with record_type='blog-post']
Claude: Created blog post with ID: post-123
Title: "Exploring React 19 Features"
Content: "React 19 introduces..."
Cloud Platform Examples
For hosted MCP (Brightsy):
You: List all my Brightsy agents
Claude: [calls list_agents tool]
Claude: You have 3 agents available:
1. Customer Support Agent (agent-123) - Handles customer inquiries
2. Data Analysis Agent (agent-456) - Performs data analysis tasks
3. Content Writer Agent (agent-789) - Creates marketing content
You: Ask my Customer Support Agent about order #12345
Claude: [calls chat tool with agent_id and message]
Claude: The agent says: Order #12345 was shipped on Jan 15th and is expected to arrive by Jan 20th...
You: Start a conversation with my Data Analysis Agent about Q4 sales
Claude: [calls chat tool, receives chat_id in response]
Claude: The agent says: I'd be happy to analyze Q4 sales data. What specific metrics are you interested in?
You: Focus on revenue by region
Claude: [calls chat tool with chat_id to continue conversation]
Claude: Based on the Q4 data, here are the revenue figures by region:
- North America: $2.4M
- Europe: $1.8M
- Asia Pacific: $1.2M
You: End this conversation
Claude: [calls clear_chat tool]
Claude: Conversation ended and history cleared.
SDK Integration Examples
MCP tools complement the BrightsyClient SDK for programmatic access:
import { BrightsyClient } from '@brightsy/client';
const client = new BrightsyClient({
api_key: 'bsy_...',
account_id: 'account-uuid'
});
// MCP can help discover available record types
const types = await client.cma().listRecordTypes();
// Then work with specific types
const posts = await client.cda()
.recordType('blog-post')
.where('status', 'eq', 'published')
.get();
CLI Integration Examples
The Brightsy CLI works alongside MCP for development workflows:
# MCP can help with CLI operations
brightsy create my-component-library
cd my-component-library
brightsy connect # MCP helps select site/library
brightsy push --dev # Register with MCP-configured environments
BrightsyClient SDK Integration
MCP tools provide AI-assisted access to Brightsy's TypeScript/JavaScript SDK, making it easier to discover and use platform features:
Installation
npm install @brightsy/client
Basic Usage
import { BrightsyClient } from '@brightsy/client';
const client = new BrightsyClient({
api_key: 'bsy_...',
account_id: 'account-uuid'
});
// MCP tools can help you discover available APIs
// Use list_agents, get_record_types, etc. through MCP
// Then use the SDK programmatically
const agents = await client.agents().list();
const records = await client.cda()
.recordType('blog-post')
.where('published', 'eq', true)
.get();
SDK Features Accessible via MCP
Content Management: CMA/CDA/CPA APIs for draft/published/preview content
Agent Management: Create, chat, and manage AI agents
File Operations: Upload, download, and manage files with signed URLs
Site Building: Visual page builder and component libraries
Automation: Scenarios, schedules, and webhooks
Direct AI: Model access without agent wrappers
Brightsy CLI Integration
The Brightsy CLI (@brightsy/cli) provides command-line access to platform features and works seamlessly with MCP:
# Agent interactions (similar to MCP chat tools)
brightsy chat "Hello!" # Chat with default agent
brightsy chat --agent <id> "Message" # Chat with specific agent
brightsy chat --model gpt_5_2 "Query" # Direct model completion
brightsy agents # List available agents
# Content management
brightsy records list blog-post # List records
brightsy records create blog-post # Create record
brightsy record-types list # List schemas
# Component development
brightsy create my-components # Scaffold library
brightsy connect # Link to site
brightsy push --dev # Deploy to dev
brightsy push # Deploy to production
MCP + CLI Workflow
MCP and CLI complement each other:
Use MCP for AI-assisted discovery and complex operations
Use CLI for repetitive tasks and scripting
Both share the same authentication and account context
Example workflow:
# MCP discovers available record types
# MCP: "You have 'blog-post' and 'product' record types"
# CLI creates content based on MCP guidance
brightsy records create blog-post --data '{"title":"New Post","content":"..."}'
# MCP helps with deployment
# MCP: "Your component library is ready. Run 'brightsy push' to deploy"
Chat Session Management
Stateless Mode (Default)
By default, each chat is independent:
No chat_id is provided
Agent has no memory of previous messages
Best for one-off queries
Stateful Mode
To maintain conversation history:
Provide a chat_id to continue a conversation
The MCP client automatically manages the chat_id
Sessions expire after 1 hour of inactivity
Active sessions refresh their expiration on every message
Session Isolation
Each user's chats are completely isolated
Team members can't access each other's conversations
Redis provides persistent storage across server restarts
Advanced Features
UI Tool Auto-Handling
When agents use UI tools (like show_buttons or present_content), the MCP server automatically:
Detects the UI tool call
Generates a mock response
Continues the conversation seamlessly
This allows agents designed for web interfaces to work through MCP without modification.
Sliding Expiration
Chat sessions use Redis with sliding expiration:
Sessions expire after 1 hour of inactivity
Every message resets the timer
Active conversations never expire
Old sessions auto-cleanup
Concurrent Conversations
You can maintain multiple simultaneous conversations:
Each has a unique chat_id
Switch between conversations seamlessly
Clear individual conversations when done
Troubleshooting
Brightsy Desktop Issues
Problem: "brightsy-desktop-mcp command not found"
Solutions:
Ensure Brightsy Desktop is installed and running
Restart your AI tool (Cursor/Claude Desktop) after installing Desktop
Check that Brightsy Desktop is logged in to your account