MCP Tools
95+ Model Context Protocol tools for seamless Claude integration
Pre-built integrations for GitHub, Docker, browser automation, databases, and more. Connect Adverant Nexus to Claude Desktop or Claude Code in minutes.
What is MCP?
Model Context Protocol (MCP) is an open standard for connecting AI models to external tools and data sources. Think of it as a universal adapter that lets Claude interact with your applications, databases, and services.
Fast Integration
Connect in minutes, not days
Secure by Default
Built-in authentication and permissions
Extensible
Build custom tools easily
Tool Categories
GitHub Integration
30+ toolsComplete GitHub operations including repositories, issues, pull requests, reviews, and code search.
Featured Tools:
create_repositoryfork_repositorycreate_pull_requestmerge_pull_requestcreate_issueupdate_issueadd_issue_commentcreate_branch+6 moreFilesystem Operations
15+ toolsRead, write, edit, and manage files and directories with advanced search capabilities.
Featured Tools:
read_text_fileread_media_fileread_multiple_fileswrite_fileedit_filecreate_directorylist_directorydirectory_tree+3 moreBrowser Automation
20+ toolsFull browser control with Playwright including navigation, interaction, and screenshot capture.
Featured Tools:
browser_navigatebrowser_snapshotbrowser_clickbrowser_typebrowser_fill_formbrowser_screenshotbrowser_evaluatebrowser_wait_for+5 moreDocker Management
10+ toolsContainer and image management with Docker commands and Kubernetes operations.
Featured Tools:
docker_commandkubectl_commandget_k8s_podsget_k8s_servicesapply_k8s_manifestdelete_k8s_resourcerestart_serviceget_logsDatabase Tools
15+ toolsQuery and manage PostgreSQL, Redis, Neo4j, and Qdrant databases.
Featured Tools:
query_databasebrain_store_memorybrain_recall_memorybrain_store_documentbrain_retrievebrain_store_entitybrain_query_entitiesbrain_get_entity+1 moreWeb Scraping
5+ toolsAdvanced web scraping with Firecrawl for content extraction and search.
Featured Tools:
firecrawl_scrapefirecrawl_searchfirecrawl_crawlfirecrawl_mapfirecrawl_extractInstallation
Get up and running with MCP tools in under 5 minutes
1Start Adverant Nexus
Make sure Adverant Nexus is running with Docker Compose:
# Clone and start the platform
git clone https://github.com/adverant/nexus-platform.git
cd nexus-platform
docker-compose up -d
# Verify MCP server is running
curl http://localhost:9120/health2Configure Claude Desktop
Add the MCP server to your Claude Desktop configuration:
# Edit Claude Desktop config
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"adverant-nexus": {
"url": "http://localhost:9120",
"name": "Adverant Nexus",
"description": "95+ tools for AI integration"
}
}
}Restart Claude Desktop after updating the configuration.
3Test the Connection
Verify that Claude can access the MCP tools:
Try asking Claude:
"What MCP tools are available from Adverant Nexus?"
Configuration Examples
Basic Configuration
{
"mcpServers": {
"adverant-nexus": {
"url": "http://localhost:9120",
"name": "Adverant Nexus",
"timeout": 30000,
"retries": 3
}
}
}Production Configuration
{
"mcpServers": {
"adverant-nexus": {
"url": "https://nexus.adverant.ai",
"name": "Adverant Nexus",
"apiKey": "your_api_key_here",
"timeout": 60000,
"retries": 3,
"ssl": {
"verify": true,
"cert": "/path/to/cert.pem"
}
}
}
}Multiple Servers
{
"mcpServers": {
"adverant-nexus": {
"url": "http://localhost:9120",
"name": "Adverant Nexus"
},
"github": {
"url": "http://localhost:9121",
"name": "GitHub MCP Server"
},
"filesystem": {
"url": "http://localhost:9122",
"name": "Filesystem MCP Server"
}
}
}Usage Examples
GitHub Operations
Ask Claude to perform GitHub operations:
Example 1:
"Create a new issue in the adverant/nexus repo titled 'Add rate limiting' with the description 'We need to implement rate limiting on the API endpoints'"
Example 2:
"Search for all open pull requests in the adverant/nexus repo that mention 'authentication'"
Example 3:
"Create a new branch called 'feature/api-v2' from main in the adverant/nexus repo"
Filesystem Operations
Work with files and directories:
Example 1:
"Read the contents of /src/app/page.tsx and show me the component structure"
Example 2:
"Search for all TypeScript files in the /src directory that import React"
Example 3:
"Create a new directory at /src/components/cards if it doesn't exist"
Memory and Knowledge Graph
Store and recall information:
Example 1:
"Store this information: The API uses port 9100 for GraphRAG and port 9101 for MageAgent"
Example 2:
"What ports does the Adverant Nexus platform use?"
Example 3:
"Search my memories for information about Docker configuration"
Server Integration
Integrate Adverant Nexus MCP tools into your own applications
Node.js Integration
import { MCPClient } from '@anthropic-ai/mcp-client';
// Connect to Adverant Nexus MCP server
const client = new MCPClient({
url: 'http://localhost:9120',
apiKey: process.env.NEXUS_API_KEY,
});
await client.connect();
// List available tools
const tools = await client.listTools();
console.log('Available tools:', tools.length);
// Execute a tool
const result = await client.executeTool({
name: 'brain_store_memory',
parameters: {
content: 'Important information about the project',
tags: ['project', 'documentation'],
},
});
console.log('Memory stored:', result);
// Disconnect
await client.disconnect();Tool Discovery
Discover all available tools programmatically
List All Tools
# HTTP request
GET http://localhost:9120/tools
# Response
{
"tools": [
{
"name": "brain_store_memory",
"description": "Store a memory with VoyageAI embeddings",
"parameters": {
"content": {
"type": "string",
"required": true,
"description": "Memory content to store"
},
"tags": {
"type": "array",
"required": false,
"description": "Tags for categorization"
}
}
},
// ... 94 more tools
]
}Get Tool Details
# HTTP request
GET http://localhost:9120/tools/brain_store_memory
# Response
{
"name": "brain_store_memory",
"description": "Store a memory with VoyageAI embeddings for long-term recall",
"category": "Memory",
"parameters": {
"content": {
"type": "string",
"required": true,
"description": "Memory content to store"
},
"tags": {
"type": "array",
"items": { "type": "string" },
"required": false,
"description": "Tags for categorization"
},
"metadata": {
"type": "object",
"required": false,
"description": "Additional metadata"
}
},
"examples": [
{
"description": "Store a customer preference",
"parameters": {
"content": "Customer prefers email communication",
"tags": ["customer", "preference"]
}
}
]
}