justinmccuistion/orca-ai-mcp
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The Orca AI MCP Server is a Model Context Protocol server that integrates with Orca AI's HUNT Platform API to provide information about people, companies, and other entities.
Orca AI MCP Server
A Model Context Protocol (MCP) server that integrates with Orca AI's HUNT Platform API for finding information about people, companies, and other entities.
About Orca AI
Orca AI is a leading provider of investigative intelligence and due diligence solutions. The HUNT platform provides access to comprehensive datasets for person and entity research across multiple authoritative sources.
This MCP server connects to the Orca AI API to enable AI assistants to search and retrieve information from the HUNT platform directly within their workflows.
Features
- Dynamic Configuration: Directory-based configuration using local
.orcaai.jsonfiles - HUNT API Integration: Search across authoritative datasets for people, companies, and entities
- Flexible Authentication: API key-based authentication with environment variable fallback
- Context-Aware: Automatically detects and switches between different configurations
- Robust Error Handling: Built-in retry logic with exponential backoff
Installation
Claude Desktop
Add to your Claude Desktop MCP configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%/Claude/claude_desktop_config.json on Windows):
{
"mcpServers": {
"orca-ai": {
"command": "node",
"args": ["dist/index.js"],
"env": {
"ORCA_API_TOKEN": "your-orca-ai-api-key"
}
}
}
}
Claude Code (CLI)
Add to your ~/.config/claude-code/mcp_servers.json:
{
"mcpServers": {
"orca-ai": {
"command": "node",
"args": ["dist/index.js"],
"env": {
"ORCA_API_TOKEN": "your-orca-ai-api-key"
}
}
}
}
Gemini CLI
Add to your ~/.gemini/settings.json or project .gemini/settings.json:
{
"mcpServers": {
"orca-ai-mcp": {
"command": "node",
"args": ["dist/index.js"],
"env": {
"ORCA_API_TOKEN": "your-orca-ai-api-key"
},
"timeout": 30000,
"trust": false,
"includeTools": ["detect_orca_context", "get_hunt_results"]
}
}
}
Cursor CLI
Add to your MCP configuration file:
{
"mcpServers": {
"orca-ai": {
"command": "node",
"args": ["dist/index.js"],
"env": {
"ORCA_API_TOKEN": "your-orca-ai-api-key"
}
}
}
}
Development Setup
cd orca-ai-mcp
npm install
npm run build
Configuration
Local Configuration (Recommended)
Create a .orcaai.json file in your working directory:
{
"apiUrl": "https://api.orcaai.io",
"apiToken": "your-orca-ai-api-key-here",
"settings": {
"timeout": 30000,
"retries": 3
},
"tools": {
"hunt": true
}
}
Environment Variables
Alternatively, use environment variables:
export ORCA_API_TOKEN="your-orca-ai-api-key"
export ORCA_API_URL="https://api.orcaai.io"
export ORCA_TIMEOUT="30000"
export ORCA_RETRIES="3"
export ORCA_TOOLS_HUNT="true"
Available Tools
HUNT Search
get_hunt_results: Search across datasets for people, companies, and entities- Required:
query- Search string (person name, company name, etc.) - Optional:
nextToken- Pagination token for subsequent pages (v0.2 API)
- Required:
Context Detection
detect_orca_context: Detect current configuration and verify API connectivity
Authentication
This MCP server requires an Orca AI API key:
- API Key Format: 40 alphanumeric characters
- Header Used:
x-api-key - Endpoint:
https://api.orcaai.io
API Endpoints Used
- v0.2 HUNT API:
POST /v0.2/hunt(default, supports pagination, max 100 results per page)
Usage Examples
Basic Person Search
// Search for a person
const results = await callTool("get_hunt_results", {
query: "Vladimir Putin"
});
Company Search
// Search for a company
const results = await callTool("get_hunt_results", {
query: "Microsoft Corporation"
});
Paginated Search
// Get first page
const page1 = await callTool("get_hunt_results", {
query: "John Smith"
});
// Get next page using returned token
const page2 = await callTool("get_hunt_results", {
query: "John Smith",
nextToken: "returned-token-from-page1"
});
Configuration Check
// Verify your configuration
const context = await callTool("detect_orca_context");
Response Format
HUNT search results include:
- query: Original search query
- nextToken: Pagination token (v0.2 only)
- huntDocuments: Array of matching records containing:
primaryName: Main identified namenames: Array of alternative namesdataset: Source dataset informationrawData: Unstructured text about the recordvalues: Array of relevant values/attributestabularData: Structured data fields
Development
Available Scripts
npm run dev- Start development server with hot reloadnpm run build- Build the projectnpm run start- Start the built servernpm run test- Run testsnpm run type-check- Check TypeScript typesnpm run clean- Clean build directory
Testing the Server
# Build and start the server
npm run build
npm start
# In another terminal, test with Claude Code
# The server runs on stdio and communicates via MCP protocol
How to Start and Use
Step 1: Configure Your API Key
# Copy the example configuration
cp .orcaai.json.example .orcaai.json
# Edit .orcaai.json and add your API key
Step 2: Build the Server
npm run build
Step 3: Configure Claude Code
Add the server to your Claude Code MCP configuration:
{
"mcpServers": {
"orca-ai": {
"command": "node",
"args": ["dist/index.js"]
}
}
}
Step 4: Restart Claude Code
Restart Claude Code to load the MCP server.
Step 5: Use the Tools
Ask Claude Code to:
- "Search for information about [person/company] using Orca AI"
- "Check my Orca AI configuration"
- "Search the HUNT platform for [query]"
Error Handling
The server handles common API errors:
- 401 Unauthorized: Invalid or missing API key
- 400 Bad Request: Invalid query parameters
- 429 Rate Limited: Too many requests
- 5xx Server Errors: Automatic retry with exponential backoff
Directory Structure
orca-ai-mcp/
├── src/
│ └── index.ts # Main MCP server implementation
├── dist/ # Built JavaScript files
├── tests/ # Test files
├── .orcaai.json.example # Configuration example
├── .orcaai.json # Your actual config (gitignored)
├── package.json # Dependencies and scripts
├── tsconfig.json # TypeScript configuration
└── README.md # This file
Contributing
- Fork the repository
- Create a feature branch
- Make your changes with proper comments
- Ensure the build passes:
npm run build - Test your changes
- Submit a pull request
License
MIT License
Support
For issues and questions:
- Check the Orca AI API documentation
- Create an issue on GitHub
- Review existing issues for similar problems
Troubleshooting
Common Issues
-
"No configuration found"
- Ensure
.orcaai.jsonexists with valid API key - Check environment variables are set correctly
- Ensure
-
"Authentication failed"
- Verify your API key is 40 characters and valid
- Ensure no extra spaces in configuration
-
"Rate limit exceeded"
- Wait before making another request
- Consider implementing delays between requests
-
Server not starting
- Run
npm run buildfirst - Check Node.js version (requires 18+)
- Verify all dependencies installed:
npm install
- Run