A Model Context Protocol (MCP) server that provides unified access to multiple search providers and AI tools. This server combines the capabilities of Tavily, Perplexity, Kagi, Jina AI, Brave, Exa AI, and Firecrawl to offer comprehensive search, AI responses, content processing, and enhancement features through a single interface.

• Tavily Search: Optimized for factual information with strong citation support. Supports domain filtering through API parameters (include_domains/exclude_domains).
• Brave Search: Privacy-focused search with good technical content coverage. Features native support for search operators (site:, -site:, filetype:, intitle:, inurl:, before:, after:, and exact phrases).
• Kagi Search: High-quality search results with minimal advertising influence, focused on authoritative sources. Supports search operators in query string (site:, -site:, filetype:, intitle:, inurl:, before:, after:, and exact phrases).
• Exa Search: AI-powered web search using neural and keyword search. Optimized for AI applications with semantic understanding, content extraction, and research capabilities.
• GitHub Search: Comprehensive code search across public GitHub repositories with three specialized tools:
  • Code Search: Find code examples, function definitions, and files using advanced syntax (filename:, path:, repo:, user:, language:, in:file)
  • Repository Search: Discover repositories with sorting by stars, forks, or recent updates
  • User Search: Find GitHub users and organizations

MCP Omnisearch provides powerful search capabilities through operators and parameters:

• Domain filtering: Available across all providers
  • Tavily: Through API parameters (include_domains/exclude_domains)
  • Brave & Kagi: Through site: and -site: operators
• File type filtering: Available in Brave and Kagi (filetype:)
• Title and URL filtering: Available in Brave and Kagi (intitle:, inurl:)
• Date filtering: Available in Brave and Kagi (before:, after:)
• Exact phrase matching: Available in Brave and Kagi ("phrase")

// Using Brave or Kagi with query string operators
{
  "query": "filetype:pdf site:microsoft.com typescript guide"
}

// Using Tavily with API parameters
{
  "query": "typescript guide",
  "include_domains": ["microsoft.com"],
  "exclude_domains": ["github.com"]
}

• Brave Search: Full native operator support in query string
• Kagi Search: Complete operator support in query string
• Tavily Search: Domain filtering through API parameters
• Exa Search: Domain filtering through API parameters, semantic search with neural understanding
• GitHub Search: Advanced code search syntax with qualifiers:
  • filename:remote.ts - Search for specific files
  • path:src/lib - Search within specific directories
  • repo:user/repo - Search within specific repositories
  • user:username - Search within a user's repositories
  • language:typescript - Filter by programming language
  • in:file "export function" - Search for text within files

• Perplexity AI: Advanced response generation combining real-time web search with GPT-4 Omni and Claude 3
• Kagi FastGPT: Quick AI-generated answers with citations (900ms typical response time)
• Exa Answer: Get direct AI-generated answers to questions using Exa Answer API

• Jina AI Reader: Clean content extraction with image captioning and PDF support
• Kagi Universal Summarizer: Content summarization for pages, videos, and podcasts
• Tavily Extract: Extract raw content from single or multiple web pages with configurable extraction depth ('basic' or 'advanced'). Returns both combined content and individual URL content, with metadata including word count and extraction statistics
• Firecrawl Scrape: Extract clean, LLM-ready data from single URLs with enhanced formatting options
• Firecrawl Crawl: Deep crawling of all accessible subpages on a website with configurable depth limits
• Firecrawl Map: Fast URL collection from websites for comprehensive site mapping
• Firecrawl Extract: Structured data extraction with AI using natural language prompts
• Firecrawl Actions: Support for page interactions (clicking, scrolling, etc.) before extraction for dynamic content
• Exa Contents: Extract full content from Exa search result IDs
• Exa Similar: Find web pages semantically similar to a given URL using Exa

• Kagi Enrichment API: Supplementary content from specialized indexes (Teclis, TinyGem)
• Jina AI Grounding: Real-time fact verification against web knowledge

MCP Omnisearch is designed to work with the API keys you have available. You don't need to have keys for all providers - the server will automatically detect which API keys are available and only enable those providers.

For example:

• If you only have a Tavily and Perplexity API key, only those providers will be available
• If you don't have a Kagi API key, Kagi-based services won't be available, but all other providers will work normally
• The server will log which providers are available based on the API keys you've configured

This flexibility makes it easy to get started with just one or two providers and add more as needed.

This server requires configuration through your MCP client. Here are examples for different environments:

Add this to your Cline MCP settings:

{
	"mcpServers": {
		"mcp-omnisearch": {
			"command": "node",
			"args": ["/path/to/mcp-omnisearch/dist/index.js"],
			"env": {
				"TAVILY_API_KEY": "your-tavily-key",
				"PERPLEXITY_API_KEY": "your-perplexity-key",
				"KAGI_API_KEY": "your-kagi-key",
				"JINA_AI_API_KEY": "your-jina-key",
				"BRAVE_API_KEY": "your-brave-key",
				"GITHUB_API_KEY": "your-github-key",
				"EXA_API_KEY": "your-exa-key",
				"FIRECRAWL_API_KEY": "your-firecrawl-key",
				"FIRECRAWL_BASE_URL": "http://localhost:3002"
			},
			"disabled": false,
			"autoApprove": []
		}
	}
}

For WSL environments, add this to your Claude Desktop configuration:

{
	"mcpServers": {
		"mcp-omnisearch": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"TAVILY_API_KEY=key1 PERPLEXITY_API_KEY=key2 KAGI_API_KEY=key3 JINA_AI_API_KEY=key4 BRAVE_API_KEY=key5 GITHUB_API_KEY=key6 EXA_API_KEY=key7 FIRECRAWL_API_KEY=key8 FIRECRAWL_BASE_URL=http://localhost:3002 node /path/to/mcp-omnisearch/dist/index.js"
			]
		}
	}
}

The server uses API keys for each provider. You don't need keys for all providers - only the providers corresponding to your available API keys will be activated:

• TAVILY_API_KEY: For Tavily Search
• PERPLEXITY_API_KEY: For Perplexity AI
• KAGI_API_KEY: For Kagi services (FastGPT, Summarizer, Enrichment)
• JINA_AI_API_KEY: For Jina AI services (Reader, Grounding)
• BRAVE_API_KEY: For Brave Search
• GITHUB_API_KEY: For GitHub search services (Code, Repository, User search)
• EXA_API_KEY: For Exa AI services (Search, Answer, Contents, Similar)
• FIRECRAWL_API_KEY: For Firecrawl services (Scrape, Crawl, Map, Extract, Actions)
• FIRECRAWL_BASE_URL: For self-hosted Firecrawl instances (optional, defaults to Firecrawl cloud service)

You can start with just one or two API keys and add more later as needed. The server will log which providers are available on startup.

To use GitHub search features, you'll need a GitHub personal access token with public repository access only for security:

1. Go to GitHub Settings: Navigate to GitHub Settings > Developer settings > Personal access tokens

2. Create a new token: Click "Generate new token" → "Generate new token (classic)"

3. Configure token settings:

   • Name: MCP Omnisearch - Public Search

   • Expiration: Choose your preferred expiration (90 days recommended)

   • Scopes: Leave all checkboxes UNCHECKED

     ⚠️ Important: Do not select any scopes. An empty scope token can only access public repositories and user profiles, which is exactly what we want for search functionality.

4. Generate and copy: Click "Generate token" and copy the token immediately

5. Add to environment: Set GITHUB_API_KEY=your_token_here

Security Notes:

• This token configuration ensures no access to private repositories
• Only public code search, repository discovery, and user profiles are accessible
• Rate limits: 5,000 requests/hour for code search, 10 requests/minute for code search specifically
• You can revoke the token anytime from GitHub settings if needed

If you're running a self-hosted instance of Firecrawl, you can configure MCP Omnisearch to use it by setting the FIRECRAWL_BASE_URL environment variable. This allows you to maintain complete control over your data processing pipeline.

Self-hosted Firecrawl setup:

1. Follow the Firecrawl self-hosting guide
2. Set up your Firecrawl instance (default runs on http://localhost:3002)
3. Configure MCP Omnisearch with your self-hosted URL:

FIRECRAWL_BASE_URL=http://localhost:3002
# or for a remote self-hosted instance:
FIRECRAWL_BASE_URL=https://your-firecrawl-domain.com

Important notes:

• If FIRECRAWL_BASE_URL is not set, MCP Omnisearch will default to the Firecrawl cloud service
• Self-hosted instances support the same API endpoints (/v1/scrape, /v1/crawl, etc.)
• You'll still need a FIRECRAWL_API_KEY even for self-hosted instances
• Self-hosted Firecrawl provides enhanced security and customization options

The server implements MCP Tools organized by category:

Search the web using Tavily Search API. Best for factual queries requiring reliable sources and citations.

Parameters:

• query (string, required): Search query

Example:

{
	"query": "latest developments in quantum computing"
}

Privacy-focused web search with good coverage of technical topics.

Parameters:

• query (string, required): Search query

Example:

{
	"query": "rust programming language features"
}

High-quality search results with minimal advertising influence. Best for finding authoritative sources and research materials.

Parameters:

• query (string, required): Search query
• language (string, optional): Language filter (e.g., "en")
• no_cache (boolean, optional): Bypass cache for fresh results

Example:

{
	"query": "latest research in machine learning",
	"language": "en"
}

Search for code on GitHub using advanced syntax. This tool searches through file contents in public repositories and provides code snippets with metadata.

Parameters:

• query (string, required): Search query with GitHub search syntax
• limit (number, optional): Maximum number of results (1-50, default: 10)

Example:

{
	"query": "filename:remote.ts @sveltejs/kit",
	"limit": 5
}

Advanced query examples:

• "filename:config.json path:src" - Find config.json files in src directories
• "function fetchData language:typescript" - Find fetchData functions in TypeScript
• "repo:microsoft/vscode extension" - Search within specific repository
• "user:torvalds language:c" - Search user's repositories for C code

Discover GitHub repositories with enhanced metadata including stars, forks, language, and last update information.

Parameters:

• query (string, required): Repository search query
• limit (number, optional): Maximum number of results (1-50, default: 10)
• sort (string, optional): Sort results by 'stars', 'forks', or 'updated'

Example:

{
	"query": "sveltekit remote functions",
	"sort": "stars",
	"limit": 5
}

Find GitHub users and organizations with profile information.

Parameters:

• query (string, required): User/organization search query
• limit (number, optional): Maximum number of results (1-50, default: 10)

Example:

{
	"query": "Rich-Harris",
	"limit": 3
}

AI-powered web search using neural and keyword search. Automatically chooses between traditional keyword search and Exa's embeddings-based model to find the most relevant results for your query.

Parameters:

• query (string, required): Search query
• limit (number, optional): Maximum number of results (1-100, default: 10)
• include_domains (array, optional): Only include results from these domains
• exclude_domains (array, optional): Exclude results from these domains

Example:

{
	"query": "latest AI research papers",
	"limit": 15,
	"include_domains": ["arxiv.org", "scholar.google.com"]
}

AI-powered response generation with real-time web search integration.

Parameters:

• query (string, required): Question or topic for AI response

Example:

{
	"query": "Explain the differences between REST and GraphQL"
}

Quick AI-generated answers with citations.

Parameters:

• query (string, required): Question for quick AI response

Example:

{
	"query": "What are the main features of TypeScript?"
}

Get direct AI-generated answers to questions using Exa Answer API.

Parameters:

• query (string, required): Question for AI response
• include_domains (array, optional): Only include sources from these domains
• exclude_domains (array, optional): Exclude sources from these domains

Example:

{
	"query": "How does machine learning work?",
	"include_domains": ["arxiv.org", "nature.com"]
}

Convert URLs to clean, LLM-friendly text with image captioning.

Parameters:

• url (string, required): URL to process

Example:

{
	"url": "https://example.com/article"
}

Summarize content from URLs.

Parameters:

• url (string, required): URL to summarize

Example:

{
	"url": "https://example.com/long-article"
}

Extract raw content from web pages with Tavily Extract.

Parameters:

• url (string | string[], required): Single URL or array of URLs to extract content from
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced'

Example:

{
	"url": [
		"https://example.com/article1",
		"https://example.com/article2"
	],
	"extract_depth": "advanced"
}

Response includes:

• Combined content from all URLs
• Individual raw content for each URL
• Metadata with word count, successful extractions, and any failed URLs

Extract clean, LLM-ready data from single URLs with enhanced formatting options.

Parameters:

• url (string | string[], required): Single URL or array of URLs to extract content from
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced'

Example:

{
	"url": "https://example.com/article",
	"extract_depth": "basic"
}

Response includes:

• Clean, markdown-formatted content
• Metadata including title, word count, and extraction statistics

Deep crawling of all accessible subpages on a website with configurable depth limits.

Parameters:

• url (string | string[], required): Starting URL for crawling
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced' (controls crawl depth and limits)

Example:

{
	"url": "https://example.com",
	"extract_depth": "advanced"
}

Response includes:

• Combined content from all crawled pages
• Individual content for each page
• Metadata including title, word count, and crawl statistics

Fast URL collection from websites for comprehensive site mapping.

Parameters:

• url (string | string[], required): URL to map
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced' (controls map depth)

Example:

{
	"url": "https://example.com",
	"extract_depth": "basic"
}

Response includes:

• List of all discovered URLs
• Metadata including site title and URL count

Structured data extraction with AI using natural language prompts.

Parameters:

• url (string | string[], required): URL to extract structured data from
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced'

Example:

{
	"url": "https://example.com",
	"extract_depth": "basic"
}

Response includes:

• Structured data extracted from the page
• Metadata including title, extraction statistics

Support for page interactions (clicking, scrolling, etc.) before extraction for dynamic content.

Parameters:

• url (string | string[], required): URL to interact with and extract content from
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced' (controls complexity of interactions)

Example:

{
	"url": "https://news.ycombinator.com",
	"extract_depth": "basic"
}

Response includes:

• Content extracted after performing interactions
• Description of actions performed
• Screenshot of the page (if available)
• Metadata including title and extraction statistics

Extract full content from Exa search result IDs.

Parameters:

• ids (string | string[], required): Exa search result ID(s) to extract content from
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced'

Example:

{
	"ids": ["exa-result-id-123", "exa-result-id-456"],
	"extract_depth": "advanced"
}

Response includes:

• Combined content from all result IDs
• Individual raw content for each ID
• Metadata with word count and extraction statistics

Find web pages semantically similar to a given URL using Exa.

Parameters:

• url (string, required): URL to find similar pages for
• extract_depth (string, optional): Extraction depth - 'basic' (default) or 'advanced'

Example:

{
	"url": "https://arxiv.org/abs/2106.09685",
	"extract_depth": "advanced"
}

Response includes:

• Combined content from all similar pages
• Similarity scores and metadata
• Individual content for each similar page

Get supplementary content from specialized indexes.

Parameters:

• query (string, required): Query for enrichment

Example:

{
	"query": "emerging web technologies"
}

Verify statements against web knowledge.

Parameters:

• statement (string, required): Statement to verify

Example:

{
	"statement": "TypeScript adds static typing to JavaScript"
}

MCP Omnisearch supports containerized deployment using Docker with MCPO (Model Context Protocol Over HTTP) integration, enabling cloud deployment and OpenAPI access.

1. Using Docker Compose (Recommended):

# Clone the repository
git clone https://github.com/spences10/mcp-omnisearch.git
cd mcp-omnisearch

# Create .env file with your API keys
echo "TAVILY_API_KEY=your-tavily-key" > .env
echo "KAGI_API_KEY=your-kagi-key" >> .env
echo "PERPLEXITY_API_KEY=your-perplexity-key" >> .env
echo "EXA_API_KEY=your-exa-key" >> .env
# Add other API keys as needed
echo "GITHUB_API_KEY=your-github-key" >> .env

# Start the container
docker-compose up -d

2. Using Docker directly:

docker build -t mcp-omnisearch .
docker run -d \
  -p 8000:8000 \
  -e TAVILY_API_KEY=your-tavily-key \
  -e KAGI_API_KEY=your-kagi-key \
  -e PERPLEXITY_API_KEY=your-perplexity-key \
  -e EXA_API_KEY=your-exa-key \
  -e GITHUB_API_KEY=your-github-key \
  --name mcp-omnisearch \
  mcp-omnisearch

Configure the container using environment variables for each provider:

• TAVILY_API_KEY: For Tavily Search
• PERPLEXITY_API_KEY: For Perplexity AI
• KAGI_API_KEY: For Kagi services (FastGPT, Summarizer, Enrichment)
• JINA_AI_API_KEY: For Jina AI services (Reader, Grounding)
• BRAVE_API_KEY: For Brave Search
• GITHUB_API_KEY: For GitHub search services
• EXA_API_KEY: For Exa AI services
• FIRECRAWL_API_KEY: For Firecrawl services
• FIRECRAWL_BASE_URL: For self-hosted Firecrawl instances (optional)
• PORT: Container port (defaults to 8000)

Once deployed, the MCP server is accessible via OpenAPI at:

• Base URL: http://your-container-host:8000
• OpenAPI Endpoint: /omnisearch
• Compatible with: OpenWebUI and other tools expecting OpenAPI

The containerized version can be deployed to any container platform that supports Docker:

• Cloud Run (Google Cloud)
• Container Instances (Azure)
• ECS/Fargate (AWS)
• Railway, Render, Fly.io
• Any Kubernetes cluster

Example deployment to a cloud platform:

# Build and tag for your registry
docker build -t your-registry/mcp-omnisearch:latest .
docker push your-registry/mcp-omnisearch:latest

# Deploy with your platform's CLI or web interface
# Configure environment variables through your platform's settings

1. Clone the repository
2. Install dependencies:

pnpm install

3. Build the project:

pnpm run build

4. Run in development mode:

pnpm run dev

1. Update version in package.json
2. Build the project:

pnpm run build

3. Publish to npm:

pnpm publish

Each provider requires its own API key and may have different access requirements:

• Tavily: Requires an API key from their developer portal
• Perplexity: API access through their developer program
• Kagi: Some features limited to Business (Team) plan users
• Jina AI: API key required for all services
• Brave: API key from their developer portal
• GitHub: Personal access token with no scopes selected (public access only)
• Exa AI: API key from their dashboard at dashboard.exa.ai
• Firecrawl: API key required from their developer portal

Each provider has its own rate limits. The server will handle rate limit errors gracefully and return appropriate error messages.

Contributions are welcome! Please feel free to submit a Pull Request.

MIT License - see the LICENSE file for details.

Built on:

• Model Context Protocol
• Tavily Search
• Perplexity AI
• Kagi Search
• Jina AI
• Brave Search
• Exa AI
• Firecrawl