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Omnisearch

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🔍 A Model Context Protocol (MCP) server providing unified access to multiple search engines (Tavily, Brave, Kagi, Exa), AI tools (Kagi FastGPT, Exa, Linkup), and content extraction services (Firecrawl, Tavily, Kagi). Includes GitHub search. All through a single interface.

Publisherspences10
Repositorymcp-omnisearch
LanguageTypeScript
Forks
52
Stars
351
Available tools
0
Transport typestdio
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LicenseMIT
Links
  • Connect tools to AI workflows

    Omnisearch exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    351 stars and 52 forks from the linked repository.

mcp-omnisearch

built with vite+ tested with vitest

A Model Context Protocol (MCP) server that provides unified access to Tavily, Brave, Kagi, Exa AI, GitHub, Linkup, and Firecrawl through four consolidated tools.

Quick start

Install the published server with MCPick:

bash
npx mcpick add \
  --name mcp-omnisearch \
  --command npx \
  --args=-y,mcp-omnisearch

MCPick defaults to Claude Code. Use --client and --scope to target another client or add the server to the current repository:

bash
npx mcpick add \
  --name mcp-omnisearch \
  --command npx \
  --args=-y,mcp-omnisearch \
  --client vscode \
  --scope project

See Deployment for supported clients and provider credential storage options.

To run from source instead:

bash
pnpm install
pnpm run build
node ./dist/index.js

Providers without keys are skipped and the rest keep working. If your client supports environment-variable expansion, reference keys instead of storing their values in the MCP configuration:

json
{
	"mcpServers": {
		"mcp-omnisearch": {
			"command": "npx",
			"args": ["-y", "mcp-omnisearch"],
			"env": {
				"TAVILY_API_KEY": "${TAVILY_API_KEY}",
				"EXA_API_KEY": "${EXA_API_KEY}"
			}
		}
	}
}

Add only the provider keys you use. Expansion syntax and secret storage are client-specific; see Deployment for secure options and plaintext fallback guidance.

Tools

web_search

Search the web with Tavily, Brave, Kagi, Exa, or Kagi Enrichment.

json
{
	"query": "latest SvelteKit releases",
	"provider": "tavily",
	"limit": 10,
	"search_depth": "advanced",
	"topic": "news",
	"time_range": "month",
	"safe_search": true,
	"include_raw_content": false,
	"auto_parameters": false
}

Search controls apply when supported by the selected provider.

ai_search

Get sourced AI answers with Kagi FastGPT, Exa Answer, Linkup, or Tavily Research. Tavily Research returns a task ID first; pass it back as research_id to retrieve the report.

json
{
	"query": "Explain the differences between REST and GraphQL",
	"provider": "kagi_fastgpt"
}

github_search

Search GitHub code, repositories, or users.

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

web_extract

Extract, crawl, scrape, summarize, or find similar content with Tavily, Kagi, Firecrawl, or Exa.

json
{
	"url": "https://example.com/long-article",
	"provider": "tavily",
	"mode": "extract",
	"extract_depth": "advanced",
	"query": "installation requirements",
	"chunks_per_source": 3,
	"format": "markdown"
}

Documentation

Environment variables

  • TAVILY_API_KEY
  • KAGI_API_KEY
  • BRAVE_API_KEY
  • GITHUB_API_KEY
  • EXA_API_KEY
  • LINKUP_API_KEY
  • FIRECRAWL_API_KEY
  • FIRECRAWL_BASE_URL optional, for self-hosted Firecrawl
  • OMNISEARCH_LARGE_RESULT_MODE optional, file default or inline

Development

bash
pnpm install
pnpm run build
pnpm test

Please read CONTRIBUTING.md before opening a PR.

License

MIT License - see LICENSE.

Acknowledgments

Built on Model Context Protocol, Tavily, Kagi, Brave Search, Exa AI, Linkup, and Firecrawl.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "mcp-omnisearch": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-omnisearch"
      ],
      "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": "https://your-firecrawl-domain.com"
      }
    }
  }
}

Use Omnisearch MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Omnisearch is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Omnisearch as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Omnisearch MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "omnisearch": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-omnisearch"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Omnisearch MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Omnisearch MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Omnisearch to help me with this task?
Omnisearch
Sure. I read it.
Here is what I found using Omnisearch.

Frequently asked questions

What is the Omnisearch MCP server used for?

Omnisearch is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Omnisearch MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Omnisearch with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Omnisearch MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Omnisearch connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Omnisearch MCP to TypingMind?

Omnisearch runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Omnisearch MCP provide in TypingMind?

Omnisearch exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Omnisearch MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Omnisearch requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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