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The Official Model Context Protocol (MCP) server for Kagi search & other tools. Python-based implementation.

214 stars PythonAI & Machine Learning Updated Nov 2, 2025

Documentation

Kagi MCP Server

An MCP server backed by the Kagi API. It exposes search and extraction tools to MCP-compatible clients.

Tools

  • `kagi_search_fetch` - web, news, videos, podcasts, and image search with optional page extracts, filters, and Kagi lenses.
  • `kagi_extract` - fetch a page's full content as markdown.

> Note: The previous `kagi_fastgpt` and `kagi_summarizer` tools have been removed. Both are planned to return in a future release.

Hosted Server

We run a hosted MCP server at `https://mcp.kagi.com/mcp` — no install required. Point any HTTP-capable MCP client at it and authenticate with your Kagi API key.

OAuth2 isn't supported yet (it's on our roadmap), so for now grab your API key from the dashboard and pass it via `Bearer` HTTP authentication.

Example with Claude Code:

bash
claude mcp add kagi https://mcp.kagi.com/mcp --transport http --header "Authorization: Bearer $(read -sp 'API key: ' k; echo $k)" --scope user

Prefer to run it yourself? See Client Setup for the local `uvx` install, or Self-Hosting to host the HTTP server on your own infrastructure.

Requirements

  • A Kagi API key in `KAGI_API_KEY`.
  • `uv` for the recommended `uvx` install path.

Install `uv`:

bash
curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Client Setup

Codex CLI

bash
codex mcp add kagi --env KAGI_API_KEY= -- uvx kagimcp

Codex writes MCP configuration to `~/.codex/config.toml`.

Claude Desktop

Install uv first.

MacOS/Linux:

bash
curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

code
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Then in your Claude Desktop config (found through Settings -> Developer -> Edit Config):

json
{
  "mcpServers": {
    "kagi": {
      "command": "uvx",
      "args": ["kagimcp"],
      "env": {
        "KAGI_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

Claude Code

bash
claude mcp add kagi -e KAGI_API_KEY="YOUR_API_KEY_HERE" -- uvx kagimcp

Smithery

bash
npx -y @smithery/cli install kagimcp --client claude

Kiro

Add to your Kiro MCP config file (`~/.kiro/settings/mcp.json` for global, or `.kiro/settings/mcp.json` for project-scoped) using the same `mcpServers` JSON as Claude Desktop. See the Kiro MCP documentation for more details.

OpenCode

Edit the OpenCode configuration file in `~/.config/opencode/opencode.json` and add the following:

json
{
  "mcp": {
    "kagi": {
      "type": "local",
      "command": ["uvx", "kagimcp"],
      "enabled": true,
      "environment": {
        "KAGI_API_KEY": ""
      }
    }
  }
}

Usage Examples

  • Search: `Who was Time's 2024 person of the year?`
  • Extract: `extract the full content of https://en.wikipedia.org/wiki/Model_Context_Protocol`

Configuration

Environment variableDescription
------
`KAGI_API_KEY`Required Kagi API key.
`FASTMCP_LOG_LEVEL`Logging level, for example `ERROR`.
`KAGI_SEARCH_TIMEOUT`Search timeout in seconds. Defaults to `10`.
`KAGI_EXTRACT_TIMEOUT`Extract timeout in seconds. Defaults to `30`.
`KAGI_MAX_RETRIES`Max retry attempts after the first request. Defaults to `2`; set `0` to disable retries.
`KAGI_HIDDEN_PARAMS`Comma-separated search params to hide from the LLM-facing schema.

Hideable search params:

text
workflow, extract_count, limit, include_domains, exclude_domains, time_relative, after, before, file_type, lens_id

Example:

bash
KAGI_HIDDEN_PARAMS="extract_count,after,before,time_relative,include_domains,exclude_domains"

Local Development

bash
git clone https://github.com/kagisearch/kagimcp.git
cd kagimcp
uv sync

Run locally over stdio:

bash
KAGI_API_KEY= uv run kagimcp

Run with streamable HTTP transport:

bash
KAGI_API_KEY= uv run kagimcp --http --host 0.0.0.0 --port 8000

Self-Hosting

HTTP mode is multi-tenant: each request supplies its API key via the

`Authorization: Bearer ` header instead of a server-wide env var, so one

instance can serve multiple users. The repo ships a `Dockerfile` that installs a pinned `kagimcp` from PyPI and

runs it in HTTP mode. The container respects `$PORT` so it works on any

platform that injects one (Railway, Render, Cloud Run, Fly.io, etc.).

Build and run locally:

sh
docker build -t kagimcp-hosted .
docker run --rm -p 8000:8000 kagimcp-hosted

Smoke test:

sh
curl -sL http://127.0.0.1:8000/mcp -X POST \
  -H "authorization: Bearer $KAGI_API_KEY" \
  -H "content-type: application/json" \
  -H "accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

To bump the version in production, edit the pin in the `Dockerfile` and redeploy.

Debugging

Inspect the published package:

bash
npx @modelcontextprotocol/inspector uvx kagimcp

Inspect a local checkout:

bash
npx @modelcontextprotocol/inspector uv --directory /ABSOLUTE/PATH/TO/kagimcp run kagimcp

The inspector is usually available at `http://localhost:5173`.

Prerelease Instructions

If using a prerelease build, the same installation instructions apply, but use `uvx --prerelease allow --from kagimcp==1.0.0rc2 kagimcp` instead of `uvx kagimcp` (replace `1.0.0rc2` with whatever version you're wanting to install).

Frequently asked questions

What is kagimcp?

kagimcp is The Official Model Context Protocol (MCP) server for Kagi search & other tools. Python-based implementation.

How do I install kagimcp?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is kagimcp open source?

Yes — it is hosted on GitHub at https://github.com/kagisearch/kagimcp and has 214 stars.

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