Improve AI-generated code quality by 20%

Web search + robust content retrieval for AI coding tools.

Kindly Web Search is a part of the Shelpuk AI Technology Consulting agentic suite – a set of tools that together improve the code quality produced by AI coding agents by 15–20%. Read more on Claude Code generation quality improvement.

Works with Claude Code, Codex, Antigravity, Cursor, Windsurf, and any agent that supports skills or MCP servers.

Component	Role
tdd	Enforces TDD, requirements discipline, and peer review for every coding task
Serena	Semantic code navigation + persistent project memory
Kindly Web Search ← you are here	Up-to-date API and package documentation via web search
Lad MCP Server	Project-aware AI design and code review

If you like what we're building, please ⭐ star this repo – it's a huge motivation for us to keep going!

How to use the suite

1. Install three MCP servers and one skill:

2. Use the skill when requesting a feature:

Prompt your favorite AI coding agent (Claude Code, Codex, Cursor, etc.) as usual. Then just add Follow $tdd in the end.

hljs language-shell

> Build [your feature description]. Follow $tdd.

Kindly Web Search MCP Server

Kindly Web Search

Why do we need another web search MCP server?

Picture this: You're debugging a cryptic error in Google Cloud Batch with GPU instances. Your AI coding assistant searches the web and finds the perfect StackOverflow thread. Great, right? Not quite. Here's what most web search MCP servers give your AI:

hljs language-json

{
  "title": "GCP Cloud Batch fails with the GPU instance template",
  "url": "https://stackoverflow.com/questions/76546453/...",
  "snippet": "I am trying to run a GCP Cloud Batch job with K80 GPU. The job runs for ~30 min. and then fails..."
}

The question is there, but where are the answers? Where are the solutions that other developers tried? The workarounds? The "this worked for me" comments?

They're not there. Your AI now has to make a second call to scrape the page. Sometimes it does, sometimes it doesn't. And even when it does, most scrapers return either incomplete content or the entire webpage with navigation panels, ads, and other noise that wastes tokens and confuses the AI.

The Real Problem

At Shelpuk AI Technology Consulting, we build custom AI products under a fixed-price model. Development efficiency isn't just nice to have - it's the foundation of our business. We've been using AI coding assistants since 2023 (GitHub Copilot, Cursor, Windsurf, Claude Code, Codex), and we noticed something frustrating:

When we developers face a complex bug, we don't just want to find a URL - we want to find the conversation. We want to see what others tried, what worked, what didn't, and why. We want the GitHub Issue with all the comments. We want the StackOverflow thread with upvoted answers and follow-up discussions. We want the arXiv paper content, not just its abstract.

Existing web search MCP servers are basically wrappers around search APIs. They're great at finding content, but terrible at delivering it in a way that's useful for AI coding assistants.

What Kindly Does Differently

We built Kindly Web Search because we needed our AI assistants to work the way we work. When searching for solutions, Kindly:

✅ Integrates directly with APIs for StackExchange, GitHub Issues, arXiv, and Wikipedia - presenting content in LLM-optimized formats with proper structure

✅ Returns the full conversation in a single call: questions, answers, comments, reactions, and metadata

✅ Parses any webpage in real-time using a headless browser for cutting-edge issues that were literally posted yesterday

✅ Passes all useful content to the LLM immediately - no need for a second scraping call

✅ Supports multiple search providers (Serper and Tavily) with intelligent fallback

Now, when Claude Code or Codex searches for that GPU batch error, it gets the question and the answers. The code snippets. The "this fixed it for me" comments. Everything it needs to help you solve the problem - in one call.

If you give Kindly a try or like the idea, please drop us a star on GitHub - it’s always huge motivation for us to keep improving it! ⭐️

One MCP Server to Rule Them All

Kindly eliminates the need for:

✅ Generic web search MCP servers

✅ StackOverflow MCP servers

✅ Web scraping MCP servers (Playwright, Puppeteer, etc.)

It also significantly reduces reliance on GitHub MCP servers by providing structured Issue content through intelligent extraction.

Kindly has been our daily companion in production work for months, saving us countless hours and improving the effectiveness of our AI coding assistants. We're excited to share it with the community!

Tools

web_search(query, num_results=3) → top results with title, link, snippet, and page_content (Markdown, best-effort).
get_content(url) → page_content (Markdown, best-effort).

Search uses Serper (primary, if configured) or Tavily, and page extraction uses a local Chromium-based browser via nodriver.

Requirements

A search provider (priority order): SERPER_API_KEY (recommended) → TAVILY_API_KEY → SEARXNG_BASE_URL (self-hosted SearXNG)
A Chromium-based browser installed on the same machine running the MCP client (Chrome/Chromium/Edge/Brave)
- Without a browser: specialized sources (StackExchange, GitHub Issues/Discussions, Wikipedia, arXiv) still work well, but universal HTML page_content extraction may fail for other sites.
Highly recommended: GITHUB_TOKEN (renders GitHub Issues in a much more LLM-friendly format: question + answers/comments + reactions/metadata; fewer rate limits)
Python 3.13+ is supported (Python 3.14 supported; optional “advanced PDF layout” extras are disabled on 3.14 because onnxruntime wheels may be unavailable).

GITHUB_TOKEN can be read-only and limited to public repositories to avoid security/privacy concerns.

Quickstart

1) Install `uvx`

macOS / Linux:

hljs language-bash

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

Windows (PowerShell):

hljs language-powershell

irm https://astral.sh/uv/install.ps1 | iex

Re-open your terminal and verify:

hljs language-bash

uvx --version

2) Install a Chromium-based browser (required for `page_content`)

You need Chrome / Chromium / Edge / Brave installed on the same machine running your MCP client.

Note: If you skip this, specialized sources (StackOverflow/StackExchange, GitHub Issues/Discussions, Wikipedia, arXiv) will still work well. Only universal page_content extraction for arbitrary sites requires the browser.

macOS:

Install Chrome, or:

hljs language-bash

brew install --cask chromium

Windows:

Install Chrome or Edge.
If browser auto-detection fails later, you’ll need the path:

hljs language-powershell

Get-Command chrome | Select-Object -ExpandProperty Source
# Common path:
# C:\Program Files\Google\Chrome\Application\chrome.exe
# If `Get-Command chrome` fails, try one of these:
# C:\Program Files (x86)\Google\Chrome\Application\chrome.exe
# C:\Program Files\Microsoft\Edge\Application\msedge.exe

Linux (Ubuntu/Debian):

hljs language-bash

sudo apt-get update
sudo apt-get install -y chromium
which chromium

Other Linux distros: install chromium (or chromium-browser) via your package manager.

3) Set your search API key (required)

Set one of these. Provider selection order is: Serper → Tavily → SearXNG.

macOS / Linux:

hljs language-bash

export SERPER_API_KEY="..."
# or:
export TAVILY_API_KEY="..."
# or (self-hosted SearXNG):
export SEARXNG_BASE_URL="https://searx.example.org"

Windows (PowerShell):

hljs language-powershell

$env:SERPER_API_KEY="..."
# or:
$env:TAVILY_API_KEY="..."
# or (self-hosted SearXNG):
$env:SEARXNG_BASE_URL="https://searx.example.org"

Optional (SearXNG): if your instance requires authentication or blocks bots, set:

hljs language-bash

export SEARXNG_HEADERS_JSON='{"Authorization":"Bearer ..."}'
export SEARXNG_USER_AGENT="Mozilla/5.0 ..."

Windows (PowerShell):

hljs language-powershell

$env:SEARXNG_HEADERS_JSON='{"Authorization":"Bearer ..."}'
$env:SEARXNG_USER_AGENT="Mozilla/5.0 ..."

Optional (recommended for better GitHub Issue / PR extraction):

hljs language-bash

export GITHUB_TOKEN="..."

For public repos, a read-only token is enough (classic tokens often use public_repo; fine-grained tokens need repo read access).

4) Run command used by all MCP clients

hljs language-bash

uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server \
  kindly-web-search-mcp-server start-mcp-server

First-run note: the first uvx invocation may take 30–60 seconds while it builds the tool environment. If your MCP client times out on first start, run the command once in a terminal to “prewarm” it, then retry in your client.

Now configure your MCP client to run that command. Make sure your API keys are set in the same shell/OS environment that launches the MCP client (unless you paste them directly into the client config).

Client setup

Codex

Set one of SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL.

CLI (no file editing) — add a local stdio MCP server:

macOS / Linux (Serper):

hljs language-bash

codex mcp add kindly-web-search \
  --env SERPER_API_KEY="$SERPER_API_KEY" \
  --env GITHUB_TOKEN="$GITHUB_TOKEN" \
  --env KINDLY_BROWSER_EXECUTABLE_PATH="$KINDLY_BROWSER_EXECUTABLE_PATH" \
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server \
  kindly-web-search-mcp-server start-mcp-server

macOS / Linux (Tavily):

hljs language-bash

codex mcp add kindly-web-search \
  --env TAVILY_API_KEY="$TAVILY_API_KEY" \
  --env GITHUB_TOKEN="$GITHUB_TOKEN" \
  --env KINDLY_BROWSER_EXECUTABLE_PATH="$KINDLY_BROWSER_EXECUTABLE_PATH" \
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server \
  kindly-web-search-mcp-server start-mcp-server

If you use SearXNG, replace the provider env var above with:

hljs language-bash

--env SEARXNG_BASE_URL="$SEARXNG_BASE_URL"

Windows (PowerShell):

hljs language-powershell

codex mcp add kindly-web-search `
  --env SERPER_API_KEY="$env:SERPER_API_KEY" `
  --env GITHUB_TOKEN="$env:GITHUB_TOKEN" `
  --env KINDLY_BROWSER_EXECUTABLE_PATH="$env:KINDLY_BROWSER_EXECUTABLE_PATH" `
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server `
  kindly-web-search-mcp-server start-mcp-server

Windows (PowerShell, Tavily):

hljs language-powershell

codex mcp add kindly-web-search `
  --env TAVILY_API_KEY="$env:TAVILY_API_KEY" `
  --env GITHUB_TOKEN="$env:GITHUB_TOKEN" `
  --env KINDLY_BROWSER_EXECUTABLE_PATH="$env:KINDLY_BROWSER_EXECUTABLE_PATH" `
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server `
  kindly-web-search-mcp-server start-mcp-server

Alternative (file-based): Edit ~/.codex/config.toml:

hljs language-toml

[mcp_servers.kindly-web-search]
command = "uvx"
args = [
  "--from",
  "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
  "kindly-web-search-mcp-server",
  "start-mcp-server",
]
# Forward variables from your shell/OS environment:
env_vars = ["SERPER_API_KEY", "TAVILY_API_KEY", "SEARXNG_BASE_URL", "GITHUB_TOKEN", "KINDLY_BROWSER_EXECUTABLE_PATH"]
startup_timeout_sec = 120.0

Claude Code

Set one of SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL.

CLI (no file editing) — add a local stdio MCP server:

macOS / Linux (Serper):

hljs language-bash

claude mcp add --transport stdio kindly-web-search \
  -e SERPER_API_KEY="$SERPER_API_KEY" \
  -e GITHUB_TOKEN="$GITHUB_TOKEN" \
  -e KINDLY_BROWSER_EXECUTABLE_PATH="$KINDLY_BROWSER_EXECUTABLE_PATH" \
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server \
  kindly-web-search-mcp-server start-mcp-server

macOS / Linux (Tavily):

hljs language-bash

claude mcp add --transport stdio kindly-web-search \
  -e TAVILY_API_KEY="$TAVILY_API_KEY" \
  -e GITHUB_TOKEN="$GITHUB_TOKEN" \
  -e KINDLY_BROWSER_EXECUTABLE_PATH="$KINDLY_BROWSER_EXECUTABLE_PATH" \
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server \
  kindly-web-search-mcp-server start-mcp-server

If you use SearXNG, replace the provider env var above with:

hljs language-bash

-e SEARXNG_BASE_URL="$SEARXNG_BASE_URL"

Windows (PowerShell):

hljs language-powershell

claude mcp add --transport stdio kindly-web-search `
  -e SERPER_API_KEY="$env:SERPER_API_KEY" `
  -e GITHUB_TOKEN="$env:GITHUB_TOKEN" `
  -e KINDLY_BROWSER_EXECUTABLE_PATH="$env:KINDLY_BROWSER_EXECUTABLE_PATH" `
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server `
  kindly-web-search-mcp-server start-mcp-server

Windows (PowerShell, Tavily):

hljs language-powershell

claude mcp add --transport stdio kindly-web-search `
  -e TAVILY_API_KEY="$env:TAVILY_API_KEY" `
  -e GITHUB_TOKEN="$env:GITHUB_TOKEN" `
  -e KINDLY_BROWSER_EXECUTABLE_PATH="$env:KINDLY_BROWSER_EXECUTABLE_PATH" `
  -- uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server `
  kindly-web-search-mcp-server start-mcp-server

Note: On current Claude Code versions, keep the server name immediately after --transport stdio and before -e/--env flags. Tested with Claude Code 2.0.76.

If Claude Code times out while starting the server, set a 120s startup timeout (milliseconds):

macOS / Linux:

hljs language-bash

export MCP_TIMEOUT=120000

Windows (PowerShell):

hljs language-powershell

$env:MCP_TIMEOUT="120000"

Alternative (file-based): Create/edit .mcp.json (project scope; recommended for teams):

hljs language-json

{
  "mcpServers": {
    "kindly-web-search": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
        "kindly-web-search-mcp-server",
        "start-mcp-server"
      ],
      "env": {
        "SERPER_API_KEY": "${SERPER_API_KEY}",
        "TAVILY_API_KEY": "${TAVILY_API_KEY}",
        "SEARXNG_BASE_URL": "${SEARXNG_BASE_URL}",
        "GITHUB_TOKEN": "${GITHUB_TOKEN}",
        "KINDLY_BROWSER_EXECUTABLE_PATH": "${KINDLY_BROWSER_EXECUTABLE_PATH}"
      }
    }
  }
}

Gemini CLI

Set one of SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL. Edit ~/.gemini/settings.json (or .gemini/settings.json in a project):

hljs language-json

{
  "mcpServers": {
    "kindly-web-search": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
        "kindly-web-search-mcp-server",
        "start-mcp-server"
      ],
      "env": {
        "SERPER_API_KEY": "$SERPER_API_KEY",
        "TAVILY_API_KEY": "$TAVILY_API_KEY",
        "SEARXNG_BASE_URL": "$SEARXNG_BASE_URL",
        "GITHUB_TOKEN": "$GITHUB_TOKEN",
        "KINDLY_BROWSER_EXECUTABLE_PATH": "$KINDLY_BROWSER_EXECUTABLE_PATH"
      },
      "timeout": 120000
    }
  }
}

OpenClaw

Set one of SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL. If mcporter is not installed yet: npm i -g mcporter. mcporter docs: https://github.com/steipete/mcporter/blob/main/docs/config.md

CLI (no file editing) — mcporter (recommended):

hljs language-bash

# Replace `$...` vars with real values, or export them in your shell first.
mcporter config add kindly-search \
  --scope home \
  --command "uvx --from git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server kindly-web-search-mcp-server start-mcp-server" \
  --env SERPER_API_KEY="$SERPER_API_KEY" \
  --env TAVILY_API_KEY="$TAVILY_API_KEY" \
  --env SEARXNG_BASE_URL="$SEARXNG_BASE_URL" \
  --env GITHUB_TOKEN="$GITHUB_TOKEN" \
  --env KINDLY_BROWSER_EXECUTABLE_PATH="$KINDLY_BROWSER_EXECUTABLE_PATH"

This writes to ~/.mcporter/mcporter.json (--scope home). You can replace kindly-search with any server name you prefer. Verify:

hljs language-bash

mcporter config get kindly-search

Alternative (file-based): Edit mcporter config (~/.mcporter/mcporter.json, or config/mcporter.json if you use project scope) and add this under mcpServers:

hljs language-json

{
  "mcpServers": {
    "kindly-search": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
        "kindly-web-search-mcp-server",
        "start-mcp-server"
      ],
      "env": {
        "SERPER_API_KEY": "PASTE_SERPER_KEY_OR_LEAVE_EMPTY",
        "TAVILY_API_KEY": "PASTE_TAVILY_KEY_OR_LEAVE_EMPTY",
        "SEARXNG_BASE_URL": "PASTE_SEARXNG_URL_OR_LEAVE_EMPTY",
        "GITHUB_TOKEN": "PASTE_GITHUB_TOKEN_OR_LEAVE_EMPTY",
        "KINDLY_BROWSER_EXECUTABLE_PATH": "PASTE_IF_NEEDED"
      }
    }
  }
}

Do not add root-level mcpServers to ~/.openclaw/openclaw.json (OpenClaw config uses strict schema validation and unknown keys are rejected).

If OpenClaw is already running and doesn’t pick up the new server, restart/reload the gateway:

hljs language-bash

openclaw gateway restart

Antigravity (Google IDE)

Set one of SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL.

In Antigravity, open the MCP store, then:

Click Manage MCP Servers
Click View raw config (this opens mcp_config.json)
Add the server config under mcpServers, save, then go back and click Refresh

Paste this into your mcpServers object (don’t overwrite other servers):

hljs language-json

{
  "kindly-web-search": {
    "command": "uvx",
    "args": [
      "--from",
      "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
      "kindly-web-search-mcp-server",
      "start-mcp-server"
    ],
    "env": {
      "SERPER_API_KEY": "PASTE_SERPER_KEY_OR_LEAVE_EMPTY",
      "TAVILY_API_KEY": "PASTE_TAVILY_KEY_OR_LEAVE_EMPTY",
      "SEARXNG_BASE_URL": "PASTE_SEARXNG_URL_OR_LEAVE_EMPTY",
      "GITHUB_TOKEN": "PASTE_GITHUB_TOKEN_OR_LEAVE_EMPTY",
      "KINDLY_BROWSER_EXECUTABLE_PATH": "PASTE_IF_NEEDED"
    }
  }
}

If Antigravity can’t find uvx, replace "uvx" with the absolute path (which uvx on macOS/Linux, where uvx on Windows). Make sure at least one of SERPER_API_KEY / TAVILY_API_KEY / SEARXNG_BASE_URL is non-empty. If the first start is slow, run the uvx command from Quickstart once in a terminal to prebuild the environment, then click Refresh. Don’t commit/share mcp_config.json if it contains API keys.

Cursor

Set one of SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL. Startup timeout: Cursor does not currently expose a per-server startup timeout setting. If the first run is slow, run the uvx command from Quickstart once in a terminal to prebuild the tool environment, then restart Cursor. Create .cursor/mcp.json:

hljs language-json

{
  "mcpServers": {
    "kindly-web-search": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
        "kindly-web-search-mcp-server",
        "start-mcp-server"
      ],
      "env": {
        "SERPER_API_KEY": "${env:SERPER_API_KEY}",
        "TAVILY_API_KEY": "${env:TAVILY_API_KEY}",
        "SEARXNG_BASE_URL": "${env:SEARXNG_BASE_URL}",
        "GITHUB_TOKEN": "${env:GITHUB_TOKEN}",
        "KINDLY_BROWSER_EXECUTABLE_PATH": "${env:KINDLY_BROWSER_EXECUTABLE_PATH}"
      }
    }
  }
}

Claude Desktop

Edit claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\\Claude\\claude_desktop_config.json

Note: values in this file are literal strings. Don’t commit this file or share it. Startup timeout: Claude Desktop does not expose a per-server startup timeout setting. If the first run is slow, run the uvx command from Quickstart once in a terminal to prebuild the tool environment, then restart Claude Desktop.

hljs language-json

{
  "mcpServers": {
    "kindly-web-search": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
        "kindly-web-search-mcp-server",
        "start-mcp-server"
      ],
      "env": {
        "SERPER_API_KEY": "PASTE_SERPER_KEY_OR_LEAVE_EMPTY",
        "TAVILY_API_KEY": "PASTE_TAVILY_KEY_OR_LEAVE_EMPTY",
        "SEARXNG_BASE_URL": "PASTE_SEARXNG_URL_OR_LEAVE_EMPTY",
        "GITHUB_TOKEN": "PASTE_GITHUB_TOKEN_OR_LEAVE_EMPTY",
        "KINDLY_BROWSER_EXECUTABLE_PATH": "PASTE_IF_NEEDED"
      }
    }
  }
}

GitHub Copilot / Microsoft Copilot (VS Code)

Most secure option: uses interactive prompts, so secrets don’t need to be stored in the file. Startup timeout: VS Code currently does not expose a per-server startup timeout setting for MCP servers. If the first run is slow, run the uvx command from Quickstart once in a terminal to prebuild the tool environment, then restart VS Code. Create .vscode/mcp.json:

hljs language-json

{
  "servers": {
    "kindly-web-search": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server",
        "kindly-web-search-mcp-server",
        "start-mcp-server"
      ],
      "env": {
        "SERPER_API_KEY": "${input:serper-api-key}",
        "TAVILY_API_KEY": "${input:tavily-api-key}",
        "SEARXNG_BASE_URL": "${input:searxng-base-url}",
        "GITHUB_TOKEN": "${input:github-token}",
        "KINDLY_BROWSER_EXECUTABLE_PATH": "${input:browser-path}"
      }
    }
  },
  "inputs": [
    { "id": "serper-api-key", "type": "promptString", "description": "Serper API key (optional if using Tavily or SearXNG)" },
    { "id": "tavily-api-key", "type": "promptString", "description": "Tavily API key (optional if using Serper or SearXNG)" },
    { "id": "searxng-base-url", "type": "promptString", "description": "SearXNG base URL (optional if using Serper or Tavily)" },
    { "id": "github-token", "type": "promptString", "description": "GitHub token (recommended)" },
    { "id": "browser-path", "type": "promptString", "description": "Browser binary path (only if needed)" }
  ]
}

Browser path (only if auto-detection fails)

Set KINDLY_BROWSER_EXECUTABLE_PATH to your browser binary.

macOS (Homebrew Chromium):

hljs language-bash

export KINDLY_BROWSER_EXECUTABLE_PATH="/Applications/Chromium.app/Contents/MacOS/Chromium"

Linux:

hljs language-bash

export KINDLY_BROWSER_EXECUTABLE_PATH="$(command -v chromium || command -v chromium-browser)"

Windows (PowerShell):

hljs language-powershell

$env:KINDLY_BROWSER_EXECUTABLE_PATH="C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe"

Chromium proxy (optional)

Set KINDLY_CHROME_PROXY to route all headless Chromium traffic (used for page_content extraction) through a proxy server. The value is passed directly as Chromium's --proxy-server flag.

Supported schemes: http://, https://, socks5://, socks4://.

hljs language-bash

export KINDLY_CHROME_PROXY="socks5://127.0.0.1:1080"

When running in Docker, use host.docker.internal instead of 127.0.0.1 to reach the host:

hljs language-bash

docker run ... -e KINDLY_CHROME_PROXY="socks5://host.docker.internal:1080" ...

This only affects Chromium-based page_content extraction. Search API calls (Serper, Tavily, SearXNG) use httpx and respect standard HTTP_PROXY / HTTPS_PROXY / ALL_PROXY environment variables instead.

Note: Chromium's --proxy-server does not support embedded credentials (e.g. socks5://user:pass@host:port will not work). If your proxy requires authentication, set up a local credential-less proxy forwarder (e.g. SSH tunnel, gost, socat) and point KINDLY_CHROME_PROXY to the local endpoint.

Proxy bypass list

Set KINDLY_CHROME_PROXY_BYPASS to exclude specific hosts from the proxy. The value is passed directly as Chromium's --proxy-bypass-list flag (comma-separated). Syntax:

Exact host: localhost, 127.0.0.1
Wildcard suffix: *.example.com, .local
IPv6: [::1]

hljs language-bash

export KINDLY_CHROME_PROXY_BYPASS="localhost,127.0.0.1,*.internal"

When unset, Chromium uses its default bypass list (which includes localhost and 127.0.0.1).

Remote / Docker deployment (separate machine)

Whether you can run the MCP server on a different PC depends on your MCP client:

Stdio / command-based clients (config uses command + args to spawn the server): the server must run on the same machine (or at least somewhere the client can run the command). You can still use Docker, but locally (the client launches docker run ...).
HTTP-capable clients (can connect to a server URL): you can run Kindly remotely in Docker using Streamable HTTP.

Docker (Streamable HTTP)

Build the image:

hljs language-bash

docker build -t kindly-web-search-mcp-server .

Run the server (port 8000):

hljs language-bash

docker run --rm -p 8000:8000 \
  -e SERPER_API_KEY="..." \
  -e GITHUB_TOKEN="..." \
  -e KINDLY_CHROME_PROXY="socks5://host.docker.internal:1080" \
  kindly-web-search-mcp-server \
  --http --host 0.0.0.0 --port 8000

Or (Tavily):

hljs language-bash

docker run --rm -p 8000:8000 \
  -e TAVILY_API_KEY="..." \
  -e GITHUB_TOKEN="..." \
  -e KINDLY_CHROME_PROXY="socks5://host.docker.internal:1080" \
  kindly-web-search-mcp-server \
  --http --host 0.0.0.0 --port 8000

MCP endpoint: http://<server-host>:8000/mcp
Make sure at least one of SERPER_API_KEY / TAVILY_API_KEY / SEARXNG_BASE_URL is set.
page_content extraction runs on the server machine/container (this Docker image includes Chromium).
Remote HTTP is typically unauthenticated and unencrypted by default; don’t expose this port publicly. Use VPN/firewall rules or a reverse proxy with TLS + auth.
Don’t bake API keys into the image; pass them via env vars at runtime.

Troubleshooting

“No Chromium-based browser executable found”: install Chrome/Chromium/Edge and set KINDLY_BROWSER_EXECUTABLE_PATH if needed.
“Failed to connect to browser”: increase retries/timeouts:
- KINDLY_NODRIVER_RETRY_ATTEMPTS=5
- KINDLY_NODRIVER_DEVTOOLS_READY_TIMEOUT_SECONDS=20
- Ensure proxy/VPN env vars don’t hijack localhost (set NO_PROXY=localhost,127.0.0.1 if you use HTTP_PROXY/HTTPS_PROXY)
- KINDLY_HTML_TOTAL_TIMEOUT_SECONDS=45
page_content shows _Failed to retrieve page content: TimeoutError_ (can happen on any OS, especially Windows): the MCP tool time budget was exceeded (often due to slower headless browser cold starts).
- How to spot it: one or more results include _Failed to retrieve page content: TimeoutError_ in page_content (or get_content(url) returns that message).
- Fix: increase KINDLY_TOOL_TOTAL_TIMEOUT_SECONDS (and, if needed, raise the cap KINDLY_TOOL_TOTAL_TIMEOUT_MAX_SECONDS).
- Env vars:
  - KINDLY_TOOL_TOTAL_TIMEOUT_SECONDS: total time budget per web_search / get_content call (search + extraction). Default: 120.
  - KINDLY_TOOL_TOTAL_TIMEOUT_MAX_SECONDS: caps the above value (safety). Default: 600.
  - KINDLY_WEB_SEARCH_MAX_CONCURRENCY: max parallel content fetches. Default: 3 (when unset or invalid).
- Recommended starting point (PowerShell):
  - $env:KINDLY_TOOL_TOTAL_TIMEOUT_SECONDS="180"
  - $env:KINDLY_TOOL_TOTAL_TIMEOUT_MAX_SECONDS="600"
  - Optional (reduces parallel browser work): $env:KINDLY_WEB_SEARCH_MAX_CONCURRENCY="1"
Browser reuse is on by default for universal HTML loading. Note: pooled Chromium shares state across requests (cookies, local storage, cache, and user-agent from the first request handled by each slot).
- KINDLY_NODRIVER_REUSE_BROWSER=0 disables reuse (fresh Chromium per request).
- KINDLY_NODRIVER_BROWSER_POOL_SIZE=2 controls how many Chromium instances are kept warm.
- KINDLY_NODRIVER_ACQUIRE_TIMEOUT_SECONDS=30 controls how long to wait for a pooled slot before falling back to per-request Chromium.
- Optional: KINDLY_NODRIVER_PORT_RANGE=45000-45100 restricts remote debugging ports.
- Pooled slots are health-checked before use and auto-restarted if the DevTools endpoint is stale (diagnostics emit pool.slot_probe and pool.slot_restart).
- If pool acquisition times out or fails, the server falls back to per-request Chromium and emits a pool.acquire_timeout/pool.slot_error diagnostic when diagnostics are enabled.
Need deeper debugging? Enable diagnostics:
- Set KINDLY_DIAGNOSTICS=1 to emit JSON-line diagnostics to stderr and include diagnostics in tool responses.
- get_content returns top-level diagnostics; web_search attaches diagnostics per result.
OSError: [Errno 39] Directory not empty: '/tmp/kindly-nodriver-.../Default': update to the latest server revision (uv may cache tool envs; uv cache clean can help).
“web_search fails: no provider key”: set SERPER_API_KEY, TAVILY_API_KEY, or SEARXNG_BASE_URL.

Security

Don’t commit API keys.
Prefer env-var expansion (Codex env_vars, Cursor ${env:...}, Gemini $VAR, Claude Code ${VAR}) instead of hardcoding secrets.

kindly-web-search-mcp-server

Improve AI-generated code quality by 20%

How to use the suite

Kindly Web Search MCP Server

Why do we need another web search MCP server?

The Real Problem

What Kindly Does Differently

One MCP Server to Rule Them All

Requirements

Quickstart

1) Install `uvx`

2) Install a Chromium-based browser (required for `page_content`)

3) Set your search API key (required)

4) Run command used by all MCP clients

Client setup

Codex

Claude Code

Gemini CLI

OpenClaw

Antigravity (Google IDE)

Cursor

Claude Desktop

GitHub Copilot / Microsoft Copilot (VS Code)

Browser path (only if auto-detection fails)

Chromium proxy (optional)

Proxy bypass list

Remote / Docker deployment (separate machine)

Docker (Streamable HTTP)

Troubleshooting

Security

Similar Packages

Tags

Original Author

Publisher

kindly-web-search-mcp-server

Improve AI-generated code quality by 20%

How to use the suite

Kindly Web Search MCP Server

Why do we need another web search MCP server?

The Real Problem

What Kindly Does Differently

One MCP Server to Rule Them All

Requirements

Quickstart

1) Install uvx

2) Install a Chromium-based browser (required for page_content)

3) Set your search API key (required)

4) Run command used by all MCP clients

Client setup

Codex

Claude Code

Gemini CLI

OpenClaw

Antigravity (Google IDE)

Cursor

Claude Desktop

GitHub Copilot / Microsoft Copilot (VS Code)

Browser path (only if auto-detection fails)

Chromium proxy (optional)

Proxy bypass list

Remote / Docker deployment (separate machine)

Docker (Streamable HTTP)

Troubleshooting

Security

Similar Packages

Tags

Original Author

Publisher

1) Install `uvx`

2) Install a Chromium-based browser (required for `page_content`)