kindly-web-search-mcp-server

name	kindly-web-search-mcp-server
description	Web search MCP server with intelligent content extraction for AI coding tools, supporting Serper, Tavily, and SearXNG with specialized parsers for StackOverflow, GitHub, Wikipedia, and arXiv.
triggers	["search the web for","find documentation about","look up recent discussions on","get content from this URL","search StackOverflow for","find GitHub issues about","retrieve article content from","search for API documentation"]

Kindly Web Search MCP Server

Skill by ara.so — MCP Skills collection.

Overview

Kindly Web Search is an MCP server that provides intelligent web search and content extraction for AI coding assistants. Unlike basic search wrappers, it returns full structured content in a single call—including StackOverflow answers, GitHub issue comments, Wikipedia articles, and arXiv papers—eliminating the need for separate scraping tools.

Key differentiator: When searching for solutions, Kindly fetches the entire conversation (questions + answers + comments) in one request, not just a title and URL snippet.

Eliminates need for:

• Generic web search MCP servers
• Separate StackOverflow/GitHub/Wikipedia MCP servers
• Web scraping MCP servers (Playwright/Puppeteer)

Installation

Prerequisites

1. Python 3.13+ (3.14 supported)
2. Chromium-based browser (Chrome/Chromium/Edge/Brave) installed locally
3. Search provider API key (one of):
   • SERPER_API_KEY (recommended)
   • TAVILY_API_KEY
   • SEARXNG_BASE_URL (self-hosted SearXNG)
4. Optional but recommended: GITHUB_TOKEN (read-only, public repos only)

Setup Steps

1. Install uvx

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

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

2. Install Chromium (if not already installed)

# macOS
brew install --cask chromium

# Ubuntu/Debian
sudo apt-get update && sudo apt-get install -y chromium

# Windows: Install Chrome or Edge from official sources

3. Set environment variables

# Choose ONE search provider
export SERPER_API_KEY="your-key-here"
# OR
export TAVILY_API_KEY="your-key-here"
# OR
export SEARXNG_BASE_URL="https://searx.example.org"

# Optional: Better GitHub extraction
export GITHUB_TOKEN="your-github-token"

# Optional: Custom browser path (auto-detected by default)
export KINDLY_BROWSER_EXECUTABLE_PATH="/path/to/chrome"

4. Configure MCP Client

Codex:

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

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

Claude Desktop / Other MCP Clients:

Add to your MCP settings JSON (typically ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "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": "your-key-here",
        "GITHUB_TOKEN": "your-github-token"
      }
    }
  }
}

Windows (PowerShell):

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

MCP Tools

web_search

Search the web and return results with full page content.

Parameters:

• query (string, required): Search query
• num_results (integer, optional): Number of results (default: 3, max: 10)

Returns:

• title: Page title
• link: URL
• snippet: Brief excerpt
• page_content: Full page content in Markdown (extracted intelligently based on source)

Example usage in prompt:

Search for "FastAPI async database connection pooling best practices"

What happens behind the scenes:

# The MCP server executes:
results = web_search(
    query="FastAPI async database connection pooling best practices",
    num_results=5
)

# Returns structured results with full content:
# - StackOverflow threads include all answers and comments
# - GitHub issues include full conversation threads
# - Documentation pages return clean Markdown
# - arXiv papers return full text content

get_content

Extract content from a specific URL.

Parameters:

• url (string, required): URL to extract content from

Returns:

• page_content: Full page content in Markdown

Example usage in prompt:

Get the full content from https://github.com/tiangolo/fastapi/issues/12345

What happens behind the scenes:

# The MCP server executes:
content = get_content(url="https://github.com/tiangolo/fastapi/issues/12345")

# Returns full GitHub issue with:
# - Issue description
# - All comments
# - Reactions and metadata
# - Code snippets in proper format

Specialized Content Extraction

Kindly automatically detects and optimally extracts content from:

StackOverflow / StackExchange

Returns complete Q&A threads with:

• Question body with code examples
• All answers (sorted by votes/acceptance)
• Comments on questions and answers
• Vote counts and acceptance status

GitHub Issues & Discussions

Returns full conversation threads:

• Issue/discussion body
• All comments chronologically
• Reactions and labels
• State (open/closed) and metadata

Requires GITHUB_TOKEN for best results (avoids rate limits, returns richer structure).

Wikipedia

Returns clean article content:

• Main article text
• Section structure preserved
• Links converted to references
• Tables formatted in Markdown

arXiv Papers

Returns full paper content:

• Abstract
• Full paper text
• Mathematical equations preserved
• References and citations

Generic Webpages

Uses headless browser (nodriver) to:

• Handle JavaScript-heavy sites
• Extract main content area
• Convert to clean Markdown
• Remove navigation/ads/clutter

Configuration

Search Provider Priority

Kindly checks providers in this order:

1. Serper (if SERPER_API_KEY set) — recommended, most reliable
2. Tavily (if TAVILY_API_KEY set)
3. SearXNG (if SEARXNG_BASE_URL set)

Environment Variables

# Search providers (set ONE)
SERPER_API_KEY=         # Google Serper API key
TAVILY_API_KEY=         # Tavily API key
SEARXNG_BASE_URL=       # Self-hosted SearXNG instance URL

# Content extraction
GITHUB_TOKEN=           # GitHub personal access token (read-only)
KINDLY_BROWSER_EXECUTABLE_PATH=  # Custom browser path (auto-detected)

# SearXNG advanced config
SEARXNG_HEADERS_JSON=   # JSON string with custom headers
SEARXNG_USER_AGENT=     # Custom user agent for SearXNG

Browser Detection

Kindly auto-detects browsers in this order:

1. KINDLY_BROWSER_EXECUTABLE_PATH (if set)
2. Chrome
3. Chromium
4. Edge
5. Brave

Windows manual path (if auto-detection fails):

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

Common Usage Patterns

Finding Error Solutions

Prompt:

I'm getting "RuntimeError: CUDA out of memory" in PyTorch. Search for solutions and workarounds.

Result: Kindly searches StackOverflow, GitHub issues, and documentation, returning complete threads with:

• Multiple solutions tried by others
• Code examples showing memory management
• Comments explaining tradeoffs
• Links to related discussions

API Documentation Lookup

Prompt:

Search for the latest FastAPI dependency injection documentation and examples.

Result: Returns official docs with:

• Full API reference
• Complete code examples
• Best practices sections
• Version-specific notes

Research Paper Content

Prompt:

Get the full content of the attention mechanism paper from arXiv.

Result: Returns complete paper text with:

• Abstract and introduction
• Full methodology section
• Equations and formulas
• References

GitHub Issue Investigation

Prompt:

Find GitHub issues about memory leaks in Langchain streaming responses.

Result: Returns relevant issues with:

• Full issue descriptions
• All community comments
• Proposed solutions and workarounds
• Current status and fixes

Real-World Examples

Example 1: Debugging Cloud Infrastructure

Prompt:

Search for solutions to "GCP Cloud Batch fails with GPU instance template"

What you get:

# StackOverflow Result 1
## Question (Score: 15)
I am trying to run a GCP Cloud Batch job with K80 GPU...
[full question with code]

## Answer (Accepted, Score: 23)
The issue is related to GPU quota limits...
[complete solution with configuration examples]

## Comments
- User123: "This also works for T4 GPUs"
- OP: "Confirmed, this fixed it. Also needed to..."

# GitHub Issue Result 2
## googleapis/python-batch #145 - GPU instance template failures
[Full issue thread with 12 comments and resolution]

Example 2: Library Usage Pattern

Prompt:

Search for best practices on using SQLAlchemy async sessions with FastAPI

What you get: Multiple results including:

• Official FastAPI documentation on async DB
• StackOverflow threads with production patterns
• GitHub discussions with real codebases
• All with complete code examples inline

Troubleshooting

"No search provider configured"

Problem: None of the search provider env vars are set.

Solution:

export SERPER_API_KEY="your-key"
# OR
export TAVILY_API_KEY="your-key"
# OR
export SEARXNG_BASE_URL="https://searx.example.org"

"Browser executable not found"

Problem: Chromium-based browser not installed or not detected.

Solution:

# Install Chrome/Chromium
brew install --cask chromium  # macOS
sudo apt-get install chromium  # Linux

# Or set custom path
export KINDLY_BROWSER_EXECUTABLE_PATH="/usr/bin/chromium"

First run timeout

Problem: First uvx invocation takes 30-60 seconds to build environment.

Solution: Run once in terminal to prewarm:

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

Then restart your MCP client.

GitHub rate limiting

Problem: GitHub API returns 403 or rate limit errors.

Solution: Set GITHUB_TOKEN:

# Create token at https://github.com/settings/tokens
# Needs: public_repo (classic) or repo read access (fine-grained)
export GITHUB_TOKEN="ghp_..."

Incomplete content extraction

Problem: Some pages return minimal content.

Possible causes:

1. Site blocks automated browsers
2. Content requires authentication
3. JavaScript errors prevent rendering

Workarounds:

• For documentation sites: Use direct API endpoints if available
• For gated content: Provide credentials via custom headers (SearXNG)
• For JavaScript-heavy sites: Content should work; file an issue if persistent

Integration with AI Coding Agent Workflow

Recommended Usage

When working with AI coding agents (Claude Code, Codex, Cursor):

1. Start broad: Let the agent search first

   Search for FastAPI WebSocket authentication patterns
   

2. Then drill down: Use get_content for specific URLs

   Get the content from [URL the agent found]
   

3. Combine with code generation: The agent now has full context

   Based on the search results, implement WebSocket auth for our API
   

Part of Shelpuk AI Suite

Kindly works best when combined with:

• tdd skill: Enforces TDD workflow
• Serena: Semantic code navigation
• Lad MCP Server: AI design reviews

Together these improve AI-generated code quality by 15-20%.

Advanced: SearXNG Self-Hosted

If using self-hosted SearXNG:

export SEARXNG_BASE_URL="https://searx.example.org"

# If instance requires authentication
export SEARXNG_HEADERS_JSON='{"Authorization":"Bearer your-token"}'

# If instance blocks default user agents
export SEARXNG_USER_AGENT="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"

Contributing

GitHub: https://github.com/Shelpuk-AI-Technology-Consulting/kindly-web-search-mcp-server

Issues and PRs welcome. See repository for contribution guidelines.