scrapeRL / docs /mcp.md
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# mcp-server-integration
## table-of-contents
1. [Overview](#overview)
2. [Available MCP Servers](#available-mcp-servers)
3. [Tool Registry & Discovery](#tool-registry--discovery)
4. [HTML Processing MCPs](#html-processing-mcps)
5. [Lazy Loading System](#lazy-loading-system)
6. [MCP Composition](#mcp-composition)
7. [Testing Panel](#testing-panel)
8. [Configuration](#configuration)
---
## overview
The **Model Context Protocol (MCP)** enables the WebScraper agent to interact with external tools, databases, and services through a standardized interface. MCP servers expose **tools** that the agent can discover and use dynamically.
### why-mcp
**Without MCP:**
- Agent limited to built-in capabilities
- Cannot access external databases, APIs, or specialized libraries
- Difficult to extend without code changes
**With MCP:**
- Dynamically discover and use 100+ community tools
- Access databases (PostgreSQL, MongoDB, etc.)
- Use specialized libraries (BeautifulSoup, Selenium, Playwright)
- Integrate with external APIs (Google, GitHub, etc.)
- Extend agent capabilities without code changes
### architecture
```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ WebScraper Agent β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ β”‚
β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚
β”‚ β”‚ MCP Tool Registry β”‚ β”‚
β”‚ β”‚ - Discovers available tools from all MCP servers β”‚ β”‚
β”‚ β”‚ - Provides tool metadata to agent β”‚ β”‚
β”‚ β”‚ - Routes tool calls to appropriate server β”‚ β”‚
β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚
β”‚ β”‚ β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
β”‚
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ β”‚ β”‚ β”‚ β”‚
β–Ό β–Ό β–Ό β–Ό β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ HTML Parser β”‚ β”‚Browser β”‚ β”‚ Database β”‚ β”‚ File β”‚ β”‚ Custom β”‚
β”‚ MCP β”‚ β”‚ MCP β”‚ β”‚ MCP β”‚ β”‚ System β”‚ β”‚ MCP β”‚
β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ MCP β”‚ β”‚ β”‚
β”‚β€’ BeautifulSoupβ”‚β”‚β€’ Puppeteerβ”‚β”‚β€’ Postgresβ”‚β”‚β€’ Read β”‚β”‚β€’ Your β”‚
β”‚β€’ lxml β”‚β”‚β€’ Playwrightβ”‚β”‚β€’ MongoDB β”‚β”‚β”‚β€’ Write β”‚β”‚ tools β”‚
β”‚β€’ html5lib β”‚β”‚β€’ Selenium β”‚β”‚β€’ Redis β”‚β”‚β”‚β€’ Search β”‚β”‚ β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```
---
## available-mcp-servers
### 1-html-processing-and-parsing
#### beautifulsoup-mcp
Advanced HTML parsing and extraction.
**Tools:**
- `parse_html(html: str, parser: str = "html.parser")` β†’ Parse HTML into DOM tree
- `find_all(html: str, selector: str)` β†’ CSS selector search
- `extract_text(html: str, selector: str)` β†’ Extract text content
- `extract_attributes(html: str, selector: str, attrs: List[str])` β†’ Get element attributes
- `clean_html(html: str)` β†’ Remove scripts, styles, comments
- `extract_tables(html: str)` β†’ Parse all tables into structured data
**Configuration:**
```json
{
"mcpServers": {
"beautifulsoup": {
"command": "python",
"args": ["-m", "mcp_beautifulsoup"],
"enabled": true,
"autoDownload": true,
"config": {
"default_parser": "lxml",
"encodings": ["utf-8", "latin-1"]
}
}
}
}
```
**Example Usage:**
```python
# Agent action
action = Action(
action_type="MCP_TOOL_CALL",
tool_name="beautifulsoup.find_all",
tool_params={
"html": observation.page_html,
"selector": "div.product-card"
}
)
# Response
{
"products": [
{"name": "Widget", "price": "$49.99"},
{"name": "Gadget", "price": "$39.99"}
]
}
```
#### lxml-mcp
Fast XML/HTML parsing with XPath support.
**Tools:**
- `xpath_query(html: str, xpath: str)` β†’ XPath extraction
- `css_select(html: str, css: str)` β†’ CSS selector (fast)
- `validate_html(html: str)` β†’ Check well-formedness
#### html5lib-mcp
Standards-compliant HTML5 parsing.
**Tools:**
- `parse_html5(html: str)` β†’ Parse like a browser would
- `sanitize_html(html: str, allowed_tags: List[str])` β†’ Safe HTML cleaning
### 2-browser-automation
#### playwright-mcp
Full browser automation with JavaScript rendering.
**Tools:**
- `navigate(url: str, wait_for: str = "networkidle")` β†’ Load page with JS
- `click(selector: str)` β†’ Click element
- `fill_form(selector: str, value: str)` β†’ Fill input
- `screenshot(selector: str = None)` β†’ Capture screenshot
- `wait_for_selector(selector: str, timeout: int = 5000)` β†’ Wait for element
- `execute_script(script: str)` β†’ Run custom JavaScript
**Use Cases:**
- Pages with client-side rendering (React, Vue, Angular)
- Infinite scroll / lazy loading
- Forms and interactions
- Captcha handling
**Configuration:**
```json
{
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp-server"],
"enabled": false, // Only enable when needed (heavy)
"autoDownload": true,
"config": {
"browser": "chromium",
"headless": true,
"viewport": {"width": 1920, "height": 1080}
}
}
}
}
```
#### puppeteer-mcp
Lightweight browser automation (Chrome DevTools Protocol).
Similar to Playwright but lighter weight.
#### selenium-mcp
Legacy browser automation (more compatible, slower).
### 3-database-access
#### postgresql-mcp
Access PostgreSQL databases.
**Tools:**
- `query(sql: str, params: List = [])` β†’ Execute SELECT
- `execute(sql: str, params: List = [])` β†’ Execute INSERT/UPDATE/DELETE
- `list_tables()` β†’ Get schema
**Use Case:** Store scraped data directly to production database.
#### mongodb-mcp
Access MongoDB collections.
**Tools:**
- `find(collection: str, query: dict)` β†’ Query documents
- `insert(collection: str, document: dict)` β†’ Insert document
- `aggregate(collection: str, pipeline: List)` β†’ Aggregation pipeline
#### redis-mcp
Fast cache and pub/sub.
**Tools:**
- `get(key: str)` β†’ Retrieve cached value
- `set(key: str, value: str, ttl: int)` β†’ Cache value
- `publish(channel: str, message: str)` β†’ Pub/sub
**Use Case:** Cache parsed HTML, share state between agents.
### 4-file-system
#### filesystem-mcp
Read/write local files.
**Tools:**
- `read_file(path: str)` β†’ Read text/binary file
- `write_file(path: str, content: str)` β†’ Write file
- `list_directory(path: str)` β†’ List files
- `search_files(pattern: str)` β†’ Glob search
**Use Case:** Save scraped data to CSV/JSON, read configuration files.
### 5-search-engines
#### google-search-mcp
Google Search API integration.
**Tools:**
- `search(query: str, num: int = 10)` β†’ Google Search results
- `search_images(query: str)` β†’ Image search
**Configuration:**
```json
{
"mcpServers": {
"google-search": {
"command": "python",
"args": ["-m", "mcp_google_search"],
"enabled": true,
"autoDownload": true,
"config": {
"api_key": "YOUR_GOOGLE_API_KEY",
"search_engine_id": "YOUR_SEARCH_ENGINE_ID"
}
}
}
}
```
#### bing-search-mcp
Bing Search API.
#### brave-search-mcp
Privacy-focused search (Brave Search API).
#### duckduckgo-mcp
Free, no-API search.
**Tools:**
- `search(query: str, max_results: int = 10)` β†’ DDG results
### 6-data-extraction
#### readability-mcp
Extract main article content (removes ads, navigation, etc.).
**Tools:**
- `extract_article(html: str)` β†’ Returns clean article text + metadata
**Use Case:** Extract blog posts, news articles, documentation.
#### trafilatura-mcp
Advanced web scraping and text extraction.
**Tools:**
- `extract(url: str)` β†’ Extract main content
- `extract_metadata(html: str)` β†’ Get title, author, date, etc.
#### newspaper-mcp
News article extraction and NLP.
**Tools:**
- `parse_article(url: str)` β†’ Full article data
- `extract_keywords(text: str)` β†’ Keyword extraction
- `summarize(text: str)` β†’ Auto-summarization
### 7-data-validation
#### cerberus-mcp
Schema validation for extracted data.
**Tools:**
- `validate(data: dict, schema: dict)` β†’ Validate against schema
**Example:**
```python
# Define schema
schema = {
"product_name": {"type": "string", "required": True, "minlength": 1},
"price": {"type": "float", "required": True, "min": 0},
"rating": {"type": "float", "min": 0, "max": 5}
}
# Validate extracted data
result = mcp.call("cerberus.validate", data=extracted_data, schema=schema)
if not result["valid"]:
print("Validation errors:", result["errors"])
```
#### pydantic-mcp
Pydantic model validation.
### 8-computer-vision
#### ocr-mcp
Extract text from images (Tesseract OCR).
**Tools:**
- `extract_text(image_path: str, lang: str = "eng")` β†’ OCR text
**Use Case:** Extract prices from product images, read captchas (if legal).
#### image-analysis-mcp
Vision AI (GPT-4 Vision, Claude Vision).
**Tools:**
- `describe_image(image_path: str)` β†’ Natural language description
- `extract_structured(image_path: str, schema: dict)` β†’ Extract structured data from images
### 9-http-and-networking
#### requests-mcp
HTTP client with retry, session management.
**Tools:**
- `get(url: str, headers: dict = {})` β†’ HTTP GET
- `post(url: str, data: dict = {})` β†’ HTTP POST
#### proxy-manager-mcp
Manage proxy rotation, IP reputation.
**Tools:**
- `get_proxy()` β†’ Get next proxy from pool
- `report_dead_proxy(proxy: str)` β†’ Mark proxy as failed
### 10-utility
#### regex-mcp
Advanced regex operations.
**Tools:**
- `find_all(pattern: str, text: str)` β†’ Find all matches
- `replace(pattern: str, replacement: str, text: str)` β†’ Regex replace
- `validate(pattern: str)` β†’ Check if regex is valid
#### datetime-mcp
Parse and normalize dates.
**Tools:**
- `parse_date(text: str)` β†’ Parse natural language dates
- `normalize_timezone(date: str, tz: str)` β†’ Convert timezone
#### currency-mcp
Currency parsing and conversion.
**Tools:**
- `parse_price(text: str)` β†’ Extract price and currency
- `convert(amount: float, from_currency: str, to_currency: str)` β†’ Convert
---
## tool-registry-and-discovery
The **Tool Registry** automatically discovers all available tools from enabled MCP servers.
### architecture
```python
class MCPToolRegistry:
def __init__(self):
self.servers: Dict[str, MCPServer] = {}
self.tools: Dict[str, Tool] = {} # tool_name β†’ Tool
def discover_servers(self, config: MCPConfig):
"""Load and connect to all enabled MCP servers."""
for server_name, server_config in config.mcpServers.items():
if not server_config.enabled:
continue
# Auto-download if needed
if server_config.autoDownload and not self.is_installed(server_config):
self.download_and_install(server_name, server_config)
# Connect to server
server = self.connect_server(server_name, server_config)
self.servers[server_name] = server
# Discover tools
for tool in server.list_tools():
full_name = f"{server_name}.{tool.name}"
self.tools[full_name] = tool
def get_tool(self, tool_name: str) -> Tool:
"""Get tool by fully qualified name (server.tool)."""
return self.tools.get(tool_name)
def search_tools(self, query: str, category: str = None) -> List[Tool]:
"""Search tools by natural language query."""
# Semantic search using tool descriptions
candidates = list(self.tools.values())
if category:
candidates = [t for t in candidates if t.category == category]
# Embed query and tools, rank by similarity
scored = []
for tool in candidates:
score = self.semantic_similarity(query, tool.description)
scored.append((tool, score))
scored.sort(key=lambda x: x[1], reverse=True)
return [tool for tool, score in scored[:10]]
```
### tool-metadata
Each tool exposes rich metadata:
```python
class Tool(BaseModel):
name: str # e.g., "find_all"
full_name: str # e.g., "beautifulsoup.find_all"
server: str # Server name
description: str # Human-readable description
category: str # "parsing" | "browser" | "database" | ...
input_schema: Dict[str, Any] # JSON Schema for parameters
output_schema: Dict[str, Any] # JSON Schema for return value
examples: List[ToolExample] # Usage examples
cost: ToolCost # Time/resource cost estimate
requires_auth: bool # Needs API keys?
rate_limit: Optional[RateLimit] # Rate limiting info
```
**Example:**
```python
Tool(
name="find_all",
full_name="beautifulsoup.find_all",
server="beautifulsoup",
description="Find all HTML elements matching a CSS selector",
category="parsing",
input_schema={
"type": "object",
"properties": {
"html": {"type": "string", "description": "HTML content to search"},
"selector": {"type": "string", "description": "CSS selector"}
},
"required": ["html", "selector"]
},
output_schema={
"type": "array",
"items": {"type": "object"}
},
examples=[
ToolExample(
input={"html": "<div class='item'>A</div>", "selector": ".item"},
output=[{"tag": "div", "text": "A", "class": "item"}]
)
],
cost=ToolCost(time_ms=10, cpu_intensive=False),
requires_auth=False
)
```
### auto-tool-discovery-by-agent
The agent can query the registry to find relevant tools:
```python
# Agent needs to parse HTML
available_tools = tool_registry.search_tools(
query="parse HTML and extract elements by CSS selector",
category="parsing"
)
# Top result: beautifulsoup.find_all
tool = available_tools[0]
# Agent calls the tool
action = Action(
action_type="MCP_TOOL_CALL",
tool_name=tool.full_name,
tool_params={
"html": observation.page_html,
"selector": "div.product-price"
}
)
```
---
## html-processing-mcps
### beautifulsoup-mcp-detailed
**Installation:**
```bash
pip install mcp-beautifulsoup
```
**Tools:**
#### 1-find-all-html-selector-limit-none
Find all elements matching CSS selector.
```python
result = mcp.call("beautifulsoup.find_all", {
"html": "<div class='price'>$10</div><div class='price'>$20</div>",
"selector": "div.price"
})
# Returns: [{"text": "$10"}, {"text": "$20"}]
```
#### 2-find-one-html-selector
Find first matching element.
```python
result = mcp.call("beautifulsoup.find_one", {
"html": obs.page_html,
"selector": "h1.product-title"
})
# Returns: {"text": "Widget Pro", "tag": "h1"}
```
#### 3-extract-tables-html
Parse all `<table>` elements into structured data.
```python
result = mcp.call("beautifulsoup.extract_tables", {"html": obs.page_html})
# Returns:
[
{
"headers": ["Product", "Price", "Stock"],
"rows": [
["Widget", "$49.99", "In Stock"],
["Gadget", "$39.99", "Out of Stock"]
]
}
]
```
#### 4-extract-links-html-base-url-none
Extract all links from page.
```python
result = mcp.call("beautifulsoup.extract_links", {
"html": obs.page_html,
"base_url": "https://example.com"
})
# Returns:
[
{"url": "https://example.com/product/123", "text": "View Product"},
{"url": "https://example.com/category/widgets", "text": "Widgets"}
]
```
#### 5-clean-html-html-remove-script-style-noscript
Remove unwanted elements.
```python
result = mcp.call("beautifulsoup.clean_html", {
"html": obs.page_html,
"remove": ["script", "style", "footer", "nav"]
})
# Returns: Clean HTML without ads, scripts, navigation
```
#### 6-smart-extract-html-field-name
Intelligent extraction based on field name.
```python
# Agent wants to extract "price"
result = mcp.call("beautifulsoup.smart_extract", {
"html": obs.page_html,
"field_name": "price"
})
# MCP searches for:
# - Elements with class/id containing "price"
# - Text matching price patterns ($X.XX, €X,XX)
# - Schema.org markup (itemprop="price")
# Returns: {"value": "$49.99", "confidence": 0.92, "selector": "span.product-price"}
```
### batch-processing-for-long-content
When HTML is too large (> 100KB), process in batches:
```python
class HTMLBatchProcessor:
def __init__(self, mcp_client, chunk_size: int = 50000):
self.mcp = mcp_client
self.chunk_size = chunk_size
def process_large_html(self, html: str, selector: str) -> List[Dict]:
"""Process large HTML in chunks."""
# Split HTML into meaningful chunks (by sections, not mid-tag)
chunks = self.split_html_intelligently(html)
results = []
for i, chunk in enumerate(chunks):
# Process each chunk
chunk_results = self.mcp.call("beautifulsoup.find_all", {
"html": chunk,
"selector": selector
})
# Deduplicate across chunk boundaries
results.extend(self.deduplicate(chunk_results, results))
return results
def split_html_intelligently(self, html: str) -> List[str]:
"""Split HTML at section boundaries, not mid-tag."""
soup = BeautifulSoup(html, 'lxml')
# Split by major sections (article, section, div.container, etc.)
sections = soup.find_all(['article', 'section', 'main'])
chunks = []
current_chunk = ""
for section in sections:
section_html = str(section)
if len(current_chunk) + len(section_html) > self.chunk_size:
chunks.append(current_chunk)
current_chunk = section_html
else:
current_chunk += section_html
if current_chunk:
chunks.append(current_chunk)
return chunks
```
---
## lazy-loading-system
MCP servers are **NOT downloaded by default**. They are installed on-demand when first used.
### download-on-demand-flow
```
Agent wants to use a tool
β”‚
β–Ό
Is MCP server installed?
β”‚
β”Œβ”€β”€β”€β”€β”΄β”€β”€β”€β”€β”
No Yes
β”‚ β”‚
β–Ό β–Ό
Show dialog Execute tool
"Download
server X?"
β”‚
β”Œβ”€β”€β”€β”΄β”€β”€β”€β”
No Yes
β”‚ β”‚
Skip Download & Install
β”‚
β–Ό
Cache for future use
β”‚
β–Ό
Execute tool
```
### implementation
```python
class LazyMCPLoader:
def __init__(self):
self.installed_servers: Set[str] = set()
self.download_queue: Queue[str] = Queue()
def ensure_server(self, server_name: str, config: MCPServerConfig) -> bool:
"""Ensure MCP server is installed, download if needed."""
if server_name in self.installed_servers:
return True
if not config.autoDownload:
# Prompt user
if not self.prompt_user_download(server_name):
return False
# Download and install
return self.download_server(server_name, config)
def download_server(self, server_name: str, config: MCPServerConfig) -> bool:
"""Download and install MCP server."""
try:
logger.info(f"Downloading MCP server: {server_name}")
if config.command == "npx":
# NPM package
subprocess.run([
"npm", "install", "-g", config.args[1]
], check=True)
elif config.command == "python":
# Python package
package_name = config.args[1].replace("-m ", "")
subprocess.run([
"pip", "install", package_name
], check=True)
self.installed_servers.add(server_name)
logger.info(f" Installed {server_name}")
return True
except Exception as e:
logger.error(f"Failed to install {server_name}: {e}")
return False
def prompt_user_download(self, server_name: str) -> bool:
"""Ask user if they want to download the server."""
# In UI, show dialog:
# "Tool X requires MCP server Y. Download and install? (50MB) [Yes] [No]"
return self.show_download_dialog(server_name)
```
### ui-dialog
```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ MCP Server Required β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ β”‚
β”‚ The tool "beautifulsoup.find_all" requires the MCP β”‚
β”‚ server "beautifulsoup" which is not installed. β”‚
β”‚ β”‚
β”‚ Package: mcp-beautifulsoup β”‚
β”‚ Size: ~5 MB β”‚
β”‚ β”‚
β”‚ Would you like to download and install it now? β”‚
β”‚ β”‚
β”‚ [Download & Install] [Skip] β”‚
β”‚ β”‚
β”‚ Remember my choice for this server β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```
---
## mcp-composition
Combine multiple MCP tools to create powerful workflows.
### example-1-parse-html-extract-tables-save-to-database
```python
# Step 1: Clean HTML
cleaned = mcp.call("beautifulsoup.clean_html", {
"html": observation.page_html
})
# Step 2: Extract tables
tables = mcp.call("beautifulsoup.extract_tables", {
"html": cleaned["html"]
})
# Step 3: Save to PostgreSQL
for table in tables:
mcp.call("postgresql.execute", {
"sql": "INSERT INTO scraped_data (data) VALUES (%s)",
"params": [json.dumps(table)]
})
```
### example-2-search-google-navigate-parse-article-summarize
```python
# Step 1: Search
results = mcp.call("google-search.search", {
"query": "best widgets 2026",
"num": 5
})
# Step 2: Navigate to top result
mcp.call("playwright.navigate", {
"url": results[0]["url"]
})
# Step 3: Extract article
article = mcp.call("readability.extract_article", {
"html": mcp.call("playwright.get_html", {})
})
# Step 4: Summarize
summary = mcp.call("llm.summarize", {
"text": article["text"],
"max_length": 200
})
```
### composition-dsl
Define reusable workflows:
```python
class MCPWorkflow:
def __init__(self, name: str, steps: List[WorkflowStep]):
self.name = name
self.steps = steps
async def execute(self, initial_input: Dict) -> Dict:
"""Execute workflow steps sequentially."""
context = initial_input
for step in self.steps:
result = await mcp.call(step.tool, step.params(context))
context[step.output_var] = result
return context
# Define workflow
extract_and_save = MCPWorkflow(
name="extract_and_save",
steps=[
WorkflowStep(
tool="beautifulsoup.find_all",
params=lambda ctx: {"html": ctx["html"], "selector": ctx["selector"]},
output_var="extracted"
),
WorkflowStep(
tool="cerberus.validate",
params=lambda ctx: {"data": ctx["extracted"], "schema": ctx["schema"]},
output_var="validated"
),
WorkflowStep(
tool="postgresql.execute",
params=lambda ctx: {"sql": "INSERT INTO items ...", "params": ctx["validated"]},
output_var="saved"
)
]
)
# Execute
result = await extract_and_save.execute({
"html": obs.page_html,
"selector": "div.product",
"schema": PRODUCT_SCHEMA
})
```
---
## testing-panel
Test MCP tools manually before using them in agent workflows.
### ui
```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ MCP Testing Panel β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚ β”‚
β”‚ Server: [beautifulsoup β–Ό] β”‚
β”‚ Tool: [find_all β–Ό] β”‚
β”‚ β”‚
β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚
β”‚ β”‚ Input Parameters: β”‚ β”‚
β”‚ β”‚ β”‚ β”‚
β”‚ β”‚ html: β”‚ β”‚
β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚
β”‚ β”‚ β”‚ <div class="item">Item 1</div> β”‚ β”‚ β”‚
β”‚ β”‚ β”‚ <div class="item">Item 2</div> β”‚ β”‚ β”‚
β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚
β”‚ β”‚ β”‚ β”‚
β”‚ β”‚ selector: [div.item ] β”‚ β”‚
β”‚ β”‚ β”‚ β”‚
β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚
β”‚ β”‚
β”‚ [Execute Tool] [Clear] β”‚
β”‚ β”‚
β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚
β”‚ β”‚ Output: β”‚ β”‚
β”‚ β”‚ β”‚ β”‚
β”‚ β”‚ [ β”‚ β”‚
β”‚ β”‚ {"tag": "div", "class": "item", "text": "Item 1"}, β”‚ β”‚
β”‚ β”‚ {"tag": "div", "class": "item", "text": "Item 2"} β”‚ β”‚
β”‚ β”‚ ] β”‚ β”‚
β”‚ β”‚ β”‚ β”‚
β”‚ β”‚ Execution time: 12ms β”‚ β”‚
β”‚ β”‚ Status: Success β”‚ β”‚
β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚
β”‚ β”‚
β”‚ [Save as Example] β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```
---
## configuration
### full-mcp-configuration-example
```json
{
"mcpServers": {
"beautifulsoup": {
"command": "python",
"args": ["-m", "mcp_beautifulsoup"],
"enabled": true,
"autoDownload": true,
"config": {
"default_parser": "lxml"
}
},
"playwright": {
"command": "npx",
"args": ["@playwright/mcp-server"],
"enabled": false,
"autoDownload": false,
"config": {
"browser": "chromium",
"headless": true
}
},
"postgresql": {
"command": "python",
"args": ["-m", "mcp_postgresql"],
"enabled": false,
"autoDownload": false,
"config": {
"host": "localhost",
"port": 5432,
"database": "scraper_db",
"user": "postgres",
"password": "${PG_PASSWORD}"
}
},
"google-search": {
"command": "python",
"args": ["-m", "mcp_google_search"],
"enabled": true,
"autoDownload": true,
"config": {
"api_key": "${GOOGLE_API_KEY}",
"search_engine_id": "${GOOGLE_SE_ID}"
}
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "./scraped_data"],
"enabled": true,
"autoDownload": true
}
},
"mcpSettings": {
"autoDiscoverTools": true,
"toolTimeout": 30,
"maxConcurrentCalls": 5,
"retryFailedCalls": true,
"cacheToolResults": true,
"cacheTTL": 3600
}
}
```
---
**Next:** See [settings.md](./settings.md) for complete dashboard settings.
## related-api-reference
| item | value |
| --- | --- |
| api-reference | `api-reference.md` |
## document-metadata
| key | value |
| --- | --- |
| document | `mcp.md` |
| status | active |
## document-flow
```mermaid
flowchart TD
A[document] --> B[key-sections]
B --> C[implementation]
B --> D[operations]
B --> E[validation]
```