Claude Code MCP Tool Examples - Anysite Documentation

Overview

This guide demonstrates practical workflows and examples for using Anysite MCP tools with Claude Code. These examples show how to leverage the CLI-based integration for development, automation, and team collaboration.

Basic Usage

Starting a Session with MCP

Once configured, simply start Claude Code and the MCP server connects automatically:

claude

Your Anysite tools are immediately available. Try:

What MCP tools do I have access to?

Claude will list all available Anysite tools from the connected MCP server.

Quick Data Extraction

Example: LinkedIn Profile Analysis

Extract information from this LinkedIn profile:
https://linkedin.com/in/satyanadella

Focus on:
- Current role and company
- Career progression
- Education background

Claude will use the linkedin_user MCP tool to fetch and analyze the data.

Scope Management Examples

Example 1: Personal User-Scoped Setup

For tools you use across all your projects:

# Add to user scope (available everywhere)
claude mcp add --transport http --scope user anysite "https://mcp.anysite.io/mcp?api_key=YOUR_KEY"

Use case: You’re a researcher who frequently extracts LinkedIn data across multiple projects. Benefits:

Configure once, use everywhere
No per-project setup needed
Personal API key stays private

Example 2: Team Project-Scoped Setup

For shared team projects with version-controlled config:

# Navigate to project directory
cd my-team-project

# Add with project scope
claude mcp add --scope project anysite "https://mcp.anysite.io/mcp" \
  --env API_KEY=$ANYSITE_API_KEY

# Commit .mcp.json to git
git add .mcp.json
git commit -m "Add Anysite MCP configuration"

Team members then:

# Pull the project
git pull

# Set their own API key
export ANYSITE_API_KEY="their_key_here"

# Claude Code automatically uses project config
claude

Benefits:

Shared configuration
Individual API keys (not committed)
Consistent team setup

Example 3: Temporary Local Setup

For quick tests or temporary work:

# Add with local scope (default)
claude mcp add --transport http anysite "https://mcp.anysite.io/mcp?api_key=TEMP_KEY"

# Use for this session
claude

# Remove when done
claude mcp remove anysite

Use case: Testing with a trial API key or working on a temporary proof-of-concept.

Development Workflows

Workflow 1: Competitive Intelligence Research

Setup:

# Create research project
mkdir competitor-research
cd competitor-research

# Add Anysite with project scope
claude mcp add --scope project anysite "YOUR_URL"

Usage in Claude Code:

I'm researching competitors in the CRM space. For each company, get:

1. LinkedIn company pages:
   - https://linkedin.com/company/salesforce
   - https://linkedin.com/company/hubspot
   - https://linkedin.com/company/zoho

2. Extract:
   - Company size
   - Growth trends
   - Recent updates
   - Key executives

3. Create a comparison table

Automated with script:

#!/bin/bash
# research.sh

companies=(
  "salesforce"
  "hubspot"
  "zoho"
)

for company in "${companies[@]}"; do
  echo "Researching $company..." >> research.log
  claude <<EOF
Extract company information from https://linkedin.com/company/$company
Save key metrics to ${company}_data.json
EOF
done

Workflow 2: Lead Generation Pipeline

Project structure:

lead-gen/
├── .mcp.json          # Project config
├── leads.csv          # Input: URLs to research
├── scripts/
│   └── process_leads.sh
└── output/
    └── enriched_leads.json

Setup:

cd lead-gen
claude mcp add --scope project anysite "YOUR_URL"

Processing script:

#!/bin/bash
# scripts/process_leads.sh

while IFS=, read -r name linkedin_url; do
  echo "Processing: $name"

  claude <<EOF
Extract profile data from: $linkedin_url
Output JSON format:
{
  "name": "$name",
  "current_role": "",
  "company": "",
  "location": "",
  "experience_years": 0,
  "skills": []
}
EOF
done < leads.csv

Workflow 3: Content Monitoring Dashboard

Setup multiple servers for different sources:

# Anysite for social data
claude mcp add --scope user anysite "ANYSITE_URL"

# Monitor Reddit
claude <<EOF
Track mentions of "our-product" in these subreddits:
- r/technology
- r/startups
- r/SaaS

Check every 6 hours and summarize sentiment
EOF

Automated monitoring:

#!/bin/bash
# monitor.sh - Run via cron

claude <<EOF
Monitor these Reddit posts for new comments:
$(cat monitored_posts.txt)

If sentiment changes significantly, send summary to monitor@company.com
EOF

CI/CD Integration

Example 1: Automated Profile Verification

GitHub Actions workflow:

# .github/workflows/verify-profiles.yml
name: Verify Team Profiles

on:
  schedule:
    - cron: '0 9 * * 1'  # Every Monday at 9 AM
  workflow_dispatch:

jobs:
  verify:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Setup Claude Code
        run: |
          # Install Claude Code
          curl -sSL https://claude.ai/install.sh | bash

      - name: Configure MCP
        env:
          ANYSITE_API_KEY: ${{ secrets.ANYSITE_API_KEY }}
        run: |
          claude mcp add --scope local anysite \
            "https://mcp.anysite.io/mcp?api_key=$ANYSITE_API_KEY"

      - name: Verify Profiles
        run: |
          claude <<EOF
          Verify all LinkedIn profiles in team_profiles.json are still active.
          Check for:
          - Profile still exists
          - Job title changes
          - Company changes

          Output differences to profile_changes.md
          EOF

      - name: Create Issue if Changes
        if: success()
        run: |
          if [ -s profile_changes.md ]; then
            gh issue create \
              --title "Team Profile Changes Detected" \
              --body-file profile_changes.md \
              --label "hr,automated"
          fi

Example 2: Data Pipeline Integration

Airflow DAG:

# dags/linkedin_enrichment_dag.py

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'start_date': datetime(2024, 1, 1),
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
}

dag = DAG(
    'linkedin_enrichment',
    default_args=default_args,
    schedule_interval='@daily',
    catchup=False
)

# Configure MCP
setup_mcp = BashOperator(
    task_id='setup_mcp',
    bash_command='''
        claude mcp add --scope local anysite "$ANYSITE_MCP_URL"
    ''',
    dag=dag
)

# Enrich leads
enrich_data = BashOperator(
    task_id='enrich_leads',
    bash_command='''
        claude <<EOF
        Read leads from /data/new_leads.csv
        For each LinkedIn URL, extract:
        - Profile data
        - Current company info
        - Recent activity

        Output to /data/enriched_leads.json
        EOF
    ''',
    dag=dag
)

# Load to database
load_db = BashOperator(
    task_id='load_to_database',
    bash_command='python scripts/load_enriched_data.py',
    dag=dag
)

setup_mcp >> enrich_data >> load_db

Multi-Workspace Management

Managing Multiple Projects

Scenario: You work on different projects with different MCP needs.

# Project A: Marketing research
cd ~/projects/marketing-research
claude mcp add --scope project anysite-marketing "URL1"

# Project B: Sales intelligence
cd ~/projects/sales-intel
claude mcp add --scope project anysite-sales "URL2"

# Personal utilities (available everywhere)
claude mcp add --scope user anysite-personal "URL3"

Check current configuration:

# In any project
claude mcp list

# Output shows:
# anysite-personal (user scope) ✔ connected
# anysite-marketing (project scope, if in marketing-research/) ✔ connected

Switching Contexts

# Work on marketing project
cd ~/projects/marketing-research
claude  # Uses marketing-research/.mcp.json + user scope

# Switch to sales project
cd ~/projects/sales-intel
claude  # Uses sales-intel/.mcp.json + user scope

# Personal work
cd ~/documents
claude  # Uses only user scope

Advanced Techniques

Batch Processing with Scripts

Process multiple LinkedIn profiles:

#!/bin/bash
# batch_linkedin_extract.sh

input_file="linkedin_urls.txt"
output_dir="./extracted_data"
mkdir -p "$output_dir"

while IFS= read -r url; do
  # Extract profile ID from URL
  profile_id=$(echo "$url" | sed 's/.*linkedin.com\/in\///' | sed 's/\/.*//')

  echo "Processing: $profile_id"

  # Use Claude Code with MCP
  claude <<EOF > "${output_dir}/${profile_id}.json"
Extract LinkedIn profile data from: $url

Output as JSON with these fields:
{
  "profile_id": "$profile_id",
  "extracted_at": "$(date -Iseconds)",
  "data": {
    "name": "",
    "headline": "",
    "location": "",
    "current_position": {},
    "experience": [],
    "education": [],
    "skills": []
  }
}
EOF

  # Rate limiting
  sleep 2

done < "$input_file"

echo "Batch processing complete. Extracted $(ls -1 "$output_dir" | wc -l) profiles."

Conditional Logic Based on MCP Data

#!/bin/bash
# conditional_analysis.sh

company_url="https://linkedin.com/company/target-company"

# Extract data and analyze
result=$(claude <<EOF
Analyze this company: $company_url

Determine if they are:
1. Growing rapidly (>20% employee growth)
2. In tech industry
3. Located in US

Output only: YES or NO
EOF
)

if [ "$result" = "YES" ]; then
  echo "Company matches criteria. Generating detailed report..."

  claude <<EOF
Create comprehensive company report for: $company_url
Include competitive analysis and market positioning.
Save to: reports/$(date +%Y%m%d)_target_company.md
EOF

  # Notify team
  echo "Report ready" | mail -s "New Target Company Report" team@company.com
else
  echo "Company does not match criteria. Skipping detailed analysis."
fi

Integration with Data Analysis Tools

Python script using Claude Code:

#!/usr/bin/env python3
# analyze_linkedin_data.py

import subprocess
import json
import pandas as pd

def extract_profile(linkedin_url):
    """Extract LinkedIn profile using Claude Code MCP"""

    prompt = f"""
    Extract profile data from: {linkedin_url}
    Output as valid JSON only, no explanation.
    """

    result = subprocess.run(
        ['claude'],
        input=prompt,
        capture_output=True,
        text=True
    )

    return json.loads(result.stdout)

def main():
    # Load list of profiles to analyze
    with open('profiles_list.txt', 'r') as f:
        urls = [line.strip() for line in f]

    # Extract data
    profiles = []
    for url in urls:
        print(f"Extracting: {url}")
        data = extract_profile(url)
        profiles.append(data)

    # Create DataFrame
    df = pd.DataFrame(profiles)

    # Analysis
    print("\n=== Profile Analysis ===")
    print(f"Total profiles: {len(df)}")
    print(f"Average experience: {df['years_experience'].mean():.1f} years")
    print(f"\nTop 5 skills:")
    print(df['skills'].explode().value_counts().head())

    # Export results
    df.to_csv('linkedin_analysis.csv', index=False)
    print("\nResults saved to linkedin_analysis.csv")

if __name__ == '__main__':
    main()

Debugging and Monitoring

Debug Mode Usage

Enable debug output:

# Run with debug flag
claude --mcp-debug

# Or set environment variable
export CLAUDE_MCP_DEBUG=1
claude

Debug output shows:

[MCP] Connecting to: anysite (http)
[MCP] Transport: http
[MCP] URL: https://mcp.anysite.io/mcp?api_key=***
[MCP] Connection established
[MCP] Tools discovered: 15
[MCP] Tools: linkedin_user, linkedin_company, instagram_profile, ...

Monitoring MCP Usage

Check server status script:

#!/bin/bash
# check_mcp_status.sh

echo "=== MCP Server Status ==="
echo

# List all servers
echo "Configured servers:"
claude mcp list

echo
echo "Testing connection to anysite:"
claude mcp get anysite

# Log to file
{
  echo "Status check at: $(date)"
  claude mcp list
  echo "---"
} >> mcp_status.log

Run periodically:

# Add to crontab
0 */4 * * * /path/to/check_mcp_status.sh

Error Handling in Scripts

#!/bin/bash
# robust_extraction.sh

extract_with_retry() {
  local url=$1
  local max_attempts=3
  local attempt=1

  while [ $attempt -le $max_attempts ]; do
    echo "Attempt $attempt of $max_attempts"

    output=$(claude <<EOF 2>&1
Extract data from: $url
Output as JSON
EOF
    )

    # Check if extraction succeeded
    if echo "$output" | jq . > /dev/null 2>&1; then
      echo "$output"
      return 0
    fi

    echo "Extraction failed, retrying..."
    attempt=$((attempt + 1))
    sleep 5
  done

  echo "ERROR: Failed after $max_attempts attempts"
  return 1
}

# Usage
if result=$(extract_with_retry "https://linkedin.com/in/profile"); then
  echo "$result" > output.json
  echo "Success"
else
  echo "Failed to extract data" >&2
  exit 1
fi

Best Practices

1. Scope Selection Strategy

User Scope

Use for:

Personal API keys
Tools you use everywhere
Cross-project utilities

Example:

claude mcp add --scope user \
  anysite "URL"

Project Scope

Use for:

Team collaborations
Version-controlled configs
Project-specific setups

Example:

claude mcp add --scope project \
  anysite "URL" \
  --env API_KEY=$KEY

Local Scope

Use for:

Temporary setups
Testing
Sensitive credentials

Example:

claude mcp add \
  anysite-test "URL"

2. Security Checklist

✅ Use environment variables for API keys
✅ Add config files to .gitignore
✅ Rotate keys regularly
✅ Use --scope local for sensitive keys
✅ Audit configurations with claude mcp list
✅ Remove unused servers
❌ Never commit API keys to version control
❌ Don’t share Direct URLs publicly

3. Performance Optimization

Rate limiting:

# Add delays between requests
for url in "${urls[@]}"; do
  claude <<< "Extract: $url"
  sleep 2  # Respect API rate limits
done

Batch similar requests:

# Instead of multiple calls
claude <<EOF
Extract all these profiles in one request:
1. linkedin.com/in/profile1
2. linkedin.com/in/profile2
3. linkedin.com/in/profile3
EOF

Cache results:

# Save extracted data
output_file="cache/profile_${profile_id}.json"

if [ ! -f "$output_file" ]; then
  # Extract only if not cached
  claude <<< "Extract: $url" > "$output_file"
fi

Common Patterns

Pattern 1: Daily Automated Report

#!/bin/bash
# daily_report.sh

date=$(date +%Y-%m-%d)
report_file="reports/daily_${date}.md"

claude <<EOF > "$report_file"
Generate daily intelligence report:

1. Check these LinkedIn company pages for updates:
   - https://linkedin.com/company/competitor1
   - https://linkedin.com/company/competitor2

2. Monitor Reddit posts in r/industry for trending topics

3. Analyze sentiment and key themes

4. Format as executive summary in markdown
EOF

# Email the report
mail -s "Daily Intelligence Report - $date" \
  -a "$report_file" \
  executives@company.com < /dev/null

Pattern 2: Interactive Research Session

# Start research session
claude

# Then interactively:

Extract target company profile
Get list of key employees
Analyze their backgrounds
Identify common patterns
Generate hiring strategy recommendations

Pattern 3: Data Validation Pipeline

#!/bin/bash
# validate_data.sh

input_csv="leads.csv"
output_csv="validated_leads.csv"

# Validate each LinkedIn URL
while IFS=, read -r id name linkedin_url; do
  # Check if profile exists and is accessible
  status=$(claude <<EOF
Check if this LinkedIn profile is valid and accessible:
$linkedin_url

Output only: VALID or INVALID
EOF
  )

  echo "$id,$name,$linkedin_url,$status" >> "$output_csv"
done < "$input_csv"

Resources

Need Help?

Get Support

Contact our support team for assistance with Claude Code MCP workflows

​Overview

​Basic Usage

​Starting a Session with MCP

​Quick Data Extraction

​Scope Management Examples

​Example 1: Personal User-Scoped Setup

​Example 2: Team Project-Scoped Setup

​Example 3: Temporary Local Setup

​Development Workflows

​Workflow 1: Competitive Intelligence Research

​Workflow 2: Lead Generation Pipeline

​Workflow 3: Content Monitoring Dashboard

​CI/CD Integration

​Example 1: Automated Profile Verification

​Example 2: Data Pipeline Integration

​Multi-Workspace Management

​Managing Multiple Projects

​Switching Contexts

​Advanced Techniques

​Batch Processing with Scripts

​Conditional Logic Based on MCP Data

​Integration with Data Analysis Tools

​Debugging and Monitoring

​Debug Mode Usage

​Monitoring MCP Usage

​Error Handling in Scripts

​Best Practices

​1. Scope Selection Strategy

User Scope

Project Scope

Local Scope

​2. Security Checklist

​3. Performance Optimization

​Common Patterns

​Pattern 1: Daily Automated Report

​Pattern 2: Interactive Research Session

​Pattern 3: Data Validation Pipeline

​Resources

​Need Help?

Get Support

Overview

Basic Usage

Starting a Session with MCP

Quick Data Extraction

Scope Management Examples

Example 1: Personal User-Scoped Setup

Example 2: Team Project-Scoped Setup

Example 3: Temporary Local Setup

Development Workflows

Workflow 1: Competitive Intelligence Research

Workflow 2: Lead Generation Pipeline

Workflow 3: Content Monitoring Dashboard

CI/CD Integration

Example 1: Automated Profile Verification

Example 2: Data Pipeline Integration

Multi-Workspace Management

Managing Multiple Projects

Switching Contexts

Advanced Techniques

Batch Processing with Scripts

Conditional Logic Based on MCP Data

Integration with Data Analysis Tools

Debugging and Monitoring

Debug Mode Usage

Monitoring MCP Usage

Error Handling in Scripts

Best Practices

1. Scope Selection Strategy

2. Security Checklist

3. Performance Optimization

Common Patterns

Pattern 1: Daily Automated Report

Pattern 2: Interactive Research Session

Pattern 3: Data Validation Pipeline

Resources

Need Help?