79 MCP Tools for Claude Code: Research, Write, Analyze, and More

The Problem: Limited Tool Access for Claude Developers

Claude is powerful, but it works best with external tools. Whether you're building research applications, content generation systems, or data analysis pipelines, you need access to a diverse toolkit without managing 79 different API integrations yourself.

Enter Model Context Protocol (MCP) tools. AiPayGent exposes 79 pre-configured MCP tools through a single unified API, letting you:

Research and fetch real-time data
Write and generate content at scale
Analyze documents, images, and code
Execute workflows without vendor lock-in
Pay only for what you use (first 10 calls/day free)

Let's dive into how to leverage these tools with concrete examples.

Getting Started: Authentication

AiPayGent uses API key authentication. Sign up at api.aipaygent.xyz to get your key.

Important: Your first 10 API calls per day are completely free. After that, top up your account with prepaid credits or pay with USDC on Base.

Discovering Available MCP Tools

Before we build, let's see what's available. The MCP endpoint category includes 79 tools. Get the full list:

curl -X GET "https://api.aipaygent.xyz/mcp/tools" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"

Example Response (Partial):

{
  "tools": [
    {
      "name": "web_search",
      "category": "research",
      "description": "Search the web for current information",
      "parameters": ["query", "limit"]
    },
    {
      "name": "fetch_url",
      "category": "research",
      "description": "Fetch and parse content from a URL",
      "parameters": ["url", "format"]
    },
    {
      "name": "analyze_sentiment",
      "category": "analyze",
      "description": "Analyze sentiment in text",
      "parameters": ["text"]
    }
  ],
  "total": 79,
  "free_calls_remaining": 8
}

Real-World Example: Multi-Tool Research Pipeline

Let's build a practical example that combines multiple MCP tools to research a topic, fetch detailed content, and analyze it.

Scenario: Analyze sentiment and key points from recent AI news

Step 1: Search for recent articles

curl -X POST "https://api.aipaygent.xyz/mcp/execute" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tool": "web_search",
    "params": {
      "query": "latest advances in Claude AI 2024",
      "limit": 5
    }
  }'

Example Response:

{
  "results": [
    {
      "title": "Claude Releases New Reasoning Model",
      "url": "https://example.com/article1",
      "snippet": "Anthropic announced Claude with extended thinking capabilities..."
    },
    {
      "title": "Enterprise AI: What Changed This Month",
      "url": "https://example.com/article2",
      "snippet": "Major updates to Claude's API and tool integration..."
    }
  ],
  "call_count": 1,
  "free_calls_remaining": 7
}

Step 2: Fetch full article content

curl -X POST "https://api.aipaygent.xyz/mcp/execute" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tool": "fetch_url",
    "params": {
      "url": "https://example.com/article1",
      "format": "text"
    }
  }'

Step 3: Analyze sentiment and extract key insights

curl -X POST "https://api.aipaygent.xyz/mcp/execute" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tool": "analyze_sentiment",
    "params": {
      "text": "[Full article text from step 2]"
    }
  }'

Python Example: Complete Workflow

import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://api.aipaygent.xyz/mcp"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Search for content
search_response = requests.post(
    f"{BASE_URL}/execute",
    headers=headers,
    json={
        "tool": "web_search",
        "params": {"query": "MCP tools AI development", "limit": 3}
    }
)

results = search_response.json()["results"]

# Fetch and analyze first result
for result in results:
    fetch_response = requests.post(
        f"{BASE_URL}/execute",
        headers=headers,
        json={
            "tool": "fetch_url",
            "params": {"url": result["url"], "format": "text"}
        }
    )
    
    content = fetch_response.json()["content"]
    
    # Analyze
    analysis = requests.post(
        f"{BASE_URL}/execute",
        headers=headers,
        json={
            "tool": "analyze_sentiment",
            "params": {"text": content[:1000]}
        }
    )
    
    print(f"URL: {result['url']}")
    print(f"Sentiment: {analysis.json()['sentiment']}")
    print("---")

Pricing & Free Tier

Free Tier: 10 API calls per day, every day. Perfect for development and testing.

Paid Plans: After your daily free calls, purchase credits via:

Prepaid API credits (add funds to your account)
USDC on Base blockchain (direct wallet payments)

Need more credits? Visit https://api.aipaygent.xyz/buy-credits

Next Steps

Explore all 79 MCP tools and detailed documentation:

Discovery Hub: https://api.aipaygent.xyz/discover
OpenAPI Spec: https://api.aipaygent.xyz/openapi.json

Start building with the first 10 free calls today. Scale as you grow.