Building AI agents in 2025 means constantly assembling a patchwork of tools. Resume parsers here, content generators there, text analysis somewhere else.
I got tired of it and built the AI Tools MCP Bundle — 8 tools in one endpoint, in MCP format so it plugs directly into Claude and Cursor without any glue code.
Here's a quick demo — resume parsing in Python:
import requests
response = requests.post(
"https://ai-tools-mcp-bundle.p.rapidapi.com/parse-resume",
json={"resume_text": "Jane Smith, Senior ML Engineer, 7 years..."},
headers={
"x-rapidapi-key": "YOUR_KEY",
"x-rapidapi-host": "ai-tools-mcp-bundle.p.rapidapi.com"
}
)
print(response.json())
# → { "name": "Jane Smith", "role": "Senior ML Engineer", "years_exp": 7, ... }
Or content generation:
response = requests.post(
"https://ai-tools-mcp-bundle.p.rapidapi.com/generate-content",
json={"type": "blog", "topic": "Why MCP is the future of AI tooling", "tone": "professional"},
headers={...}
)
Why I chose MCP format: The Model Context Protocol lets AI assistants like Claude call your tools natively in their reasoning loop. No prompt engineering to get output in the right shape — the tool schema does that for you.
All 8 tools, one subscription:
Resume parsing, job matching, content generation, sentiment analysis, keyword extraction, readability scoring, AI content detection, and prompt rephrasing.
Free tier available (50 req/mo). Paid from $14.99.
👉 https://rapidapi.com/phaniavagaddi/api/ai-tools-mcp-bundle-resume-content-and-text-analysis
What are you building with MCP? Let me know in the comments.
Top comments (1)
Interesting approach using MCP format.