What is Langflow Mcp High Ats Resume Creator

LangFlow-MCP-High-ATS-Resume-creator is a custom multi-agent flow designed to generate high ATS (Applicant Tracking System) score resumes. It utilizes a user’s existing resume and a LinkedIn job post URL to create tailored, ATS-friendly resumes in various formats.

Use cases

Use cases include job seekers wanting to tailor their resumes for specific job applications, career coaches assisting clients in creating effective resumes, and educational institutions offering resume writing services.

How to use

To use LangFlow-MCP-High-ATS-Resume-creator, input your current resume and the URL of a LinkedIn job post into the system. The flow will parse both inputs, summarize the relevant content, and generate an optimized resume.

Key features

Key features include modular low-code orchestration, interoperability with Claude Desktop and other MCP-compatible clients, stateful agent behavior, and the ability to produce resumes in LaTeX format.

Where to use

LangFlow-MCP-High-ATS-Resume-creator can be used in job application processes, career counseling, and resume workshops, particularly for individuals seeking to enhance their resume’s ATS compatibility.

🧠 LangFlow MCP High ATS Resume Creator (ATS-Aware)

This repository contains a LangFlow-exported .json file representing a custom multi-agent flow, designed to act as an MCP server. This flow can be used with Claude Desktop or any MCP-compatible client.

Its core functionality is to generate a high ATS (Applicant Tracking System) score resume based on a LinkedIn job post URL and a user’s existing resume. The system parses both sources, summarizes relevant content, and produces a tailored, ATS-friendly resume in multiple formats.

📜 Architectural Decision Records (ADRs)

ADR 001 – Why LangFlow?

LangFlow allows for modular low-code orchestration of agentic LLM pipelines, making it ideal for our goal of chaining multiple agents with custom components and data parsers.

ADR 002 – Why MCP Server?

By exposing this flow as an MCP server:

• It becomes interoperable with Claude Desktop and other MCP clients.
• Users can interact via chat interfaces and benefit from stateful agent behavior.
• Encourages reuse and composability across different workflows.

ADR 003 – Agent Design and Flow

🎯 Agent 1: Resume Summarizer

• Input: User’s current resume (raw text or document)
• Output: Summary of professional experience, total years, key skills, responsibilities, and education.
• ✅ Focused extraction using resume-aware prompt logic.

🎯 Agent 2: Job Description Summarizer

• Input: LinkedIn job post URL via chat.
• Output: Structured summary of required qualifications.
• 🛠️ Powered by a custom LangFlow component that parses HTML directly from the URL input.

🔄 Data Parsing and Conversion

• Each agent’s output passes through a Message-to-Data Converter, followed by a combined parser to normalize structure and prepare inputs for resume generation.

🎯 Agent 3: Resume Generator

• Input: Parsed summaries from Agent 1 & Agent 2.
• Output: ATS-optimized resume in LaTeX format matching job criteria with user’s skills.
• 🧠 Tailored resume structure focused on score improvement.

🛠️ Custom LangFlow Component: Format Converter

• Converts LaTeX output to:
  • ✅ PDF
  • ✅ DOCX
  • ✅ TXT
• Makes the resume instantly downloadable and shareable.

🎯 Agent 4: ATS Score Evaluator

• Input: Generated resume and job summary.
• Output: Final ATS score (out of 100) indicating how well the resume matches the job.
• ✅ Helps users iterate on improvements.

🔀 ADR 004 – Data Pipeline

flowchart TD
    FileUpload[File Upload] --> Agent1[Agent 1: Resume Summary]
    ChatInput[Chat Input URL] --> MCPCustomcomponent[URL HTML Fetcher]
    MCPCustomcomponent --> Agent2[Agent 2: Job Summary]
    Agent1 --> MsgToData1[Message to Data 1]
    Agent2 --> MsgToData2[Message to Data 2]
    MsgToData1 --> Merge1[Merge Resume + Job Summary]
    MsgToData2 --> Merge1
    Merge1 --> Parse1[Parser for Resume]
    Parse1 --> Agent3[Agent 3: Resume Generator in latex]
    Agent3 --> MsgToData3[Message to Data 3]
    MsgToData3 --> Parse2[Parser for LaTeX]
    Parse2 --> LatexToPDF[Convert LaTeX to PDF/DOCX/TXT]
    LatexToPDF --> ResumeOut[Output Resume]
    LatexToPDF --> MsgToData4[Message to Data 4]
    Agent2 --> MsgToData5[Message to Data 5]
    MsgToData4 --> Merge2[Merge Resume + Job Summary for Scoring]
    MsgToData5 --> Merge2
    Merge2 --> Parse3[Parser for Score Input]
    Parse3 --> Agent4[Agent 4: ATS Scoring]
    Agent4 --> ScoreOut[Output Score]

🔗 Compatibility

✅ Works with:

• Claude Desktop (as MCP Client)
• Any custom MCP-compatible chat client

📦 LangFlow Version: v1.4+

🧪 Example Use Case

Prompt:
“Please generate an tailored resume for [LinkedIn Job URL]”

Result:

• Tailored resume (PDF, DOCX, TXT)
• ATS match score out of 100
• Explanation of missing or weak areas

🚀 Get Started

1. Clone this repo
2. Import the .json into LangFlow
3. Connect with MCP client (e.g., Claude Desktop)
4. Start chatting!

🧩 Contributing

Have ideas for new agents or better format converters?
We welcome contributions via PR or feedback.

📄 License

MIT License
Copyright © 2025