https://github.com/vinayaks439/langflow-mcp-high-ats-resume-creator
langflow mcp example for creating high ats friendly resume
https://github.com/vinayaks439/langflow-mcp-high-ats-resume-creator
ats generator high langflow mcp resume
Last synced: 12 days ago
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langflow mcp example for creating high ats friendly resume
- Host: GitHub
- URL: https://github.com/vinayaks439/langflow-mcp-high-ats-resume-creator
- Owner: Vinayaks439
- Created: 2025-05-28T14:07:40.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-28T17:30:32.000Z (5 months ago)
- Last Synced: 2025-06-23T09:28:44.288Z (4 months ago)
- Topics: ats, generator, high, langflow, mcp, resume
- Homepage:
- Size: 111 KB
- Stars: 2
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🧠 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
```mermaid
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
---