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Hiring Tool MCP Server Streamable HTTP
MCP server: Hiring Tool MCP Server Streamable HTTP
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π Resume Ranking MCP Server (FastMCP + Streamable HTTP)
A production-ready Model Context Protocol (MCP) server for AI-powered resume analysis and ranking. This server uses FastMCP and Streamable HTTP transport with Azure OpenAI models to provide scalable, modular, and intelligent resume evaluation APIs.
π¦ Project Structure
mcp-http-server/
βββ run_server.py
βββ server_config.py
βββ requirements.txt
βββ .env (you create this)
βββ src/
β βββ mcp/
β β βββ http_server.py
β β βββ handlers.py
β β βββ tools.py
β β βββ error_handler.py
β βββ core/
β β βββ analyzers.py
β β βββ parsers.py
β β βββ basic_info.py
β β βββ inflation.py
β βββ utils/
β β βββ config_manager.py
βββ demo_data/
β βββ jd/
β βββ single_res/
β βββ bulk_res/
βββ demo_mcp/
β βββ demo_script.py
β βββ requirements.txt
β Prerequisites
- Python 3.10 to 3.13
- An Azure OpenAI resource with:
- A deployed Chat/Completions model (e.g., GPT-4)
- A deployed Embeddings model (e.g.,
text-embedding-3-small)
- Optional: Git, Docker, Node.js (for advanced integrations)
βοΈ Local Setup Instructions
1. Clone the Repository
git clone https://github.com/your-username/mcp-http-server.git
cd mcp-http-server
2. Create a Virtual Environment
python -m venv env
source env/bin/activate # macOS/Linux
env\Scripts\activate # Windows
3. Install Server Dependencies
pip install -r requirements.txt
4. Set Up Environment Variables
Create a .env file in the root folder with content like:
AZURE_OPENAI_API_KEY="your_api_key"
AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
AZURE_OPENAI_MODEL="gpt-4"
AZURE_OPENAI_API_VERSION="2024-12-01-preview"
AZURE_EMBEDDING_MODEL="text-embedding-3-small"
MCP_SERVER_HOST=0.0.0.0
MCP_SERVER_PORT=8000
MAX_CONCURRENT_REQUESTS=10
ENABLE_PARALLEL_PROCESSING=true
MAX_WORKERS=4
SEMANTIC_WEIGHT=0.4
SKILL_WEIGHT=0.4
EXPERIENCE_WEIGHT=0.2
LOG_LEVEL=INFO
π Running the Server
python run_server.py
- The MCP endpoint will be available at:
http://localhost:8000/mcp
π§ͺ Run Demo Client
1. Setup Demo Environment
cd demo_mcp
python -m venv demo_env
source demo_env/bin/activate # macOS/Linux
demo_env\Scripts\activate # Windows
pip install -r requirements.txt
2. Run the Demo Script
python demo_script.py
- Tests health, connectivity, and performs sample analysis.
- Make sure the server is already running in another terminal.
π οΈ MCP Tools (APIs)
health_check()β Server status & config checkwebsite_faq(question)β Returns company info/FAQanalyze_single_resume(jd_path, resume_path, skill_threshold)analyze_resumes_bulk(jd_path, resume_folder, skill_threshold)calculate_semantic_similarity_bulk(jd_path, resume_folder)
Testable via demo_script or any MCP-compatible client.
π§ Core Modules
parsers.py: Converts PDF/DOCX/TXT to clean textanalyzers.py: Azure OpenAI powered skill extraction and scoringbasic_info.py: Email, phone, LinkedIn extractioninflation.py: Adjusts composite scoresconfig_manager.py: Loads.envand handles all configuration
π Security Best Practices
- Do NOT commit
.envto version control - Use Key Vault or secret manager in production
- Clean up old resume files regularly
- Limit file size and validate extensions
π€ Deployment Recommendations
- Docker / Kubernetes / Azure App Service / Cloud Run are supported.
- Use API gateway for authentication and rate-limiting.
- Deploy MCP at
/mcpendpoint.
