Claude MCP Job Assistant
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Claude MCP Job Assistant recommend jobs based on my resume"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Claude MCP Job Assistant
Overview
Claude MCP Job Assistant is a production-ready MCP (Model Context Protocol) server designed to demonstrate how Tools, Resources, and Prompts can be orchestrated to build an intelligent job search assistant.
The project uses Claude Desktop as the MCP Host/Client and exposes a complete ecosystem for:
Searching job opportunities.
Saving interesting positions.
Performing labor market analysis.
Receiving personalized recommendations based on a resume.
Generating matching reports between a resume and saved jobs.
This project provides a practical introduction to the Model Context Protocol and illustrates how modern AI assistants can leverage MCP capabilities to deliver contextual and personalized experiences.
Related MCP server: jobjourney-claude-plugin
Features
MCP-compliant server implementation.
Integration with Claude Desktop.
Intelligent job search.
Job bookmarking and persistence.
Resume-based recommendations.
Labor market analysis.
Matching reports generation.
Modular architecture using MCP Tools, Resources, and Prompts.
Production-ready setup with
uv.
Architecture
The MCP server is organized around the three core MCP primitives:
Tools
Tool | Description |
| Retrieves job offers using an external API. |
| Saves selected jobs in a structured format. |
Resources
Resource | Description |
| Loads the user's resume. |
| Loads previously saved jobs. |
Prompts
Prompt | Description |
| Analyzes labor market trends. |
| Suggests jobs, skills, and companies based on the user's resume. |
| Generates a report comparing saved jobs with the user's resume. |
Workflow
User
↓
Claude Desktop (MCP Host)
↓
MCP Job Assistant Server
├── Tools
│ ├── search_jobs()
│ └── save_job()
│
├── Resources
│ ├── resume://default
│ └── jobs://saved
│
└── Prompts
├── analyze_job_market()
├── personalized_job_recommender()
└── create_match_report()
↓
Generated ResponseTechnologies Used
Python
Model Context Protocol (MCP)
Claude Desktop
UV
External Job APIs
PDF Processing
Installation
1. Clone the Repository
git clone https://github.com/eric623/Claude-MCP-Job-Assistant.git
cd Claude-MCP-Job-Assistant2. Install Dependencies
This project uses uv for dependency management.
uv syncAt this stage, the MCP server is ready to be launched by Claude Desktop.
Claude Desktop Configuration
Step 1: Install Claude Desktop
Download and install Claude Desktop from Anthropic.
Step 2: Open Developer Settings
Navigate to:
Settings → Developer → Edit ConfigThis opens the claude_desktop_config.json file.
Step 3: Add the MCP Server
Add the following configuration:
{
"mcpServers": {
"mcp_job": {
"command": "uv",
"args": [
"--directory",
"PATH_TO_PROJECT_DIRECTORY",
"run",
"server.py"
]
}
}
}Replace
PATH_TO_PROJECT_DIRECTORYwith the absolute path to your local project folder.
Adding Your Resume
Place your CV inside the resume directory.
The file must be named exactly:
resume.pdfExample:
Claude-MCP-Job-Assistant/
│
└── resume/
└── resume.pdfImportant: Resume-based recommendations and matching reports require this file.
Launching the MCP Server
After saving the configuration:
Completely close Claude Desktop.
Reopen Claude Desktop.
You should now see:
mcp_job (Running)in the Developer panel.
MCP Concepts Demonstrated
MCP Tools
MCP Resources
MCP Prompts
Claude Desktop Integration
Context-Aware AI Systems
Intelligent Job Search
External API Consumption
Personalized Recommendations
Modular MCP Server Design
Why This Project?
This project was built to explore and demonstrate the capabilities of the Model Context Protocol (MCP) by implementing a realistic use case centered around job exploration and career assistance.
It highlights how MCP enables:
Context-aware assistants.
Modular architectures.
Tool orchestration.
Resource management.
Prompt engineering.
Intelligent interactions between users and AI systems.
Author
AKAKPO Koffi Moïse
Interested in Agentic AI, Multi-Agent Systems, and Applied Artificial Intelligence.
Claude MCP Job Assistant demonstrates how the Model Context Protocol can be leveraged to build intelligent, modular, and extensible assistants through the coordinated use of Tools, Resources, and Prompts.
Available Tools
2 toolssave_jobA
Save a specific job from temporary list into candidate's saved folder.
If salary not provided, extract it from the fetched job data.
Args: job_id: The ID of the job to save. salary (optional): The salary of the job to save.
Returns: A string indicating the job was saved successfully.
Inputs: job_id (required), salary. Returns: result
search_jobsC
Fetch jobs using JSearch API and store them temporarily. Return key info.
Args: role: The role to search for. location: The location to search for. max_results: The maximum number of jobs to return.
Returns: A list of dictionaries containing the job information.
Inputs: role (required), location (required), max_results. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: search_jobs fetches and lists jobs, while save_job persists a specific job. There is no overlap in their functionality, making misselection unlikely.
Both tools follow the verb_noun pattern (search_jobs, save_job) using lowercase snake_case. The consistency is good, though with only two tools there is limited evidence of a broader pattern.
Two tools is quite thin for a job assistant server that presumably requires browsing and managing jobs. Missing operations needed for a complete workflow means the count feels inadequate for the domain.
The surface has notable gaps: there's no way to list or retrieve saved jobs, no delete/discard capability, no detailed view of a job, and no pagination beyond max_results. Agents could reach dead ends when trying to manage or review saved jobs.
Maintenance
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