Resume Optimizer

You can run and orchestrate tools for AI-powered resume optimization through a Model Context Protocol (MCP) server. The server exposes composable tools that Claude or other MCP clients can call, enabling a structured, testable workflow from data fetch to prioritization and optimization steps.

How to use

You use the MCP server by running it locally or remotely and connecting an MCP client such as Claude Desktop. Once running, the client discovers the available tools and orchestrates calls to fetch data, filter and prioritize information, and compose an optimized resume workflow. The client can invoke these tools in a sequence or in a multi-tool pipeline to achieve end-to-end resume optimization.

In practical terms, you set up your MCP client to point at the server, then issue requests to execute specific tools with your data. The server handles tool execution, data processing, and inter-tool communication, returning structured results that you can feed into subsequent steps or present to the user.

How to install

# Prerequisites
# - Node.js 18+
# - Anthropic API key for Claude access

# 1. Clone the project
# Replace with your actual repository path if needed
git clone https://github.com/YOUR_USERNAME/resume-optimizer-mcp.git
cd resume-optimizer-mcp

# 2. Install dependencies
npm install

# 3. Configure environment variables
cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY

# 4. Start the MCP server
node server.js
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Additional notes

The server exposes a stdio-based MCP runtime for local execution. A typical runtime command looks like running the server with Node, pointing to the server entry point. You can configure your MCP client to start the server process as shown in the example configuration.

Configuration for Claude Desktop

{
  "mcpServers": {
    "resume_optimizer": {
      "command": "node",
      "args": ["/path/to/resume-optimizer-mcp/server.js"]
    }
  }
}

Current features and tools

The server implements three core tools that you can call from a Claude-like MCP client: fetch-data, heap-filter, and heap. Each tool is designed to be composable and testable in isolation, allowing you to build custom resume-optimization workflows by combining them as needed.

Security and maintenance

Manage your API keys and sensitive data securely. Use environment variables for secrets, rotate keys periodically, and monitor access to the MCP server. Keep dependencies up to date to mitigate known vulnerabilities, and add unit tests for each tool to ensure reliable behavior as you extend the workflow.

Troubleshooting

• Tool discovery failures indicate the MCP client cannot reach the server; verify the server is running and the client is configured with the correct path to server.js
• MCP tool calls failing may require checking tool inputs and ensuring required data is provided
• Environment variable errors typically relate to missing API keys; re-check .env and restart the server

Available tools

fetch-data

Fetches and processes resume and job description data for subsequent analysis.

heap-filter

Filters and prioritizes information using a heap data structure to surface top items.

heap

Core heap implementation used to manage priority-based processing within tool sequences.