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., "@MCP Job Matching Server calculate my match score for alpic-fullstack with skills TypeScript, React, Node.js"
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.
A lightweight Model Context Protocol (MCP) server built with TypeScript and Node.js that helps AI agents evaluate candidate-job fit by calculating weighted match scores.
Built using the official @modelcontextprotocol/sdk.
The Model Context Protocol is an open standard that allows AI assistants (like Claude, Gemini, etc.) to interact with external tools and data sources through a unified interface. MCP servers expose tools that AI agents can discover and invoke autonomously.
This server demonstrates how to build a production-style MCP server that could power recruitment workflows in the agentic web.
Related MCP server: Recruitment AI MCP
Tool | Description |
| Returns a list of job postings with required and preferred skills (simulates a database query) |
| Compares a candidate's skills against a job's requirements and returns a weighted score with detailed breakdown |
The scoring engine uses a weighted formula:
Overall Score = (Required Skills Match × 0.70) + (Preferred Skills Match × 0.30)The response includes:
Overall match percentage
Required vs preferred skills breakdown
Matched and missed skills lists
Hiring recommendation (STRONG FIT, GOOD FIT, or STRETCH / LOW FIT)
Runtime: Node.js (ES2022)
Language: TypeScript (strict mode)
Protocol: MCP over Stdio transport (JSON-RPC 2.0)
SDK: @modelcontextprotocol/sdk
Node.js v18+ installed
npm
git clone https://github.com/nourelhoudaas/mcp-job-matching-server.git
cd mcp-job-matching-server
npm installnpm run buildA test script is included that spawns the server, sends a tools/call request via JSON-RPC, and prints the match score result:
node test-client.jsExpected output:
Starting MCP server for testing...
Server stderr log: Job Match MCP Server running on Stdio transport
Sending tools/call request to server stdin...
Received from server stdout: { ... overallMatchScore: "53%" ... }
Success: MCP Server successfully executed calculate_match_score!Add this to your claude_desktop_config.json:
{
"mcpServers": {
"job-matcher": {
"command": "node",
"args": ["/absolute/path/to/mcp-job-matching-server/build/index.js"]
}
}
}Then ask Claude: "Use the job-matcher tool to calculate my match score for the alpic-fullstack job with skills: TypeScript, React, Node.js"
mcp-job-matching-server/
├── src/
│ └── index.ts # MCP server implementation
├── build/ # Compiled JS (generated by tsc)
├── test-client.js # Automated verification script
├── package.json
├── tsconfig.json
└── README.mdRequest (JSON-RPC 2.0 via stdin):
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "calculate_match_score",
"arguments": {
"candidateSkills": ["TypeScript", "React", "Node.js", "SQL", "REST APIs"],
"jobId": "alpic-fullstack"
}
}
}Response (via stdout):
{
"jobId": "alpic-fullstack",
"company": "Alpic",
"title": "Full-Stack Software Engineer",
"overallMatchScore": "53%",
"breakdown": {
"requiredSkillsMatch": "3/4",
"preferredSkillsMatch": "0/4"
},
"matchedRequired": ["TypeScript", "React", "Node.js"],
"missedRequired": ["English communication"],
"matchedPreferred": [],
"missedPreferred": ["AWS CDK", "NestJS", "MCP", "Developer tools"],
"recommendation": "STRETCH / LOW FIT - Tailor carefully"
}Calculate a detailed match score between a candidate's skills and a job description.
Inputs: candidateSkills (required), jobId (required).
Retrieve list of mock job postings including requirements and metadata.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 2 tools
The two tools have clearly distinct purposes: one calculates a match score, the other retrieves mock job postings. No overlap or ambiguity.
Both tools follow a consistent verb_noun pattern with underscores, making the naming predictable and clear.
With only 2 tools, the server feels minimal. While it may cover a narrow scope, typical job matching servers would require more tools, so the count is borderline.
The tool set is severely incomplete for a job matching domain. It lacks essential operations like searching jobs, managing candidate profiles, or submitting applications, leaving agents with limited functionality.
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