PROTEUS MCP Server

<p align="center"> <h1 align="center">PROTEUS MCP Server</h1> <p align="center"> <em>JD-aware resume matching as an MCP tool — deterministic scoring, gap analysis, rewrites, and cover letters from a single call.</em> </p> </p>

<p align="center"> <a href="https://github.com/DanielDeshmukh/proteus-mcp/actions/workflows/ci.yml"> <img src="https://github.com/DanielDeshmukh/proteus-mcp/actions/workflows/ci.yml/badge.svg" alt="CI"> </a> <a href="https://github.com/DanielDeshmukh/proteus-mcp/blob/main/LICENSE"> <img src="https://img.shields.io/github/license/DanielDeshmukh/proteus-mcp?style=flat-square&color=blue" alt="License"> </a> <img src="https://img.shields.io/badge/TypeScript-5.x-3178c6?style=flat-square&logo=typescript&logoColor=white" alt="TypeScript"> <img src="https://img.shields.io/badge/MCP-2024--11--05-8b5cf6?style=flat-square&logo=modelcontextprotocol&logoColor=white" alt="MCP Protocol"> <img src="https://img.shields.io/badge/Node-18%2B-339933?style=flat-square&logo=node.js&logoColor=white" alt="Node.js"> <a href="https://github.com/DanielDeshmukh/proteus"> <img src="https://img.shields.io/badge/PROTEUS-pipeline-76b900?style=flat-square" alt="PROTEUS Pipeline"> </a> <img src="https://img.shields.io/badge/NVIDIA%20NIM-AI%20backend-76b900?style=flat-square&logo=nvidia&logoColor=white" alt="NVIDIA NIM"> </p>

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What It Does

PROTEUS MCP wraps a 5-agent resume-matching pipeline as 6 discrete MCP tools. Paste a job description and resume into Claude Desktop / Claude Code / OpenCode — get a deterministic match score, gap analysis, bullet rewrites, and a tailored cover letter.

No vector DB. No black-box scoring. No hosted service. Just deterministic math over embeddings, exposed as protocol-level tools you can explain in an interview.

Why MCP?

MCP (Model Context Protocol) is the open standard for connecting AI assistants to external tools. This server proves you understand the protocol — stdio transport, JSON-RPC tool schemas, discrete tool boundaries — not just "I called an LLM API."

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Tools

Tool	Input	Output	Latency
extract_jd_requirements	Raw JD text	Structured requirements (skills, seniority, keywords)	~3s
extract_resume_signals	Raw resume text	Structured candidate data (skills, experience, education)	~5s
score_match	Parsed JD + resume	Overall score + category breakdown	~2s
generate_gap_report	Parsed JD + resume	Matched / partial / missing requirements	~2s
match_resume_to_jd	Raw JD + resume text	Fast path — score + gaps	4-10s
match_resume_to_jd_full	Raw JD + resume text	Full pipeline + rewrites + cover letter	~90s

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Quick Start

Prerequisites

• Node.js 18+
• NVIDIA NIM API key — Get one here (free tier available)
• Groq API key — Get one here (free tier available)

Install

git clone https://github.com/DanielDeshmukh/proteus-mcp.git
cd proteus-mcp
npm install

Configure Environment

export NVIDIA_NIM_API_KEY=nvapi-your-key
export GROQ_API_KEY=gsk-your-key

Run

npm run dev    # development (tsx hot-reload)
npm run build  # production build
npm start      # production run

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MCP Client Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "proteus": {
      "command": "node",
      "args": ["--import", "tsx", "/absolute/path/to/proteus-mcp/src/server.ts"],
      "env": {
        "NVIDIA_NIM_API_KEY": "nvapi-your-key",
        "GROQ_API_KEY": "gsk-your-key"
      }
    }
  }
}

Claude Code / OpenCode

{
  "mcpServers": {
    "proteus": {
      "command": "node",
      "args": ["--import", "tsx", "/absolute/path/to/proteus-mcp/src/server.ts"],
      "env": {
        "NVIDIA_NIM_API_KEY": "nvapi-your-key",
        "GROQ_API_KEY": "gsk-your-key"
      }
    }
  }
}

---

Tool Schemas

<details> <summary><strong>extract_jd_requirements</strong></summary>

Input:

{ "jd_text": "Google — Senior Software Engineer..." }

Output:

{
  "title": "Senior Software Engineer, Cloud Platform",
  "company": "Google",
  "seniority_level": "senior",
  "hard_skills": ["Go", "Python", "Kubernetes", ...],
  "soft_skills": ["leadership", "communication", ...],
  "domain_keywords": ["distributed systems", "cloud infrastructure", ...],
  "ats_bait": ["Kubernetes", "Terraform", "gRPC", ...],
  "requirements_summary": "5+ years experience in distributed systems..."
}

</details>

<details> <summary><strong>extract_resume_signals</strong></summary>

Input:

{ "resume_text": "Jane Smith\njane@email.com..." }

Output:

{
  "name": "Jane Smith",
  "skills": ["Python", "Go", "Kubernetes", ...],
  "experience": [{ "role": "Senior SWE", "company": "Meta", "bullets": [...] }],
  "projects": [...],
  "education": [{ "degree": "MS CS", "institution": "Stanford" }],
  "certifications": [...]
}

</details>

<details> <summary><strong>match_resume_to_jd</strong> (fast path)</summary>

Input:

{
  "jd_text": "Google — Senior Software Engineer...",
  "resume_text": "Jane Smith\njane@email.com..."
}

Output:

{
  "overall_score": 0.7966,
  "section_scores": {
    "hard_skills": 0.6571,
    "soft_skills": 1.0,
    "domain_keywords": 0.84,
    "ats_bait": 1.0
  },
  "gap_analysis": {
    "matched": 11,
    "partial": 4,
    "missing": 4,
    "total": 19,
    "gaps": [
      {
        "requirement": "Kubernetes",
        "status": "matched",
        "score": 0.95,
        "evidence": "Led migration of 200+ microservices from ECS to Kubernetes",
        "category": "hard_skill"
      }
    ]
  },
  "timings": { "parse": "4.7s", "gap_analysis": "1.9s", "aggregate": "0.0s", "total": "6.6s" }
}

</details>

<details> <summary><strong>match_resume_to_jd_full</strong></summary>

Input:

{
  "jd_text": "Google — Senior Software Engineer...",
  "resume_text": "Jane Smith\njane@email.com...",
  "cover_letter_tone": "professional"
}

Output: Everything from match_resume_to_jd plus:

{
  "rewrite_suggestions": {
    "suggestions": [
      {
        "original": "Built monitoring dashboards",
        "rewrite": "Built real-time monitoring dashboards using Prometheus and Grafana, reducing mean-time-to-detection by 40%",
        "rationale": "Added specific tools from JD and quantified impact",
        "target": "Experience with observability (Prometheus, Grafana)",
        "impact": 0.85
      }
    ],
    "hidden_experience": ["Distributed tracing with OpenTelemetry"]
  },
  "cover_letter": {
    "job_title": "Senior Software Engineer",
    "full_letter": "Dear Hiring Manager,\n\nI am writing to express my interest...",
    "tone": "professional",
    "word_count": 342,
    "key_points_addressed": ["Kubernetes", "distributed systems", "observability"]
  }
}

</details>

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Determinism

Component	Deterministic?	Why
aggregateScores	Yes	Pure math — weighted category scoring, no LLM
analyzeGaps (embeddings)	Yes	Cosine similarity — no temperature, no sampling
parseJd	Near-yes	Temperature pinned to 0; verified identical JSON on repeat
parseResume	Near-yes	Temperature pinned to 0; verified identical JSON on repeat
suggestRewrites	No	Temperature 0.3, creative generation
generateCoverLetter	No	Temperature 0.4, creative generation

The fast-path pipeline (match_resume_to_jd) is effectively deterministic — identical inputs produce identical scores and gap counts across repeated runs.

Scoring Formula

overall = hard_skills(50%) + domain_keywords(20%) + soft_skills(15%) + ats_bait(15%)

category_score = (matched * 1.0 + partial * 0.6) / total

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Latency

Measured with real JD + resume pairs (Google Cloud SRE role vs. 7-year backend engineer):

Stage	Cold Start	Warm
Parse JD + Resume (parallel)	4.7s	2-3s
Gap Analysis	1.9s	1-2s
Aggregate (pure math)	0.0s	0.0s
Total (fast path)	6.6s	4-5s
Rewrite + Cover Letter	+20-40s	+15-30s
Total (full pipeline)	~90s	~60s

---

Architecture

proteus-mcp/
├── src/
│   ├── server.ts                    # MCP server entrypoint, tool registration
│   ├── test.ts                      # End-to-end integration test
│   └── tools/
│       ├── extractJdRequirements.ts # wraps parseJd()
│       ├── extractResumeSignals.ts  # wraps parseResume()
│       ├── scoreMatch.ts            # wraps analyzeGaps() + aggregateScores()
│       ├── generateGapReport.ts     # wraps analyzeGaps()
│       ├── matchResumeToJd.ts       # fast path: parse → gap → aggregate
│       └── matchResumeToJdFull.ts   # full pipeline with rewrites + cover letter
├── .github/workflows/ci.yml        # CI: build, lint, typecheck, test, security
├── models.json                      # PROTEUS model configuration
├── package.json
└── tsconfig.json

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CI/CD

GitHub Actions runs on every push and PR:

Job	What it does
Build & Typecheck	tsc --noEmit + tsc across Node 18/20/22
Lint	ESLint with TypeScript rules
Test	MCP server startup verification across Node 18/20/22
Security Audit	npm audit --audit-level=high
Secret Scan	Scans source for hardcoded API keys

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Privacy

• No persistence — resume/JD text never written to disk or logs
• No auth — local-only, single-user, no multi-tenant overhead
• No vector DB — on-the-fly embedding comparison, not stored
• No remote transport — stdio only, no SSE/HTTP exposure
• Calls pipeline functions directly — bypasses Next.js API routes and database

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Topics

mcp model-context-protocol resume-matching jd-analysis resume-parser career-tools nvidia-nim embeddings cosine-similarity deterministic-scoring ai-tools llm typescript claude-desktop claude-code opencode

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Related Projects

• PROTEUS — The full JD-aware resume matching pipeline with web UI, auth, and history
• MCP SDK — Official TypeScript SDK for Model Context Protocol

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License

MIT

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<p align="center"> Built by <a href="https://github.com/DanielDeshmukh">Daniel Deshmukh</a> · Mumbai, India </p>