PROTEUS MCP Server

npm install -g proteus-mcp

const result = await client.callTool({
  name: "extract_jd_requirements",
  arguments: {
    jd_text: `
      Google — Senior Software Engineer, Cloud Platform

      Requirements:
      - 5+ years of experience in distributed systems
      - Strong proficiency in Go or Python
      - Experience with Kubernetes, Terraform, and CI/CD pipelines
      - Familiarity with gRPC and microservices architecture
      - Excellent communication and leadership skills
    `
  }
});

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

const result = await client.callTool({
  name: "extract_resume_signals",
  arguments: {
    resume_text: `
      Jane Smith
      jane@email.com | (555) 123-4567 | San Francisco, CA

      EXPERIENCE
      Senior Software Engineer | Meta | 2021-Present
      - Led migration of 200+ microservices from ECS to Kubernetes
      - Built real-time monitoring dashboards using Prometheus and Grafana
      - Reduced mean-time-to-detection by 40% through observability improvements

      EDUCATION
      MS Computer Science | Stanford University | 2019
      BS Computer Science | UC Berkeley | 2017
    `
  }
});

{
  "name": "Jane Smith",
  "email": "jane@email.com",
  "skills": ["Go", "Python", "Kubernetes", "Prometheus", "Grafana", "ECS"],
  "experience": [
    {
      "role": "Senior Software Engineer",
      "company": "Meta",
      "bullets": [
        "Led migration of 200+ microservices from ECS to Kubernetes",
        "Built real-time monitoring dashboards using Prometheus and Grafana",
        "Reduced mean-time-to-detection by 40% through observability improvements"
      ]
    }
  ],
  "education": [
    { "degree": "MS Computer Science", "institution": "Stanford University" },
    { "degree": "BS Computer Science", "institution": "UC Berkeley" }
  ],
  "certifications": []
}

const result = await client.callTool({
  name: "match_resume_to_jd",
  arguments: {
    jd_text: "Google — Senior Software Engineer... (full JD text)",
    resume_text: "Jane Smith\njane@email.com... (full resume text)"
  }
});

{
  "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"
      },
      {
        "requirement": "Terraform",
        "status": "partial",
        "score": 0.6,
        "evidence": "Used IaC tools but no direct Terraform mention",
        "category": "hard_skill"
      },
      {
        "requirement": "gRPC",
        "status": "missing",
        "score": 0.0,
        "evidence": null,
        "category": "hard_skill"
      }
    ]
  },
  "timings": {
    "parse": "4.7s",
    "gap_analysis": "1.9s",
    "aggregate": "0.0s",
    "total": "6.6s"
  }
}

const result = await client.callTool({
  name: "match_resume_to_jd_full",
  arguments: {
    jd_text: "Google — Senior Software Engineer... (full JD text)",
    resume_text: "Jane Smith\njane@email.com... (full resume text)",
    cover_letter_tone: "professional"
  }
});

{
  "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 in the Senior Software Engineer position at Google...",
    "tone": "professional",
    "word_count": 342,
    "key_points_addressed": ["Kubernetes", "distributed systems", "observability"]
  }
}

const jd = await client.callTool({
  name: "extract_jd_requirements",
  arguments: { jd_text: "..." }
});

const resume = await client.callTool({
  name: "extract_resume_signals",
  arguments: { resume_text: "..." }
});

const score = await client.callTool({
  name: "score_match",
  arguments: {
    jd_requirements: jd.content,
    resume_signals: resume.content
  }
});

const gaps = await client.callTool({
  name: "generate_gap_report",
  arguments: {
    jd_requirements: jd.content,
    resume_signals: resume.content
  }
});

npm install -g proteus-mcp

# Start MCP server (stdio transport — used by Claude Desktop / Claude Code)
proteus-mcp

# Or with inline env vars
NVIDIA_NIM_API_KEY=nvapi-xxx GROQ_API_KEY=gsk-xxx proteus-mcp

{
  "mcpServers": {
    "proteus": {
      "command": "proteus-mcp",
      "env": {
        "NVIDIA_NIM_API_KEY": "nvapi-your-key",
        "GROQ_API_KEY": "gsk-your-key"
      }
    }
  }
}

{
  "mcpServers": {
    "proteus": {
      "command": "proteus-mcp",
      "env": {
        "NVIDIA_NIM_API_KEY": "nvapi-your-key",
        "GROQ_API_KEY": "gsk-your-key"
      }
    }
  }
}

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

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

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