ATS Resume Review — MCP Server

An MCP (Model Context Protocol) server for AI-powered resume review and ATS (Applicant Tracking System) scoring, built as a Cloudflare Worker. Designed for LLM-only access — there is no public web UI, no human-facing page, and no accounts. An AI agent (Claude, or any MCP-compatible client) connects, uploads a resume, and receives a structured ATS score and improvement suggestions.

Tags: mcp-server model-context-protocol ats-score resume-parser resume-analyzer applicant-tracking-system cloudflare-workers typescript llm-tools ai-agent-tools groq resume-checker job-application-tools

What it does

A single MCP tool, analyze_resume(resume_base64, mime_type?, target_role?, target_location?):

1. Parses the uploaded resume — PDF (via unpdf), DOCX (via mammoth), or plain text — capped at 2MB.

2. Scores it deterministically (src/score.ts): ATS parseability (tables, columns, icon-font glyphs that break real parsers), standard section coverage (experience/education/skills/summary), contact-info detectability, and keyword overlap against the target role. No LLM in this half of the score, so it's reproducible and free to run.

3. Adds qualitative feedback via the Groq API (openai/gpt-oss-120b) — strengths, specific improvement suggestions, and a short summary. This call is fully decoupled from the score above; if it fails, the score still returns.

4. Persists the submission (parsed text, score breakdown, suggestions) to Cloudflare D1.

Related MCP server: ATS Scanner MCP Server

Why MCP, and why LLM-only

This isn't a SaaS product with a landing page — it's infrastructure meant to be discovered and called by AI agents, the same way a REST API is discovered and called by other software. MCP is the emerging standard for that: a typed tool contract an LLM client can introspect (tools/list) and invoke (tools/call) over a documented transport (Streamable HTTP), without scraping a UI or parsing HTML. Building this as an MCP server rather than a REST endpoint with a Swagger page is a deliberate bet on how AI agents will source capabilities going forward.

Architecture

MCP client (AI agent)
   │  POST /mcp  — JSON-RPC 2.0 over Streamable HTTP
   ▼
Cloudflare Worker (this repo)
   │  shared-secret auth → D1-backed rate limit (5 req/min/IP)
   ▼
parse (PDF/DOCX/text) → deterministic score → Groq qualitative pass → D1

Reached in production only through a same-origin Cloudflare Pages service binding on a separate site — no public workers.dev URL is exposed. That's a deployment detail, not a requirement of the code here: point any MCP-compatible transport at POST /mcp and it works.

Security & reliability engineering

Built as a tool that must survive untrusted third-party input read by an LLM — the resume itself is attacker-reachable content, not a trusted API payload.

• Prompt-injection resistant by construction. The resume text is framed strictly as data to evaluate, never as instructions, inside the Groq system prompt — wrapped in explicit delimiters, with an instruction to flag rather than obey anything that looks like a directive. Tool selection and control flow never depend on resume content; only one tool exists. Output is constrained to a zod-validated JSON schema, so even a successful injection can only emit text that still has to fit {summary, strengths[], suggestions[]}. A secondary heuristic pass (src/injection.ts) flags suspected attempts for audit visibility — defense in depth, not the primary defense.

• Rate limiting. D1-backed fixed-window counter per client IP, layered under zone-level Cloudflare rate-limiting rules.

• Shared-secret auth on top of the network-level access restriction (no public URL), so the tool is authenticated even if network exposure ever changes.

• Bounded input. 2MB file-size cap, 50,000-character cap on extracted text, 15-second timeout on the LLM call — all sized to make cost and abuse predictable regardless of what's uploaded.

• Deterministic core. The ATS score itself never depends on an LLM call succeeding, being available, or being non-deterministic — only the qualitative suggestions layer does.

Stack

Cloudflare Workers · TypeScript · @modelcontextprotocol/sdk (Streamable HTTP transport) · Cloudflare D1 · Groq API · zod · unpdf · mammoth

Local development

npm install
npx wrangler dev

Secrets (never committed):

npx wrangler secret put GROQ_API_KEY       # https://console.groq.com
npx wrangler secret put ATS_SHARED_SECRET  # any caller must present this
                                            # in X-ATS-Shared-Secret

Database

npx wrangler d1 create ats-db
npx wrangler d1 execute ats-db --remote --file=migrations/0001_ats_schema.sql

Two tables: ats_submissions (every analysis) and ats_rate_limits (fixed-window counters).

Deploy

npx wrangler deploy

Project layout

src/
  index.ts      Worker entrypoint — auth, rate limiting, MCP transport wiring
  mcp.ts        MCP server + the analyze_resume tool definition
  parse.ts      PDF/DOCX/text extraction
  score.ts      Deterministic ATS scoring
  groq.ts       Qualitative suggestions via Groq, injection-safe prompt framing
  injection.ts  Heuristic prompt-injection detection (audit flag, not a gate)
  db.ts         D1 writes
  ratelimit.ts  D1-backed fixed-window rate limiter
migrations/     D1 schema