mcp-overleaf-server

An MCP server that tailors your LaTeX résumé, CV, and cover letter to any job description — injecting truthfully-selected content from a master cv.md into your own LaTeX document, compiling the PDF, and logging the application automatically. Paste a job description into any MCP client (Claude Desktop, Cursor, VS Code, Windsurf) and get back an ATS-optimized, one-page PDF.

Truthfulness is enforced by code, not just prompts. The server refuses to compile any bullet that can't be traced back to your master CV, and flags any new number or skill for review.

How it works

The reasoning (analyze the JD, rank experience, rewrite bullets) is done by your MCP client's model — no API key, no extra LLM cost. The server is a set of deterministic tools plus orchestrating prompts:

/tailor_resume "<paste JD>"
   │
   ├─ prepare_tailoring(JD)    → master CV (with ids) + JD keyword signals + rules + schema
   │      … your model reasons here → emits TailoredContent JSON …
   │         (only reorders/rewrites/shortens existing bullets; cites source ids)
   └─ render_and_compile(json + coverLetter)
          inject → validate → anti-fabrication check → compile
          → Company_Position.pdf + Company_Position_CoverLetter.pdf
          → auto-logged to output/applications.csv

Related MCP server: CV Resume Builder MCP

Requirements

• Node.js ≥ 20 (developed on 26)

• A LaTeX toolchain — latexmk + pdflatex (install MacTeX / TeX Live). Check with latexmk --version.

• An Overleaf git token — optional, only for the Overleaf sync/push tools. Overleaf git access is a paid feature; everything else works without it.

Setup

npm install && cp .env.example .env && npm run build

Put your master CV at cv.md (see cv.example.md for the structure — ## Summary, ## Experience, ## Projects, ## Education, ## Skills), and drop your polished résumé at templates/main.tex. Both are gitignored — they never get published.

.env:

OL_GIT_AUTHENTICATION_TOKEN=olp_xxxxxxxxxxxx    # optional
OVERLEAF_PROJECT_URL=https://www.overleaf.com/project/xxxx   # optional
CV_MASTER_PATH=./cv.md
CAREER_OUTPUT_DIR=./output
LATEX_ENGINE=latexmk

Connect to an MCP client

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "overleaf-resume": {
      "command": "node",
      "args": ["/ABSOLUTE/PATH/mcp-overleaf-server/dist/index.js"]
    }
  }
}

The server reads .env from its own directory, so secrets don't go in the config. Restart the client fully after editing. Cursor / VS Code / Windsurf use the same shape.

Usage

One job

In Claude Code, MCP prompts are namespaced — type /mcp to pick from the list, or:

/mcp__overleaf-resume__tailor_resume

with jobDescription="…paste the posting…", company="Acme", position="Backend Engineer".

In Claude Desktop, prompts appear under the ➕ button, not as typed slash commands. Or just ask in chat: "Tailor my resume for this job: <paste>".

This produces the résumé and a matching cover letter, then logs the application — all in one call.

Many jobs at once (up to 10)

/mcp__overleaf-resume__tailor_multiple_jobs

Pass jobs as either JSON or ----separated blocks:

Company: Nuro
Position: Software Engineer
<paste the JD>
---
Company: Waymo
Position: Backend Engineer
<paste the JD>

This is the credit-efficient path. batch_plan clusters the jobs by keyword similarity and checks a cross-session cache, then tells the model exactly which jobs need fresh reasoning. Ten backend roles cost one reasoning pass, not ten — the rest are rendered from reused content. batch_render then compiles, writes cover letters, and logs every job in a single call.

Batch plan — 5 job(s), 2 reasoning pass(es) needed, 3 saved by reuse.

  #0  Nuro — Software Engineer      [resume]  →  GENERATE  ← write content for this one
  #1  Waymo — Backend Engineer      [resume]  →  reuse job #0 (similarity 0.78)
  #2  Figma — Frontend Engineer     [resume]  →  GENERATE  ← write content for this one
  #3  Linear — Frontend Engineer    [resume]  →  reuse job #2 (similarity 0.81)
  #4  Stripe — Backend Engineer     [resume]  →  reuse cache "acme|backend engineer" (0.72)

Tune reuse aggressiveness with threshold (0–1, default 0.65) on batch_plan.

Tools

Your own LaTeX document

Point the server at templates/main.tex and it auto-detects which kind of file it is:

• A finished résumé (custom macros like \roleheading / \bul, no placeholders) → rewrites only the Summary / Experience / Projects / Skills section bodies. Your preamble, fonts, colors, spacing, and Education section are preserved byte-for-byte.

• A {{PLACEHOLDER}} template → the placeholder renderer fills each token.

Project links

Write a project heading in cv.md as a markdown link and it stays hyperlinked in the compiled PDF:

**[CarbonProxy](https://devpost.com/software/carbonproxy)** - Python, FastAPI, SQLite

→ \projheading{\textbf{\href{...}{CarbonProxy}} $|$ \emph{...}}{Feb 2026}

The tracker sheet

output/applications.csv opens in Excel / Google Sheets and is git-diffable. Columns: Date Applied · Company · Position · Job Link · ATS % · Resume File · Git Link · JD Summary · Status · Notes.

Every render auto-logs, upserting on company+position — regenerating updates the row instead of duplicating.

Architecture & design decisions

• TypeScript, not Python. The whole system is I/O orchestration (git, fs, subprocess, template injection). Building on the mature MCP TypeScript SDK avoided a rewrite and a second toolchain.

• Host-model reasoning, not a server-side LLM. The MCP client already has a capable model; calling an API server-side would need a key, double the cost, and duplicate the model. The server hands the model a structured brief and renders the JSON it returns. No key required.

• Template injection, never LLM-generated LaTeX. The renderer is the only component that emits LaTeX, and it escapes every field. Your macros and typography survive exactly.

• Anti-fabrication by construction. Every source bullet gets a stable id (EXP1.2). The model must cite one per bullet; unknown ids are a hard failure, and new numbers / off-CV skills become warnings. Cover letters get a prose-tuned variant that flags unverifiable figures. See src/core/schema.ts.

• Reuse over regeneration. Deterministic Jaccard similarity over JD keywords decides what can be reused — no model call is needed to decide what to skip. See src/core/batch.ts.

• Graceful Overleaf degradation. No git access (a paid feature) returns a normal "unavailable" result, not an error, and the failure is cached so the token is never re-spent.

• CSV tracker, not a database. Excel-openable, git-friendly, zero infrastructure.

Project structure

src/
  index.ts              MCP bootstrap (tools + prompts, stdio)
  config.ts             env + resolved paths
  tools.ts              the MCP tools
  prompts.ts            tailor_resume + tailor_multiple_jobs
  core/
    cvParser.ts         cv.md → structured model with stable ids
    schema.ts           TailoredContent + CoverLetterContent (zod) + provenance checks
    latexRenderer.ts    structured content → LaTeX (escaping, macros, \href)
    documentInjector.ts rewrites section bodies of YOUR finished .tex
    latexValidate.ts    pre-compile structural checks
    latexCompile.ts     latexmk / pdflatex → PDF
    coverLetter.ts      one-page letter rendering
    batch.ts            similarity, clustering, cross-session cache, batch runner
    atsNormalize.ts     Unicode → ATS-safe ASCII
    keywords.ts         JD keyword extraction, coverage, gap analysis
    tracker.ts          CSV application sheet
    overleafGit.ts      project-URL → authed git; clone/pull/branch/commit/push
    brief.ts            the tailoring brief prepare_tailoring returns
    pipeline.ts         compose layer (render→validate→compile→save→log)
templates/              main.tex (yours, gitignored) · cv-template.tex · resume-template.tex
                        cover-letter-template.tex · AUTHORING.md
test/                   39 unit + integration tests (incl. real PDF compiles)

Development

npm test         # 39 tests, including real latexmk compiles
npm run typecheck
npm run build

Roadmap

Embedding-based bullet ranking · multiple role templates (SWE/AI-ML/Security/Quant) · preserving bold metrics through injection · optional server-side provider for a fully autonomous single call.

License

MIT. Résumé template derived from the sb2nov / Gabriel Sison template (MIT).