cv-builder
Provides MCP tools for managing a CV as structured content, including a SQLite-backed snippet library with tools to list, create, update, and match job postings, compose role-tailored CV variants, and manage drafts.
README
CV Builder
A self-hosted tool for maintaining a CV as structured content instead of a single hand-formatted document: a browser editor for a data-driven HTML/CSS resume, a SQLite-backed snippet library for assembling role-tailored variants, and an MCP server so an LLM can help do the same thing from a chat client instead of the browser.
Ships with a fully synthetic example person in cv/web/data.yaml and
content/ — replace it with your own before you rely on this for real.
Nothing in this repo is anyone's real personal data.
Layout
cv/web/— HTML/CSS CV source (data.yaml,template.html.j2,style.css,editor.js) and the app UI (cv/web/src/: shared shell- design tokens in
shell/andtheme.css, one Jinja page + CSS/JS per app-chrome route underpages/)
- design tokens in
content/— optional additional detail for the snippet library, as markdown alongsidedata.yaml:work-experience/— one file per employer (category: experience, company taken from the filename)parts/— reusable blocks not tied to an employer, e.g. an alternate bio or a longer "detailed" variant of a strength (category: part)requirements/— pre-written answers to recurring posting requirements, formatch_job_posting/the builder's posting-matcher to surface (category: requirement)- See the example files under each for the heading convention: only the last heading before a block of prose becomes a snippet
src/cvbuilder/— snippet library (SQLite), importer, matcher, composer, and the MCP serverscripts/— CLI entry points (see below)data/— SQLite snippet database (snippets.db, gitignored, regenerable) and generated variants live undercv/variants/
Quickstart
pip install -r requirements.txt
# Seed the snippet database from cv/web/data.yaml + content/
PYTHONPATH=src python3 scripts/seed-snippets.py
# Run the editor / builder / variants UI
python3 scripts/serve-editor.py # http://127.0.0.1:5057/cv/web/edit
Requires google-chrome or chromium on PATH for PDF export
(CHROME_BIN env var to point at a specific binary).
Editing in the browser
scripts/serve-editor.py serves one app, sharing a common nav/header
shell (cv/web/src/shell/) across every page below except /cv/web/edit,
which renders the CV document itself:
/cv/web/— Home dashboard: live snippet/version counts and recent versions./cv/web/edit— click any text to edit it in place; hover controls add/reorder/delete list items (bullets, skills, jobs, subsections, education, custom side panels); Save & Preview renders a real PDF. Adding a skill/bio paragraph/education entry opens a picker fed from the snippet database, with search and duplicate flagging./cv/web/build("Tailor") — paste a job posting to re-rank snippets by keyword match, choose content, assemble an ordered draft, and compose it into a new named variant undercv/variants/<name>/./cv/web/library("Content library") — browse/search/filter every snippet, switch between its brief/standard/detailed variants, and create/edit/delete snippets or re-seed the database from source files./cv/web/variants("Versions") — preview, re-render, or delete composed variant folders./cv/web/assets— browse/upload photos and logos (backed by the/api/images*endpoints) and reference the built-in contact icons./cv/web/connect("Connect AI") — MCP setup instructions and an optional local connectivity check.
Docker
Runs the editor/builder UI and the MCP server in one container, with the repo bind-mounted so edits land on the host:
docker compose up --build
# home: http://127.0.0.1:5057/cv/web/
# editor: http://127.0.0.1:5057/cv/web/edit
# tailor: http://127.0.0.1:5057/cv/web/build
# library: http://127.0.0.1:5057/cv/web/library
# variants: http://127.0.0.1:5057/cv/web/variants
# assets: http://127.0.0.1:5057/cv/web/assets
# connect: http://127.0.0.1:5057/cv/web/connect
# MCP: http://127.0.0.1:8765/mcp (streamable-http)
Both ports publish to 127.0.0.1 only. Set ENABLE_MCP=0 in the compose
environment block to run the web UI without the MCP server.
Connecting an LLM (MCP server)
src/cvbuilder/mcp_server.py (run via scripts/mcp-server.py) exposes
the snippet library and composer as MCP
tools: list_snippets, get_snippet, create_snippet, update_snippet,
add_snippet_variant, delete_snippet, delete_snippet_variant,
match_job_posting, compose_cv, list_variants, list_drafts,
get_draft, save_draft, delete_draft, reseed_snippets.
Local subprocess (stdio) — the client spawns the server itself:
claude mcp add cv-builder -- python3 /absolute/path/to/cv-builder/scripts/mcp-server.py
{
"mcpServers": {
"cv-builder": {
"command": "python3",
"args": ["/absolute/path/to/cv-builder/scripts/mcp-server.py"]
}
}
}
Already-running server (HTTP) — point a client at the Docker
container instead (MCP_TRANSPORT=streamable-http inside the container,
published at http://127.0.0.1:8765/mcp):
claude mcp add --transport http cv-builder http://127.0.0.1:8765/mcp
{
"mcpServers": {
"cv-builder": { "url": "http://127.0.0.1:8765/mcp" }
}
}
There's no authentication on the MCP endpoint or the web UI — fine for
local personal use, but don't publish either port beyond 127.0.0.1
without adding auth first.
Set SNIPPETS_DB to point either server at a different database file
(defaults to data/snippets.db).
API endpoints (same Flask process as the editor)
GET /api/person— read-onlypersonblock ofdata.yaml(Assets page)GET/POST/PUT/DELETE /api/snippets— list/create/update/delete snippetsDELETE /api/snippets/<id>/variants/<level>— remove one detail levelPOST /api/structure— insert/delete/move/replace items indata.yamlGET /api/history,POST /api/undo,POST /api/redo— editor undo/redoGET/PUT/DELETE /api/drafts[/<name>]— saved builder draftsPOST /api/match— rank snippets against posting textPOST /api/compose— compose a named variant from selected snippet idsGET/DELETE /api/variants[/<name>],POST /api/variants/<name>/renderGET /api/images,POST /api/images/upload,POST /api/images/fetch— list, upload, or download images/icons intoassets/images/POST /api/seed— re-seed the database from YAML + markdown sources
Tests
python3 -m pytest
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.