PromptVault MCP Server
Enables prompt versioning, evaluation, and management via MCP, CLI, and REST APIs, with support for multiple LLM providers.
README
PromptVault
Open-source prompt versioning, evaluation, and management as an MCP server.
What is PromptVault?
PromptVault is a self-hosted tool for AI engineers and prompt engineers to:
- Version prompts like code with immutable versions and rollback support
- Evaluate prompts against datasets using LLM providers (OpenAI, Anthropic, Ollama)
- Access everything through an MCP server, CLI, or REST API
Quickstart
Install (no clone needed)
pip install pvlt
Or with uv/pipx:
uvx --from git+https://github.com/KrishBnsl/promptVault.git promptctl --help
Or with Docker:
docker run -p 8000:8000 -e GEMINI_API_KEY=your-key ghcr.io/krishbnsl/pvlt
From Source
git clone https://github.com/KrishBnsl/promptVault.git
cd promptVault
uv sync
cp .env.example .env
# Edit .env with your API keys
Create Your First Prompt
promptctl prompt create summarize \
--content "Summarize the following article in {tone} style:\n\n{article}" \
--description "Summarizes an article" \
--variables '{"tone": "concise", "article": "string"}' \
--model-config '{"provider": "openai", "model": "gpt-4o-mini", "temperature": 0.2}' \
--tags "summarization,content"
Create a Dataset
echo '{"input": {"article": "AI is transforming..."}, "expected_output": "AI transforms..."}' > dataset.jsonl
promptctl dataset create article-summaries --file dataset.jsonl
Run Evaluation
promptctl eval run summarize --dataset article-summaries
Use with MCP (Claude Desktop)
Add to your Claude Desktop config:
{
"mcpServers": {
"pvlt": {
"command": "promptctl",
"args": ["serve"]
}
}
}
CLI Reference
promptctl prompt create <name> --content <text> [options]
promptctl prompt list [--tags TAGS] [--limit N]
promptctl prompt show <name> [--version N]
promptctl prompt versions <name>
promptctl prompt diff <name> <version-a> <version-b>
promptctl prompt rollback <name> --version N
promptctl dataset create <name> --file <jsonl-or-json>
promptctl dataset list
promptctl eval run <prompt-name> --dataset <dataset-name>
promptctl eval report <evaluation-id>
promptctl serve [--stdio|--http] [--port 8000]
promptctl web [--port 8080]
MCP Tools
| Tool | Description |
|---|---|
prompt_create |
Create a new prompt with initial version |
prompt_get |
Retrieve a prompt version |
prompt_list |
List all prompts |
prompt_versions |
List all versions of a prompt |
prompt_diff |
Diff two prompt versions |
prompt_rollback |
Rollback to a previous version |
dataset_create |
Create a dataset |
dataset_list |
List datasets |
dataset_get |
Get dataset with items |
evaluation_run |
Run evaluation |
evaluation_status |
Check evaluation status |
evaluation_report |
Get evaluation report |
evaluation_compare |
Compare two evaluations |
REST API
Base URL: http://localhost:8000/api
# Create a prompt
curl -X POST http://localhost:8000/api/prompts \
-H "Content-Type: application/json" \
-d '{"name": "test", "content": "Hello {name}"}'
# List prompts
curl http://localhost:8000/api/prompts
# Get a prompt
curl http://localhost:8000/api/prompts/test
Configuration
Copy .env.example to .env and configure:
PROMPTVAULT_DB_PATH=./promptvault.db
PROMPTVAULT_DEFAULT_PROVIDER=openai
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
OLLAMA_BASE_URL=http://localhost:11434
Architecture
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ MCP Client │ │ CLI │ │ REST Client │
│ (Claude etc) │ │ (promptctl) │ │ (curl, apps) │
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────┐
│ PromptVault Core │
│ ┌─────────────┐ ┌──────────────┐ ┌──────────┐ │
│ │ Versioning │ │ Evaluation │ │ Storage │ │
│ │ Engine │ │ Engine │ │ Layer │ │
│ └─────────────┘ └──────────────┘ └──────────┘ │
│ ┌──────────────────────────────────────────┐ │
│ │ SQLite Database (local) │ │
│ └──────────────────────────────────────────┘ │
└─────────────────────────────────────────────────┘
Documentation
📖 Full docs: https://krishbnsl.github.io/promptVault/
License
MIT License - see LICENSE for details.
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.
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.
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.
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.
E2B
Using MCP to run code via e2b.