Istedlal MCP Server
Provides AI agents with access to file metadata, vector search, and workflow metrics. It enables operations such as file metadata retrieval and semantic search over file embeddings using pgvector.
README
Istedlal MCP Server
MCP Server for Istedlal AI Agents - file metadata, vector search, workflow metrics access.
Requirements
- Python 3.10+
- See
requirements.txtfor dependencies
Setup
# Create virtual environment
python -m venv venv
venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# Create .env with required variables (see docs/ENV_SETUP.md)
Run
Terminal testing (use streamable-http to avoid "Invalid JSON: EOF" errors):
# .env: MCP_TRANSPORT=streamable-http
python -m src.main
# Server at http://localhost:8000/mcp
Cursor/IDE integration (stdio - Cursor spawns the process, don't run manually):
# .env: MCP_TRANSPORT=stdio
# Add server to Cursor MCP settings; Cursor will start it automatically
Tools
get_file_metadata- Fetch metadata for a file by IDsearch_files- Search files by metadata filterssemantic_search_files- Phase 2 - Semantic search over file embeddings (pgvector)
Testing with MCP Inspector
See docs/MCP_INSPECTOR_GUIDE.md for the complete step-by-step guide.
npx -y @modelcontextprotocol/inspector
Production
Production Checklist
| Item | Required | Notes |
|---|---|---|
| Dockerfile | Yes | Build container image |
| .dockerignore | Yes | Exclude venv, .env, pycache |
| Production .env | Yes | Set on server (never commit) |
| Port 8000 | Yes | Expose for MCP endpoint |
| PostgreSQL | Optional | For real pgvector (Phase 2) |
What to Exclude from Deployment
.cursor/– Cursor IDE config only, not needed on servervenv/– Create fresh on server or use Docker.env– Contains secrets; set separately on server__pycache__/– Python cache, auto-generateddata/– Reference docs only, not runtime
Production Environment Variables
MCP_TRANSPORT=streamable-http
HTTP_HOST=0.0.0.0
HTTP_PORT=8000
DATABASE_URL=postgresql://user:password@db-host:5432/dbname
PGVECTOR_ENABLED=true
LOG_LEVEL=INFO
MCP_BEARER_TOKEN=your-secret-token # Required – Bearer token auth for /mcp
Dockerfile (Create if Deploying via Docker)
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY src/ ./src/
ENV MCP_TRANSPORT=streamable-http
ENV PYTHONUNBUFFERED=1
EXPOSE 8000
CMD ["python", "-m", "src.main"]
.dockerignore (Create to Exclude from Build)
venv/
.env
.git/
.cursor/
__pycache__/
*.pyc
data/
docs/
scripts/
tests/
infra/
Deployment Steps
- Build:
docker build -t istedlal-mcp . - Run:
docker run -p 8000:8000 -e DATABASE_URL=... -e MCP_BEARER_TOKEN=your-secret istedlal-mcp - Verify:
curl http://localhost:8000/(info page) - MCP Endpoint:
http://your-server:8000/mcp
Kubernetes (Optional)
- Use Deployment + Service manifests in
infra/k8s/ - Expose Service (ClusterIP/NodePort/LoadBalancer)
- Set DATABASE_URL via Secret
Health & Monitoring
- Root
/returns JSON with status - MCP endpoint:
/mcp(for MCP clients only) - Logs: Set LOG_LEVEL=DEBUG for troubleshooting
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.