Azure MCP Server Directory for Developers

Overview

The Azure MCP Server Directory is a curated collection of Model Context Protocol (MCP) server implementations and resources intended for Azure developers. MCP servers provide an HTTP-based, standard way for language models and applications to request contextual data (documents, embeddings, metadata) from retrieval systems. This directory helps you discover, compare, and join MCP-compatible servers so you can plug context retrieval into model-driven applications quickly.

For developers new to MCP, the directory maintained by Arun Sekhar (Azure-Samples/mcp) collects sample servers, templates, deployment tips, and registration metadata. The repository is a practical starting point for evaluating server implementations, running local instances for testing, or contributing new MCP servers to the ecosystem.

Features

• Curated list of MCP server implementations across multiple languages and runtimes
• Example server templates to bootstrap your own MCP server
• Registration metadata and examples for adding servers to the directory
• Deployment guidance, including Docker-friendly configurations
• Integration examples showing how to query MCP servers from model workflows
• Contribution guidelines and community-oriented resources

Installation / Configuration

Below are common steps to get a sample MCP server from the repository running locally. Adjust commands to match the language template you choose (Node, Python, Go, etc.).

Clone the repo:

git clone https://github.com/Azure-Samples/mcp.git
cd mcp

Build and run using Docker (generic pattern):

# Build
docker build -t azure-mcp-server .

# Run (example)
docker run -p 8080:8080 \
  -e MCP_SERVER_NAME="example-mcp" \
  -e MCP_PORT=8080 \
  azure-mcp-server

Run a language-specific template (example: Node.js):

cd templates/node-server
npm install
npm run start
# or to run with nodemon for development
npm run dev

Environment configuration (example variables):

MCP_SERVER_NAME="example-mcp"
MCP_PORT=8080
MCP_VECTOR_DB_URL="http://localhost:9200"   # if using Elasticsearch or similar
MCP_REGISTRY_URL="https://mcp-directory.example.com/api/v1/servers"

Registering a server in the directory (example JSON + curl):

{
  "name": "example-mcp",
  "description": "Node.js sample MCP server",
  "endpoint": "https://example-mcp.azurewebsites.net",
  "language": "node",
  "features": ["vector-search", "text-embedding", "metadata-filtering"]
}

curl -X POST "https://mcp-directory.example.com/api/v1/servers" \
  -H "Content-Type: application/json" \
  -d @server-registration.json

Available Resources

• GitHub repository: https://github.com/Azure-Samples/mcp — source, templates, and docs
• Sample server templates in multiple languages (Node, Python, Go) to accelerate development
• Dockerfiles and sample docker-compose setups for local and cloud deployment
• Example registration metadata and contribution guide to add servers to the directory
• Integration snippets for clients showing how to query MCP endpoints from model workflows

Directory fields (what each server entry typically contains):

Use Cases

1. Add contextual retrieval to an LLM application

   • Scenario: You have a customer-support chatbot and want it to access product manuals.
   • Flow:
     1. Deploy or pick an MCP server from the directory that supports vector search.
     2. Index manuals into the chosen server (via its ingestion API).
     3. When the model needs context, call the MCP endpoint to retrieve top-N documents and append results to the model prompt.
   • Example client pseudocode:

     resp = requests.post("https://example-mcp/api/v1/query", json={
       "query": "how to reset device X",
       "top_k": 5
     })
     contexts = resp.json()["results"]
     

2. Evaluate and compare MCP servers

   • Scenario: You want to compare performance and features (latency, vector DB backend, filter support).
   • Approach: Use the directory to shortlist servers, run standard queries and ingestion workloads, measure latency and recall, and review feature flags (e.g., metadata filters, chunking strategies).

3. Build local test harnesses for app development

   • Scenario: During app development you need a small, reproducible retrieval service.
   • Approach: Use a template server from the repo, run it via Docker Compose with a lightweight vector DB (SQLite + Faiss or a hosted test instance), and configure your dev model pipeline to query the local MCP endpoint.

4. Contribute a new server implementation

   • Scenario: You maintain a custom retrieval stack and want to expose it via MCP.
   • Steps:
     1. Implement the MCP-compatible HTTP endpoints per the repository examples.
     2. Provide a README and sample config.
     3. Submit a PR or registration metadata to the directory to help others discover your server.

Next steps

• Browse the GitHub repo to find templates that match your language/runtime.
• Run a sample server locally and use