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.gitignore

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*.pdn
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# Pycharm
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.idea/*
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.idea/*
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articles/planetary-computer/doc-tree-generator.py
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.gitignore
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articles/planetary-computer/article_links_diagram.md
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articles/planetary-computer/index_helper.py
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articles/planetary-computer/metadata_index.csv
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articles/planetary-computer/broken_link_checker.py
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articles/planetary-computer/broken_links_external.csv
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articles/planetary-computer/broken_links.csv
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articles/planetary-computer/check_orphaned_docs.py
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# Python cache files
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*.pyc
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__pycache__/
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articles/planetary-computer/interesting_stats.py
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articles/planetary-computer/locate_text.py
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articles/planetary-computer/image_syntax_checker.py
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articles/planetary-computer/image-file-fixer.py
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articles/planetary-computer/orphaned-media.py
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articles/planetary-computer/orphaned_media_files.txt

.openpublishing.publish.config.json

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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "remote-mcp-functions-dotnet",
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"url": "https://github.com/Azure-Samples/remote-mcp-functions-dotnet",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "remote-mcp-functions-typescript",
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"url": "https://github.com/Azure-Samples/remote-mcp-functions-typescript",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "remote-mcp-functions-python",
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"url": "https://github.com/Azure-Samples/remote-mcp-functions-python",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "remote-mcp-functions-java",
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"url": "https://github.com/Azure-Samples/remote-mcp-functions-java",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azure-functions-durable-js",
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"url": "https://github.com/Azure/azure-functions-durable-js",
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"branch": "dev",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azure-functions-mongodb-extension",
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"url": "https://github.com/Azure/Azure-functions-mongodb-extension",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azure-functions-nodejs-v4",
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"url": "https://github.com/Azure/azure-functions-nodejs-samples",

.openpublishing.redirection.json

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"redirect_url": "/azure/architecture/web-apps/guides/multi-region-app-service/multi-region-app-service",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/app-service/app-service-configure-premium-tier.md",
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"redirect_url": "/azure/app-service/app-service-configure-premium-v4-tier",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/public-multi-access-edge-compute-mec/tutorial-create-vm-using-python-sdk.md",
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"redirect_url": "/previous-versions/azure/public-multi-access-edge-compute-mec/tutorial-create-vm-using-python-sdk",
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},
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{
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"source_path_from_root": "/articles/azure-functions/migration.md",
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"redirect_url": "/azure/azure-functions/migration/migrate-lambda-workloads-overview",
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"redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/azure-functions/migration/migrate-lambda-workloads-overview.md",
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"redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/azure-functions/migration/discover-lambda-workloads.md",
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"redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/azure-functions/migration/assess-lambda-workloads.md",
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"redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/azure-functions/migration/migrate-lambda-workloads.md",
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"redirect_url": "/azure/azure-functions/migration/migrate-aws-lambda-to-azure-functions",
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"redirect_document_id": false
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},
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{

articles/active-directory-b2c/service-limits.md

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| 20 | Egypt | 50 | 200 |
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| 211 | South Sudan | 10 | 30 |
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| 212 | Morocco | 20 | 100 |
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| 213 | Algeria | 20 | 100 |
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| 216 | Tunisia | 20 | 100 |
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| 221 | Senegal | 10 | 30 |
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| 223 | Mali | 20 | 100 |
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| 224 | Guinea | 20 | 100 |
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| 225 | Côte d’Ivoire | 10 | 30 |
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| 225 | Ivory Coast| 10 | 30 |
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| 226 | Burina Faso | 10 | 30 |
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| 228 | Togo | 10 | 30 |
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| 233 | Ghana| 10 | 30 |
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| 234 | Nigeria | 20 | 100 |
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| 238 | Cape Verde | 10 | 30 |
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| 251 | Ethiopia  | 10 | 30 |
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| 261 | Madagascar | 10 | 30 |
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| 263 | Zimbabwe | 10 | 30 |
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| 265 | Malawi  | 10 | 30 |
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| 373 | Moldova | 20 | 100 |
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| 375 | Belarus   | 10 | 30 |
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| 386 | Slovenia | 10 | 50 |
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| 501 | Belize| 10 | 30 |
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| 502 | Guatemala | 10 | 50
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| 503 | El Salvador | 10 | 30 |
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| 52 | Mexico | 100 | 500
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| 52 | Mexico | 100 | 500 |
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| 53 | Cuba | 10 | 30 |
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| 58 | Venezuela| 10 | 30 |
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| 60 | Malaysia | 50 | 200 |
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| 62 | Indonesia | 50 | 200 |
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| 63 | Philippines | 50 | 200 |
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| 670 | Timor-Leste  | 10 | 30 |
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| 670 | East Timor (Timor-Leste)  | 10 | 30 |
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| 675 | Papua New Guinea  | 10 | 30 |
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| 7 | Russia | 100 | 1000 |
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| 84 | Vietnam | 150 | 500 |
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| 855 | Cambodia | 50 | 200 |
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| 92 | Pakistan| 100 | 1000 |
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| 94 | Sri Lanka | 100 | 500 |
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| 95 | Myanmar (Burma) | 10 | 30 |
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| 961 | Lebanon  | 10 | 30 |
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| 963 | Syria  | 10 | 30 |
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---
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title: Include file
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description: Include file
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services: api-center
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author: dlepow
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ms.service: azure-api-center
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ms.topic: include
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ms.date: 05/12/2025
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ms.author: danlep
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ms.custom: Include file
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---
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## About MCP servers
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AI agents are becoming widely adopted because of enhanced large language model (LLM) capabilities. However, even the most advanced models face limitations because of their isolation from external data. Each new data source potentially requires custom implementations to extract, prepare, and make data accessible for the models.
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The [model context protocol](https://www.anthropic.com/news/model-context-protocol) (MCP) helps solve this problem. MCP is an open standard for connecting AI models and agents with external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet, such as remote databases or APIs).
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### MCP architecture
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The following diagram illustrates the MCP architecture:
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:::image type="content" source="media/about-mcp-servers/mcp-architecture.png" alt-text="Diagram of model context protocol (MCP) architecture.":::
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The architecture consists of the following components:
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| Component | Description |
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|----------------|-------------------------------------------------------------------------------------------------|
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| **MCP hosts** | LLM applications such as chat apps or AI assistants in your IDEs (like GitHub Copilot in Visual Studio Code) that need to access external capabilities |
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| **MCP clients**| Protocol clients, inside the host application, that maintain 1:1 connections with servers |
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| **MCP servers**| Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients |
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| **MCP protocol**| Transport layer in the middle |
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MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it connects to the MCP server. The MCP server then connects to, for example, a database or an API. MCP hosts and servers connect with each other through the MCP protocol.
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### Remote versus local MCP servers
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MCP utilizes a client-host-server architecture built on [JSON-RPC 2.0 for messaging](https://modelcontextprotocol.io/docs/concepts/architecture). Communication between clients and servers occurs over defined transport layers, and supports primarily two modes of operation:
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* **Remote MCP servers** - MCP clients connect to MCP servers over the internet, establishing a connection using HTTP and server-sent events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth.
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* **Local MCP servers** MCP clients connect to MCP servers on the same machine, using standard input/output as a local transport method.

articles/api-center/register-discover-mcp-server.md

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This article describes how to use Azure API Center to maintain an inventory (or *registry*) of remote model context protocol (MCP) servers and help stakeholders discover them using the API Center portal. MCP servers expose backend APIs or data sources in a standard way to AI agents and models that consume them.
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## About MCP servers
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AI agents are becoming widely adopted because of enhanced large language model (LLM) capabilities. However, even the most advanced models face limitations because of their isolation from external data. Each new data source potentially requires custom implementations to extract, prepare, and make data accessible for the models.
20-
21-
The [model context protocol](https://www.anthropic.com/news/model-context-protocol) (MCP) helps solve this problem. MCP is an open standard for connecting AI models and agents with external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet, such as remote databases or APIs).
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### MCP architecture
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The following diagram illustrates the MCP architecture:
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:::image type="content" source="media/register-discover-mcp-server/mcp-architecture.png" alt-text="Diagram of model context protocol (MCP) architecture.":::
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The architecture consists of the following components:
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| Component | Description |
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|----------------|-------------------------------------------------------------------------------------------------|
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| **MCP hosts** | LLM applications such as chat apps or AI assistants in your IDEs (like GitHub Copilot in Visual Studio Code) that need to access external capabilities |
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| **MCP clients**| Protocol clients, inside the host application, that maintain 1:1 connections with servers |
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| **MCP servers**| Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients |
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| **MCP protocol**| Transport layer in the middle |
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MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it connects to the MCP server. The MCP server then connects to, for example, a database or an API. MCP hosts and servers connect with each other through the MCP protocol.
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### Remote versus local MCP servers
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MCP utilizes a client-host-server architecture built on [JSON-RPC 2.0 for messaging](https://modelcontextprotocol.io/docs/concepts/architecture). Communication between clients and servers occurs over defined transport layers, and supports primarily two modes of operation:
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* **Remote MCP servers** - MCP clients connect to MCP servers over the internet, establishing a connection using HTTP and server-sent events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth.
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* **Local MCP servers** MCP clients connect to MCP servers on the same machine, using standard input/output as a local transport method.
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[!INCLUDE [about-mcp-servers](includes/about-mcp-servers.md)]
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## MCP servers in your API inventory
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articles/api-management/TOC.yml

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href: azure-openai-api-from-specification.md
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- name: Authenticate and authorize to Azure OpenAI
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href: api-management-authenticate-authorize-azure-openai.md
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- name: Expose REST API as MCP server
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href: export-rest-mcp-server.md
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- name: Semantic caching for Azure OpenAI API requests
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- name: Protect Azure OpenAI keys
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items:
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- name: API authentication and authorization options
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- name: Protect product APIs with Microsoft Entra ID applications
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href: applications.md
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- name: Protect your API with Microsoft Entra ID
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- name: Protect your API with Azure AD B2C
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- name: Azure Policy built-ins
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displayName: samples, policies, definitions
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href: ./policy-reference.md
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- name: Gateway log schema
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- name: Developer portal audit log schema
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href: developer-portal-audit-log-schema-reference.md
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- name: Event Grid events schema
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href: ../event-grid/event-schema-api-management.md?toc=/azure/api-management/toc.json&bc=/azure/api-management/breadcrumb/toc.json
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- name: Virtual network configuration
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href: self-hosted-gateway-settings-reference.md
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- name: Self-hosted gateway Azure Arc configuration
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href: self-hosted-gateway-arc-reference.md
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- name: Diagnostic logs settings
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href: diagnostic-logs-reference.md
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- name: Resources
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items:
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- name: FAQ

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