Tim Meyers, with contributions from Todd Uhl, demonstrates how to expose actionable policy insights derived from 10,000+ public comments using a standards-based MCP server, Azure cloud services, and integration into Microsoft Copilot and Teams.

Creating an AI Policy Analysis Copilot

Authors: Tim Meyers (Microsoft), with contributions from Todd Uhl

This article is part three of the “AI + Policy Analysis” series and focuses on making large-scale AI-derived policy insights accessible to users, directly inside Microsoft 365 Copilot and Teams.

Series Recap

  • Part 1: Building a scalable Gen AI pipeline with Azure Functions to summarize 10,000+ public comments on the White House AI Action Plan.
  • Part 2: Constructing a metadata-rich knowledge graph with Microsoft GraphRAG to surface deeper patterns and structure.
  • Part 3 (this post): Integrating these insights into everyday workflows using a standards-based Model Context Protocol (MCP) server, Azure containerization, and Copilot custom agent tooling.

Architecture Overview

After developing the knowledge graph, the challenge was making it usable by analysts and policymakers who require digestible answers and summaries. The solution:

  • Build a lightweight server hosted in Azure Container Apps to expose LLM tools for querying insights.
  • Expose tools for LLM agents (Copilot, ChatGPT, OSS agents) via MCP—so they can discover and invoke these endpoints in a standardized way.

Core Tools Exposed

  • aiap_report_request: Accepts user queries to kick off report generation.
  • aiap_get_report: Retrieves status/results of requested reports, supporting potentially slow knowledge graph queries with an asynchronous model.

This separation guarantees scalability and responsiveness for users working with large, complex data sets.

Why Model Context Protocol (MCP)?

MCP is an open standard for LLM tool portability, chosen for its:

  • Portability: Tools can be defined once and used by multiple LLMs (Copilot, OSS, etc.)
  • Async workflows: Designed for decoupled request/response patterns common in LLM tool scenarios
  • Future flexibility: Lays groundwork for easily supporting Teams bots, Power BI dashboards, or external apps without major rework.

Server-side, FastMCP 2.0 (Pythonic MCP server) was used to rapidly create the service, register tool functions with simple decorators, and handle all necessary protocol details.

# Skeleton tool registration with FastMCP

@mcp.tool(
  name="aiap_report_request",
  description="Submit an AI policy response report request for analysis and processing"
)
def aiap_report_request_mcp(query: str = "Analyze AI policy responses") -> str:
    return aiap_report_request(query)

@mcp.tool(
  name="aiap_get_report",
  description="Get the status and result of an AI policy response report request"
)
def aiap_get_report_mcp(request_id: str) -> str:
    return aiap_get_report(request_id)

Underlying report generation leverages LazyGraphRAG for scalable knowledge graph querying and summarization.

Step-by-step: From Dev to Workflow Integration

1. Containerizing the MCP Server

A simple Docker image (Python 3.11 base, non-root user) is built:

FROM python:3.11.8-slim-bookworm
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY .env .
COPY server.py .
COPY app/ ./app/
USER mcpuser
EXPOSE 8080
CMD ["python", "server.py"]

2. Azure Deployment

Using the Azure Developer CLI (azd) with Bicep templates, the authors provision:

  • Container app
  • Container registry
  • Managed identity
  • Workload profile for performance

The infrastructure is deployed and the MCP server is exposed for LLM agents to use.

3. Copilot Studio & Teams Integration

With Copilot Studio’s agent extension method, a custom Copilot agent connects to the MCP server through a manifest/OpenAPI spec. Steps included:

  • Crafting a plugin manifest for the custom connector
  • Adding the MCP Server as a tool to the Copilot Custom Agent
  • Optionally adding tools to handle async report polling (e.g., “Add Delay”)
  • Testing and deploying to Microsoft 365 Copilot and Teams channels

This enables analysts to ask natural-language questions and request reports—receiving structured summaries powered by the underlying knowledge graph.

4. Bonus: Portability

By adhering to MCP, the same MCP server was consumed in multiple LLM ecosystems with no additional changes—tested with GitHub Copilot, AI Foundry Agent Service, Semantic Kernel, and others.

Key Takeaways

  • Exposed GraphRAG-generated policy insights through a standards-based MCP server
  • Deployed and hosted solution using Azure Container Apps and azd
  • Integrated with Microsoft Copilot and Teams via Copilot Studio, enabling workflow-native insights
  • Emphasized a hybrid approach combining pro-code (Python) and low-code (Copilot Studio) techniques
  • Demonstrated the flexibility and scalability of the MCP approach for AI-driven policy analysis

By applying this architecture, teams can rapidly surface actionable insights from massive public data sets, make them queryable and consumable in natural language through Microsoft Copilot and Teams, and remain open to future extensibility in other LLM and AI platforms.

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