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Developer ToolsAuto-generatedScore: 28

Dynamics Telemetry MCP Server

The Dynamics Telemetry API, provided as a native service within the Microsoft Azure and Dynamics 365 ecosystem, serves as a comprehensive gateway for accessing operational, performance, and diagnostic telemetry data generated by Dynamics 365 applications and integrated cloud services.

Quick Start Summary

The Dynamics Telemetry MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Dynamics Telemetry API through natural language. It exposes 1 API endpoints as callable tools, such as Operations_List. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/azure-com-dynamicstelemetry. This integration is sourced from the auto Dynamics Telemetry OpenAPI specification (v2019-01-24) and has a quality score of 28/99 (fair documentation coverage).

1Endpointstools mapped
NoneAuthopen access
28/99Qualityfair
~30 secSetupno auth
View Hosted Config URLClaude Desktop Setup Guide

Server Details

Category
Developer Tools
Authentication
None
Endpoints
1 operations
Transport
STDIO
Spec Version
v2019-01-24
Install Command
npx -y @mcp/azure-com-dynamicstelemetry

Environment Variables

DYNAMICS_TELEMETRY_API_KEY

Example: your_dynamics_telemetry_api_key

Top Endpoints

GET
/providers/Microsoft.DynamicsTelemetry/operations

Operations_List

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📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

⚡Capabilities & Use Cases
The Dynamics Telemetry API, provided as a native service within the Microsoft Azure and Dynamics 365 ecosystem, serves as a comprehensive gateway for accessing operational, performance, and diagnostic telemetry data generated by Dynamics 365 applications and integrated cloud services. Its core capability lies in the real-time and historical ingestion, aggregation, and retrieval of critical metrics such as application event logs, user activity traces, system performance indicators, and integration pipeline status. This API is instrumented directly into the telemetry pipeline, offering developers, DevOps engineers, and IT administrators a programmatic interface to monitor the health, performance, and usage patterns of their Dynamics 365 environments. Typical enterprise use cases include proactive system health monitoring, automated alerting on performance degradation, capacity planning based on user activity analytics, and deep-dive troubleshooting of complex business process flows or custom extensions. It transforms raw, siloed telemetry into an actionable, queryable asset for maintaining robust and efficient enterprise application systems.
🤖AI Agent Value
When exposed as tools to an AI coding assistant via the Model Context Protocol (MCP), the Dynamics Telemetry API unlocks a powerful paradigm for intelligent, context-aware development and operations. An AI agent, such as one running in Claude Desktop or Cursor, can directly invoke the GET /providers/Microsoft.DynamicsTelemetry/operations endpoint to fetch live or recent operational data. This integration provides the AI with immediate, factual context about the runtime state of the systems it is helping to develop or manage. The primary value is the elimination of context-switching and manual log-searching. The developer can engage in a conversational workflow, asking the AI assistant to investigate an issue, and the assistant can autonomously query the telemetry endpoint to retrieve relevant data, analyze patterns, and propose solutions or code modifications grounded in real system behavior, drastically accelerating the debugging and optimization cycle.
💬Example Workflows
Practical workflow examples for a developer utilizing this MCP server are numerous and dynamic. A developer could instruct the AI agent with commands like: "Query the last hour of Dynamics Telemetry operations for the 'Contoso' environment and summarize any error spikes related to the custom API integration," prompting the AI to fetch, filter, and synthesize the data into a concise report. Another task might be: "Analyze the telemetry records for the 'SalesOrderProcessing' plugin over the past week and suggest performance optimizations based on execution duration outliers." Furthermore, an AI agent could be tasked to perform continuous, automated checks, such as "Monitor the telemetry stream for new 'Critical' level events and if found, generate a draft incident report with stack trace analysis and probable root causes." This transforms the AI from a static code generator into an active participant in the operational lifecycle, capable of data-driven decision support.
🛡️Security & Auth
Despite the API specification indicating "None" for authentication in its basic description, securing access is paramount in an enterprise environment. In practice, this API is invariably accessed via Azure Resource Manager or similar Azure service endpoints, which enforce authentication through Microsoft Entra ID (formerly Azure Active Directory). Developers must configure their AI coding assistant's MCP server with appropriate Azure service principals or managed identities possessing the minimal required permissions, adhering strictly to the principle of least privilege—typically, roles like "Monitoring Reader" for data access. Configuration should involve securing any API keys or tokens within encrypted environment variables or secret managers, never in source code. Network security should also be enforced by placing the API behind a virtual network or using Azure API Management to apply rate limiting and filtering. Continuous monitoring of API access logs is recommended to audit the activities performed by the AI agent and ensure compliance with organizational security policies.

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