How to Use the Azure Log Analytics Workspace MCP in Pydantic AI

Q: How do I connect Pydantic AI to my Azure Log Analytics Workspace?

You use the unified MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your agent's toolsets list to give it access to the querylogs tool.

Q: Does the Azure Log Analytics Workspace MCP server support type validation in Pydantic AI?

Yes, Pydantic AI validates the JSON output returned by the querylogs tool at runtime. If the log data does not match your expected Pydantic models, the framework raises a validation error immediately.

Q: Is the old MCPServerHTTP class supported for this MCP server?

The MCPServerHTTP class is deprecated in Pydantic AI. You should use the unified MCPToolset class to connect your agent to the Vinkius HTTP endpoint instead.

Run type-safe Azure KQL queries that fail loudly if your log schema changes.

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Connect Azure Log Analytics Workspace MCP to Pydantic AI

Create your Vinkius account to connect Azure Log Analytics Workspace to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Azure Log Analytics Workspace with Pydantic AI

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Type-Safe Log Queries with Pydantic AI

Stop worrying about your agent hallucinating fields in your log data. Pydantic AI validates every response from the `query_logs` tool against strict Python types at runtime. If the Azure log schema changes, your pipeline fails loudly instead of passing corrupted data downstream. This MCP server is model-agnostic, meaning you can use it with OpenAI, Anthropic, or local models. You get predictable, structured log data parsed directly into your Pydantic models for safe processing.

Unified HTTP Toolsets for Pydantic AI

Setting up your connection is simple. Use the unified `MCPToolset` class pointing to your Vinkius HTTP endpoint and pass it to your Pydantic AI agent. The older `MCPServerHTTP` class is deprecated, so this unified approach keeps your codebase clean and modern. Your agent can then execute KQL operations securely. The server handles the connection to Azure, meaning your Pydantic AI code remains completely decoupled from Azure SDK dependencies and credential management.

Runtime Validation of KQL Outputs

When your agent runs `query_logs`, the output is immediately validated. If the agent tries to parse a timestamp that does not match your Pydantic schema, the framework catches the error instantly. This prevents silent failures in your production monitoring systems. This level of type safety makes this MCP server ideal for automated alerting systems. Your agent can run scheduled KQL operations and trigger typed Python functions when specific log patterns are detected.

Setup guide

Set up Azure Log Analytics Workspace MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "azure-log-analytics-workspace-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Azure Log Analytics Workspace tools.",
)

result = await agent.run("List recent Azure Log Analytics Workspace transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Azure Log Analytics Workspace. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Azure Log Analytics Workspace MCP in Pydantic AI

You use the unified `MCPToolset` class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your agent's `toolsets` list to give it access to the `query_logs` tool.

Yes, Pydantic AI validates the JSON output returned by the `query_logs` tool at runtime. If the log data does not match your expected Pydantic models, the framework raises a validation error immediately.

Yes, Pydantic AI is model-agnostic. You can use this MCP server to query your logs whether your agent is powered by Anthropic, OpenAI, Gemini, or a locally hosted model.

The `MCPServerHTTP` class is deprecated in Pydantic AI. You should use the unified `MCPToolset` class to connect your agent to the Vinkius HTTP endpoint instead.

Your raw Azure diagnostic logs are fetched directly from Azure and streamed to your Pydantic AI runtime via secure HTTP. Vinkius runs the server in an ephemeral sandbox, meaning no log telemetry is stored or analyzed by our platform.

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