How to Use the Microsoft Clarity MCP in Pydantic AI

Q: How does Pydantic AI validate Microsoft Clarity API responses?

The framework intercepts raw JSON payloads from tools like getdashboard and parses them into typed models. If Microsoft Clarity returns an unexpected field, a validation error is raised immediately.

Q: How do I set up the Microsoft Clarity MCP Server integration in Pydantic AI?

Run the server externally, then use the MCPToolset class with your HTTP endpoint. Your Pydantic AI agent can then safely call listrecordings with built-in type validation.

Q: Can my Pydantic AI agent safely fetch Microsoft Clarity heatmaps?

Yes. The agent calls getheatmap to retrieve layout interaction data. The response is validated against strict schemas before the agent processes the coordinates.

Validate Microsoft Clarity UX metrics at runtime with Pydantic AI to prevent corrupt data from breaking your automated audit pipelines.

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Connect Microsoft Clarity MCP to Pydantic AI

Create your Vinkius account to connect Microsoft Clarity 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 Microsoft Clarity with Pydantic AI

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Type-Safe User Behavior Auditing with Pydantic AI

The `list_rage_clicks` tool enforces strict runtime validation on every analytics payload your model requests. Your agent calls this endpoint to find frustrated users, and the response is instantly validated against strict Python schemas. If the API returns unexpected data formats, the system fails loudly instead of letting your model make decisions on corrupted metrics. You get clean, predictable data structures for every single click event.

Validate Session Playbacks and Click Patterns

The `get_recording` tool retrieves specific user paths with guaranteed type safety. Your agent parses the precise event sequences knowing that every field matches your Pydantic models. It cross-references these paths with `list_dead_clicks` to isolate elements that confuse your visitors. The unified MCP toolset pattern ensures your agent never tries to parse malformed JSON.

Track Page Performance and Scroll Depths Safely

The `get_scroll_depth` tool verifies how far down a page users actually read. Because Pydantic AI validates the return type, your model can safely calculate average drop-off points. It combines this with `list_top_pages` to prioritize which layouts need immediate attention. The agent checks connection health via `check_clarity_status` to ensure reliable data streams.

Setup guide

Set up Microsoft Clarity 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": {
        "microsoft-clarity-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Microsoft Clarity transactions")
print(result.output)

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Common questions about Microsoft Clarity MCP in Pydantic AI

The framework intercepts raw JSON payloads from tools like `get_dashboard` and parses them into typed models. If Microsoft Clarity returns an unexpected field, a validation error is raised immediately.

Yes. The toolset is model-agnostic, allowing you to run tools like `list_projects` using local LLMs or commercial APIs.

Run the server externally, then use the `MCPToolset` class with your HTTP endpoint. Your Pydantic AI agent can then safely call `list_recordings` with built-in type validation.

Yes. The agent calls `get_heatmap` to retrieve layout interaction data. The response is validated against strict schemas before the agent processes the coordinates.

All data processed by this server is strictly limited to anonymous interaction events, click coordinates, and scroll percentages. No raw IP addresses or unmasked form inputs are ever exposed to your Python environment.

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