How to Use the Clearscope MCP in Pydantic AI

Q: Can I use Clearscope and Pydantic AI for automated grading?

Yes, gradecontent provides the metrics. Your agent uses these to decide if a draft needs more work based on your strict rules.

Use Clearscope with Pydantic AI for type-safe SEO agents. Validate every response against your models at runtime.

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Connect Clearscope MCP to Pydantic AI

Create your Vinkius account to connect Clearscope 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 Clearscope with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Strict type validation for Clearscope

Use `list_terms` to pull NLP variables into your agent. Pydantic AI validates the structure immediately, ensuring your agent never encounters bad data. `get_brief` provides the instructions you need for your writing tasks. Because it's type-checked, you know the output matches your requirements.

Build reliable content pipelines with Pydantic AI

Call `create_report` to generate SEO analysis. The response is parsed into your defined model, preventing any silent failures in your agent's logic. `get_report_details` extracts the scoring properties. If the data doesn't fit your schema, the agent catches it before it touches your database.

Verify text with this MCP Server

Run `grade_content` to check your draft against mapped NLP bounds. Pydantic AI enforces the expected output format, keeping your agent's decisions consistent. `list_competitors` fetches the rival links you need to watch. Your agent processes these arrays with full type safety guaranteed.

Setup guide

Set up Clearscope 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": {
        "clearscope-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Clearscope MCP in Pydantic AI

It prevents hallucinated data from breaking your agent. Every tool response is verified against a model before your code continues.

The MCP server returns raw data, and your Pydantic models force that data into the correct shape. It's the safest way to build agents.

Yes, `grade_content` provides the metrics. Your agent uses these to decide if a draft needs more work based on your strict rules.

Pydantic AI raises a validation error immediately. You catch this in your flow and handle it instead of letting the agent proceed with bad info.

All inputs are strictly typed and validated at the edge. Your raw report data is isolated from the agent's logic by the model definition.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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