How to Use the Moz (SEO Metrics & Link Research) MCP in Pydantic AI

Q: How do I hook up Moz (SEO Metrics & Link Research) to Pydantic AI?

Install pydantic-ai-slimmcp. Define an MCPToolset with your HTTP endpoint URL. Pass this toolset directly into your agent setup. Do not use the deprecated MCPServerHTTP class.

Q: Will Pydantic AI catch missing metrics from Moz (SEO Metrics & Link Research)?

Yes. If geturlmetrics fails to return a spam score or exact link count, Pydantic AI throws a runtime validation error. This prevents your agent from making SEO decisions on incomplete data.

Q: Can Pydantic AI rank backlinks using Moz (SEO Metrics & Link Research)?

The agent calls gettoplinks to fetch explicitly top-ranked backlinks sorted by DA. Your Pydantic models ensure the DA values are valid numbers before you sort and filter them.

Q: Does this MCP server provide global domain rankings?

You can run globaltopdomains and globaltoppages to extract static lists of the highest DA domains worldwide. The agent validates these massive lists against your custom schemas.

Force strict schema validation on Moz SEO metrics using Pydantic AI and build reliable backlink agents.

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Connect Moz (SEO Metrics & Link Research) MCP to Pydantic AI

Create your Vinkius account to connect Moz (SEO Metrics & Link Research) 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 Moz (SEO Metrics & Link Research) with Pydantic AI

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Validate Domain Authority with Pydantic AI

SEO APIs change their response formats. Your agent breaks when that happens. By routing the `get_url_metrics` tool through Pydantic AI, you force strict type checks on Domain Authority, Page Authority, and spam scores. If Moz returns a string instead of a float, the agent fails loudly. You need exact numbers to make decisions. The `get_linking_domains` tool returns summarized root linking domains hitting specific bounds. Your Pydantic models validate every single domain count before your agent decides whether to pursue a link acquisition.

Parse anchor text distributions safely

Anchor text data is messy. The `get_anchor_text` tool pulls literal anchor text distribution matching backlinks that point to an explicit domain. Your agent maps these messy strings into clean, validated Python objects. You also need to track where a site sends its link equity. The `get_outgoing_links` tool extracts external links and their outbound anchor texts. Your type-safe agent processes this data to identify link selling footprints without hallucinating missing fields.

Control API spend via MCP Server

Runaway API loops drain budgets fast. The `get_usage` tool checks your running quota tracking for current Moz API rows. You define a Pydantic model that sets a hard limit on remaining rows, forcing the agent to halt if you get too close. When you have the budget, you pull heavy data. You run `get_incoming_links` to get explicit incoming backlinks or `get_top_pages` to find the highest PA pages on a site. Every response passes through your validation layer before touching your database.

Setup guide

Set up Moz (SEO Metrics & Link Research) 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": {
        "moz-seo-metrics-link-research-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Moz (SEO Metrics & Link Research) tools.",
)

result = await agent.run("List recent Moz (SEO Metrics & Link Research) 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 Moz. 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 Moz (SEO Metrics & Link Research) MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Define an MCPToolset with your HTTP endpoint URL. Pass this toolset directly into your agent setup. Do not use the deprecated MCPServerHTTP class.

Yes. If get_url_metrics fails to return a spam score or exact link count, Pydantic AI throws a runtime validation error. This prevents your agent from making SEO decisions on incomplete data.

The agent calls get_top_links to fetch explicitly top-ranked backlinks sorted by DA. Your Pydantic models ensure the DA values are valid numbers before you sort and filter them.

You can run global_top_domains and global_top_pages to extract static lists of the highest DA domains worldwide. The agent validates these massive lists against your custom schemas.

You pass your proprietary target domain lists and backlink extraction parameters through an ephemeral V8 Isolate. Vinkius handles the auth via one endpoint token. The server holds no state and drops your SEO data the second the query finishes.

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