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How to Use the FamilySearch API MCP in Pydantic AI

Build type-safe genealogical pipelines using Pydantic AI to validate every FamilySearch API response at runtime.

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Pydantic AI

Connect FamilySearch API MCP to Pydantic AI

Create your Vinkius account to connect FamilySearch API 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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Validate Ancestral Profiles with Pydantic AI

The `get_person_details` tool fetches raw genealogical data for a specific ancestor based on their unique ID. Your agent processes this payload, forcing every field to conform to your strict Pydantic schemas before writing to your database. If the FamilySearch API returns unexpected or null fields, Pydantic AI triggers an immediate runtime validation error. This prevents corrupt historical records from silently polluting your clean lineage database.

Type-Safe Pedigree Parsing via MCP Server

The `get_person_pedigree` tool returns the ancestral lineage of a target person, structured as a multi-generational tree. Your Pydantic AI agent maps this complex JSON structure directly to typed Python models, ensuring parent-child relationships are parsed accurately. By connecting this MCP Server to your type-safe agent, you eliminate the guesswork out of nested genealogical arrays. Look, here's the deal — if the API returns a broken tree, your agent catches it immediately and your code guarantees that every ancestor node contains the required fields before proceeding.

Verify API Availability Before Querying

The `check_api_status` tool verifies whether the FamilySearch servers are online and responding to requests. Your agent executes this check at startup, ensuring your pipeline fails gracefully if the external service is experiencing downtime. Running this validation saves API quota and keeps your application from hanging. Pydantic AI handles the boolean response, allowing your code to branch into offline mode if the status check fails.

Setup guide

Set up FamilySearch API MCP in Pydantic AI

Prerequisites

  • Python 3.10+ installed
  • pydantic-ai-slim[fastmcp] package
  • Active Vinkius subscription with a valid endpoint token
  1. 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. 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. 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": {
        "familysearch-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FamilySearch API. 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 FamilySearch API MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and use the MCPToolset class to point to your Vinkius HTTP endpoint. Pass this toolset into your agent's toolsets argument to expose the genealogical tools.
The framework raises a validation error, stopping the agent from acting on corrupt data. This is crucial when parsing complex structures returned by `get_person_details`.
Yes, Pydantic AI is model-agnostic. You can connect it to local models or commercial APIs while using this server to fetch pedigrees and historical collections.
Use the `search_persons` tool to query names and dates. The agent validates the search results against your Pydantic model, ensuring you only process records with valid identifiers.
All person details and memories are processed on-the-fly inside an ephemeral V8 sandbox. The Vinkius gateway handles the API token authentication securely, ensuring your private genealogical data never touches persistent storage.

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