How to Use the Ensembl MCP in Pydantic AI

Q: How do I implement Ensembl tools in Pydantic AI?

Use MCPToolset to register the Ensembl server URL. Pydantic AI then validates all incoming tool responses against your models, ensuring runtime correctness.

Q: How do I handle large genomic sequences in Pydantic AI?

For large requests like getsequenceregion, ensure your models are configured to handle the expected payload size to avoid memory bottlenecks during validation.

Enforce strict type-safety on Ensembl genomic queries using Pydantic AI for reliable agent behavior.

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Connect Ensembl MCP to Pydantic AI

Create your Vinkius account to connect Ensembl 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 Ensembl with Pydantic AI

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Perplexity

Type-safe genomic data retrieval

Every response from `get_alignment` or `get_map` is validated against Pydantic models at runtime. If the Ensembl API returns malformed data, your agent catches it immediately with a validation error. You get predictable, clean data structures every time.

Reliable variant consequence prediction

Your agent calls `get_vep_hgvs` and the result is checked against your defined schema before the agent acts on it. This prevents the common issue of silent corruption in your genomic pipelines, ensuring your agent only works with valid, high-quality variant data.

Precise coordinate mapping

Use `get_map` to handle assembly version shifts with full type validation. Your agent defines the expected structure, and the MCP server satisfies it. It turns messy, unpredictable genomic data into a solid, typed foundation for your code.

Setup guide

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

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

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

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Common questions about Ensembl MCP in Pydantic AI

Use `MCPToolset` to register the Ensembl server URL. Pydantic AI then validates all incoming tool responses against your models, ensuring runtime correctness.

The agent will raise a validation error immediately. This stops the pipeline before bad data can cause downstream issues, keeping your research results clean.

Yes, all 27 tools are available. You can use any of them, provided you have a Pydantic model defined to validate the expected output.

For large requests like `get_sequence_region`, ensure your models are configured to handle the expected payload size to avoid memory bottlenecks during validation.

Your queries are processed through a secure, ephemeral MCP link. Since Pydantic AI validates data locally on your machine, your genomic identifiers never linger on external servers.

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