How to Use the Datalastic Maritime MCP in Pydantic AI

Build type-safe maritime tracking systems with Datalastic Maritime and Pydantic AI.

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Connect Datalastic Maritime MCP to Pydantic AI

Create your Vinkius account to connect Datalastic Maritime 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 Datalastic Maritime with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Enforce strict types on AIS data

Maritime APIs are notorious for returning messy, inconsistent JSON. When your agent calls `get_vessel_status`, Pydantic AI validates the incoming coordinates and speed against your predefined Python models. If a field is missing, the framework fails loudly. This strict validation prevents silent corruption in your logistics database. You can pull a ship's past routes via `get_vessel_history` and know exactly what data shape you are getting. The MCP integration guarantees your agent never hallucinates a missing latitude value.

Query maritime zones with guaranteed correctness

Searching a geographic area requires precise numerical inputs. Your script uses `find_vessels_in_radius` on the MCP endpoint to locate nearby ships, and Pydantic ensures the radius parameter is actually a float. Nothing executes if the types mismatch. Identifying those ships happens next. The agent runs `search_maritime_vessels` to match names to IMO numbers. Because you define the expected output structure, your downstream code never breaks from unexpected string formats.

Map port coordinates using this MCP Server

Routing algorithms need exact dock locations to calculate arrival times. Pydantic AI can trigger `search_ports_by_country` to gather a regional list of harbors. The agent then iterates through that list using `get_port_details`. Every single coordinate pair gets validated at runtime. You get reliable, type-checked geographical data ready for production use.

Setup guide

Set up Datalastic Maritime 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": {
        "datalastic-maritime-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Datalastic Maritime MCP in Pydantic AI

Install the MCP extra via pip. Use the unified MCPToolset class with your Vinkius HTTP URL, and pass it to your Agent's toolsets array.

The framework handles the validation layer. The server returns standard JSON, and your Pydantic models strictly enforce the schema on the client side.

That class is deprecated. You must use the new MCPToolset unified approach for all remote HTTP and SSE connections.

Yes. Pydantic AI is model-agnostic. You can hook up a local Llama instance or use Claude to query the maritime tools.

Vinkius isolates every single query. Your requested IMO numbers and coordinate bounding boxes run inside a temporary V8 execution environment that vanishes instantly.

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