How to Use the EOSDA MCP in Pydantic AI

Q: How do I initialize the EOSDA toolset in Pydantic AI?

You import MCPToolset from the Pydantic AI package and point it to your Vinkius HTTP endpoint. Passing this toolset to your agent exposes all 12 EOSDA tools with full runtime type validation.

Q: What happens if the EOSDA API returns unexpected data to Pydantic AI?

If an API response from getndvitimeseries contains unexpected null fields, Pydantic AI raises a validation error. This prevents your agent from making critical crop management decisions based on corrupted or partial data.

Q: Can Pydantic AI validate EOSDA zoning maps for fertilizer application?

Yes, your agent can parse the output of getzoningmap into a validated Pydantic model containing zone boundaries and average index values. This ensures your variable-rate fertilizer spreaders only receive perfectly structured spatial data.

Run type-safe agricultural pipelines by validating EOSDA satellite indices and weather data using Pydantic AI.

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Connect EOSDA MCP to Pydantic AI

Create your Vinkius account to connect EOSDA 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 EOSDA with Pydantic AI

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Type-Safe Field Onboarding with Pydantic AI

The `create_field` tool registers new field boundaries by validating your GeoJSON coordinates against strict Pydantic schemas before execution. If a coordinate is formatted incorrectly or a polygon is self-intersecting, Pydantic AI halts the agent run immediately, preventing corrupted data from hitting the EOSDA API. Managing your farm inventory becomes highly reliable when using `get_fields` within this type-safe framework. Your agent unpacks the returned field area and crop types into strongly typed Python objects, ensuring downstream calculations never encounter null pointer exceptions.

Validating Soil Moisture with an MCP Server

This MCP Server exposes `get_soil_moisture` to deliver precise volumetric water content directly into your Pydantic AI data models. Because every API response is validated at the system boundary, your automated irrigation scripts can safely act on the returned soil moisture levels without fearing malformed payloads. Combining these moisture readings with `get_weather_forecast` allows your agent to make decisions that avoid water waste. The agent parses the 15-day precipitation outlook into typed models to confirm if scheduled irrigation should be postponed.

Strict Vegetation Index Analysis and Rendering

The `get_vegetation_index` tool calculates 17 different agronomic indices and returns them wrapped in strict runtime schemas. Pydantic AI ensures that canopy health statistics—like mean, minimum, and standard deviation—conform to expected ranges before your agent flags a field as stressed. Visualizing these results is handled by `render_index_map`, which returns color-coded raster images of your field. Your agent validates the base64 image payload structure before saving the map, guaranteeing that your field reports always contain valid imagery.

Setup guide

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

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

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

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Common questions about EOSDA MCP in Pydantic AI

You import `MCPToolset` from the Pydantic AI package and point it to your Vinkius HTTP endpoint. Passing this toolset to your agent exposes all 12 EOSDA tools with full runtime type validation.

If an API response from `get_ndvi_timeseries` contains unexpected null fields, Pydantic AI raises a validation error. This prevents your agent from making critical crop management decisions based on corrupted or partial data.

Yes, your agent can parse the output of `get_zoning_map` into a validated Pydantic model containing zone boundaries and average index values. This ensures your variable-rate fertilizer spreaders only receive perfectly structured spatial data.

The framework natively supports async execution for all tools, including heavy operations like `get_satellite_imagery`. This allows your agent to fetch satellite metadata for dozens of fields concurrently without blocking.

All weather parameters and soil moisture values retrieved from the server are processed locally in your application memory after passing through our secure V8 sandbox. No historical agronomy metrics are cached or logged outside your local environment.

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