How to Use the Ember Climate MCP in Pydantic AI

Q: How do I initialize the Ember Climate server in Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your HTTP endpoint. You pass this toolset directly to your Agent instance.

Q: What happens if the Ember Climate API changes its response format in Pydantic AI?

The framework catches it immediately. If a field in getelectricitydemandyearly changes from an integer to a float, the runtime throws a validation error. You fix the model instead of hunting down silent bugs.

Build type-safe energy models by connecting the Ember Climate MCP Server to Pydantic AI.

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Connect Ember Climate MCP to Pydantic AI

Create your Vinkius account to connect Ember Climate 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 Ember Climate with Pydantic AI

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Validate carbon intensity metrics

`get_carbon_intensity_yearly` fetches the emissions footprint of national grids over time. Your Pydantic AI agent pulls this to feed compliance dashboards. The framework guarantees the incoming data matches your expected schema before any downstream logic runs. You never get silent failures here. If the API returns a string instead of a float for the carbon intensity value, Pydantic throws a loud validation error. Your application stops before corrupt data poisons your database.

Build strict multi-region queries

`get_generation_multi_entity` grabs electricity generation stats for several countries at once. Your agent passes a strict list of entity codes to compare the US, Germany, and Brazil. It is the fastest way to build cross-border energy profiles. Pydantic AI forces the agent to use the exact parameters required. When querying `get_power_sector_emissions_yearly`, the agent must provide a valid YYYY date format. The framework rejects any hallucinated date strings instantly.

Enforce schema on Ember Climate MCP

`get_electricity_generation_monthly` returns the TWh output and percentage share for specific energy sources. Your agent uses this to track how much solar or wind a grid actually produces during specific months. Model-agnostic validation means you can swap LLMs without rewriting your data layer. Whether you use Anthropic or local models, the agent always calls `get_api_options` to verify valid filter names, and Pydantic ensures the response structure never breaks your code.

Setup guide

Set up Ember Climate 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": {
        "ember-climate-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Ember Climate MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Create an MCPToolset pointing to your HTTP endpoint. You pass this toolset directly to your Agent instance.

The framework catches it immediately. If a field in get_electricity_demand_yearly changes from an integer to a float, the runtime throws a validation error. You fix the model instead of hunting down silent bugs.

Yes, the framework is completely model-agnostic. You can run a local model to query get_installed_capacity_monthly and the strict type-checking works exactly the same as it does with hosted models.

You define the expected string format in your validation models. If an agent tries to pass a full timestamp to get_power_sector_emissions_monthly instead of the required YYYY-MM, the execution blocks.

Your models live entirely on your local machine. The server only receives standard JSON-RPC requests for public energy data like monthly solar generation. Your internal application structure remains private.

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