How to Use the SkootEco MCP in Pydantic AI

Q: What tools can I use to track my carbon footprint with SkootEco and Pydantic AI?

You'll primarily use addemission for logging, getemissions for totals, and getimpactprofile to see your overall status.

Q: Can I automate compliance reporting with SkootEco and Pydantic AI?

Yes. The agent can call getesgreport, receiving a structured, predictable output that you can immediately ingest into another system without cleanup.

Q: What data types are involved when I check my account info with SkootEco and Pydantic AI?

The server handles user account details via getaccount, providing structured personal and usage metrics alongside the emission data.

Q: If I want to understand where my emissions come from, which tool should I use with SkootEco and Pydantic AI?

Use getemissionsbyscope or getemissionsbycategory. Both tools return structured data that confirms the source of your pollution.

Type-Safe ESG Reporting with Pydantic AI. Guaranteed data integrity for compliance reports.

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Setup SkootEco with Pydantic AI

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Validate and Log Emissions Data

Using the `add_emission` tool, you log every required piece of emission data. Because this MCP Server validates output against Pydantic models, you never get corrupted or unexpected fields—the agent fails loudly if the API sends bad data. This strict validation is crucial when calling tools like `get_emissions_by_category`, guaranteeing that the returned structure matches exactly what your code expects.

Generate Structured ESG Reports

Need a report? The agent executes `get_esg_report`. This tool pulls all necessary metrics and formats them into predictable, validated data structures. You won't have to write extra parsing code just because the API changed. You can also pull specific summary information with `get_summary_report` (via `get_report`), knowing the output schema is locked down.

Manage Offsets and Assets

The agent makes offset purchasing reliable. First, check options using `list_offsets`, then retrieve details with `get_offset`. The `purchase_offset` tool ensures that the transaction data returned is always valid and correct. Asset tracking works just as well; call `plant_tree` or use `get_tree_count` to track physical impact, all within a strongly typed framework.

Setup guide

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

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

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

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Common questions about SkootEco MCP in Pydantic AI

The entire MCP Server output is validated against your defined Python models. If the server sends something unexpected, the agent throws a clear validation error; no silent failures.

You'll primarily use `add_emission` for logging, `get_emissions` for totals, and `get_impact_profile` to see your overall status.

Yes. The agent can call `get_esg_report`, receiving a structured, predictable output that you can immediately ingest into another system without cleanup.

The server handles user account details via `get_account`, providing structured personal and usage metrics alongside the emission data.

Use `get_emissions_by_scope` or `get_emissions_by_category`. Both tools return structured data that confirms the source of your pollution.

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