How to Use the Upstream Lens MCP in Pydantic AI

Ensure rock-solid data results from Upstream Lens with Pydantic AI.

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Connect Upstream Lens MCP to Pydantic AI

Create your Vinkius account to connect Upstream Lens 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 Upstream Lens with Pydantic AI

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Perplexity

Verify project status and history

Use `list_projects` to gather all environmental projects. Since every response hits a Pydantic model, your agent knows exactly what fields it's getting—no unexpected garbage data. You can then follow up with `list_project_notes`, guaranteeing the structure of any date-filtered updates you retrieve.

Guarantee accurate asset details

When your agent calls `get_property_details` or `list_property_imagery`, Pydantic AI validates the response schema. If the API sends something weird, the agent fails loudly—you know immediately it’s wrong. This type safety is critical when dealing with structured data like property metadata.

Reliably list and track assets

The `list_portfolios` tool pulls your asset groupings, but Pydantic ensures that the output structure is always correct. This prevents silent corruption in your agent’s memory. You can also reliably check health with `check_api_health`, knowing the status response will match a defined schema.

Setup guide

Set up Upstream Lens 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": {
        "upstream-lens-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Upstream Lens MCP in Pydantic AI

The MCP Server provides raw data, and Pydantic AI forces that data into a strict Python object model at runtime. This means your agent always gets predictable, validated results from tools like `list_project_observations`.

It's designed to be modular. Because it exposes its tools via the standard MCP Server format, the Pydantic AI framework can easily wrap and validate every single call made.

Yes. You'll get guaranteed structure when pulling project notes or organization info. The agent doesn't have to guess what the API sent back; it knows because of Pydantic validation.

It provides environmental project details, property metadata, and observation records. All these tool outputs are validated against specific Pydantic models to ensure type safety for your agent.

Since the server handles property imagery, project notes, and organization metadata, using Pydantic AI ensures that when you process this sensitive information, it always arrives in a predictable, verifiable format.

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