How to Use the FOSSA (License Compliance) MCP in Pydantic AI

Q: How do I set up the FOSSA (License Compliance) tools with Pydantic AI?

It's one line. After pip install "pydantic-ai-slimmcp", you create an MCPToolset with your Vinkius server URL and add it to your agent's toolsets list. Pydantic AI handles the model generation and validation automatically.

Q: What happens if the FOSSA (License Compliance) server returns data in an unexpected format to my Pydantic AI agent?

Your agent will raise a ValidationError and stop. This is the main benefit: it prevents your agent from making decisions based on incomplete or corrupted data. It fails loudly and safely.

Get type-safe FOSSA results in your agent. Build reliable license compliance bots with Pydantic AI.

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Connect FOSSA (License Compliance) MCP to Pydantic AI

Create your Vinkius account to connect FOSSA (License Compliance) 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 FOSSA (License Compliance) with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Reliably List Projects and Revisions

Start by getting a list of your projects with `list_projects`. With Pydantic AI, the JSON response from the server is automatically parsed and validated against a Pydantic model. If a project is missing a name or has an invalid ID, your code raises a `ValidationError` immediately. The same principle applies when you dig deeper. Calling `list_revisions` or `get_revision` guarantees the data you work with—like commit hashes and scan dates—matches the expected schema. No more defensive coding against weird API responses.

Type-Safe Dependency Analysis

This MCP Server lets your agent map out a project's entire supply chain. You call `get_revision_dependencies` to get the full tree of packages, including all the transitive dependencies buried deep inside. Pydantic AI ensures every dependency in that list is a properly formed object. If the server ever returned a malformed entry, your agent would stop instantly instead of silently passing bad data downstream. This is critical for building compliance automation you can actually trust.

Structurally Sound Checks with Pydantic AI

The most important tool here is `check_vulnerabilities`. You give it a list of dependencies, and it tells you what's wrong with them. Pydantic AI's role is to enforce correctness on the response. You know for a fact that every vulnerability report will have a CVE, a severity level, and a description that match the types you defined. Your agent won't ever have to guess or handle `None` where a string should be.

Setup guide

Set up FOSSA (License Compliance) 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": {
        "fossa-license-compliance-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent FOSSA (License Compliance) transactions")
print(result.output)

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Common questions about FOSSA (License Compliance) MCP in Pydantic AI

It's one line. After `pip install "pydantic-ai-slim[mcp]"`, you create an `MCPToolset` with your Vinkius server URL and add it to your agent's `toolsets` list. Pydantic AI handles the model generation and validation automatically.

Your agent will raise a `ValidationError` and stop. This is the main benefit: it prevents your agent from making decisions based on incomplete or corrupted data. It fails loudly and safely.

Yes. Pydantic AI is model-agnostic. As long as your local LLM can handle tool-calling, you can connect it to this MCP server and get the same type-safe validation on all the FOSSA tool outputs.

Pydantic AI and Vinkius handle the boilerplate. You don't have to write API clients, manage auth tokens, or define Pydantic models for the responses. You just add the `MCPToolset` and your agent gets validated, structured access to all six FOSSA tools.

All data, including project lists and vulnerability reports from `check_vulnerabilities`, is handled within a dedicated, isolated runtime for your request. Vinkius manages authentication via a single token and ensures no data persists after your agent's operation is complete.

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