How to Use the AssessTEAM MCP in Pydantic AI

Q: How to set up AssessTEAM with Pydantic AI?

Use the MCPToolset class to load the server. Add it to your agent's toolsets list to enable runtime validation of all incoming tool data.

Validate AssessTEAM data types with Pydantic AI for error-free agent execution.

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Connect AssessTEAM MCP to Pydantic AI

Create your Vinkius account to connect AssessTEAM 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 AssessTEAM with Pydantic AI

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Perplexity

Strictly typed AssessTEAM data in Pydantic AI

Every response from `list_employees` gets validated against your Pydantic schemas. If the API returns malformed data, the agent halts before it can act on bad info. This keeps your agent's state clean when querying `list_organizational_teams`. You define the structure, and the framework enforces it.

Log hours with Pydantic AI validation

When your agent calls `create_timesheet_entry`, the arguments are validated before they hit the server. It prevents invalid project IDs or hour formats from entering your system. Use `list_timesheets` to pull verified logs back into your workflow. The validation layer ensures every entry matches your expected model.

Query profitability using Pydantic AI

The agent uses `get_profitability_report` to fetch project financial data. Pydantic AI verifies the currency and margin fields, preventing silent data corruption. `list_projects` provides the context for these financial queries. You get precise, model-verified data for your reporting needs.

Setup guide

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

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

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

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Common questions about AssessTEAM MCP in Pydantic AI

Use the `MCPToolset` class to load the server. Add it to your agent's toolsets list to enable runtime validation of all incoming tool data.

It fails with a validation error rather than using corrupted data. This behavior keeps your agent's logic predictable and safe.

The server uses a token-based authentication scheme. Your agent provides this token at connection time to establish a secure, private pipe for your requests.

Yes. You can pass the toolset to your agent, which then chooses the right tool for the task. Each tool response undergoes the same strict validation.

The server manages metadata including project names, employee IDs, and timesheet records. This information is processed in memory and never persisted on our side.

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