How to Use the 15Five MCP in Pydantic AI

Use 15Five with Pydantic AI for type-safe performance management and strict data validation.

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Connect 15Five MCP to Pydantic AI

Create your Vinkius account to connect 15Five 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 15Five with Pydantic AI

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Validate check-ins with Pydantic AI

Every response from `list_checkins` is checked against your Pydantic schemas. If the API returns a malformed record, your agent fails immediately rather than processing bad data. This prevents silent errors when parsing employee feedback. You get a reliable data stream that your agent can trust for analysis.

Type-safe objective tracking in Pydantic AI

Use `list_objectives` to fetch goals while ensuring every field matches your expected types. Pydantic AI forces the agent to handle missing or unexpected fields before it proceeds. This rigor keeps your agent's state consistent. You'll never deal with hallucinated fields that break your downstream logic.

Manage users via Pydantic AI

The `list_users` tool populates your agent's database with verified employee info. By enforcing strict types, you ensure your agent always references valid IDs when triggering actions. This protects you from runtime crashes caused by unexpected API responses. Your agent operates with perfect knowledge of your current user list.

Setup guide

Set up 15Five 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": {
        "15five-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to 15Five tools.",
)

result = await agent.run("List recent 15Five transactions")
print(result.output)

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Why Choose Vinkius

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Common questions about 15Five MCP in Pydantic AI

Include the pydantic-ai-slim package, create an MCPToolset with your Vinkius server address, and pass it to your agent. The framework takes care of the type validation automatically.

Yes, the framework validates all tool outputs at runtime. If the 15Five API structure changes, your agent will catch the error instantly.

Yes, the framework is model-agnostic. As long as your agent can call the validated tools, it works with any model you choose.

By using strict schemas, you prevent malformed data from ever entering your agent's memory. This adds a layer of input sanitization on top of the Vinkius secure tunnel.

Only performance-related strings are exchanged. The data is validated against your schema, and no records are stored by the server beyond the current request lifecycle.

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