Can I insert new test data dynamically tracking specific limits?

Yes. Utilizing the `insert_dataset_row` method, you can effortlessly inject exact JSON tracking payload mapping strings directly inside the text corpus evaluating the final results.

Does it pull out original Prompt definitions stored securely?

Certainly. The `get_prompt` command isolates and returns perfectly version-controlled bounding parameters slicing literal templates natively hosted under the Braintrust database.

How deeply can it inspect test regressions or scoring limits?

Using the robust `list_experiments` call, you can branch full arrays separating LLM version behaviors over massive iterations tracking the performance anomalies accurately.

Braintrust MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Braintrust
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Braintrust through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Braintrust "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Braintrust?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Braintrust MCP Server

Connect your Braintrust AI observation platform to any agent and maintain intense logic evaluation capabilities directly over conversation.

Pydantic AI validates every Braintrust tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Project Analytics — Retrieve logic banks and branch isolated AI test sets
Experiments — Create real trace regression tests appending unique LLM scoring iterations
Datasets — Query accurate Ground Truth sets and insert new prompt templates mapping your system accuracy
Prompt Versioning — Grab perfectly frozen semantic prompts without editing core code boundaries

The Braintrust MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Braintrust to Pydantic AI via MCP

Follow these steps to integrate the Braintrust MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Braintrust with type-safe schemas

Why Use Pydantic AI with the Braintrust MCP Server

Pydantic AI provides unique advantages when paired with Braintrust through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Braintrust integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Braintrust connection logic from agent behavior for testable, maintainable code

Braintrust + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Braintrust MCP Server delivers measurable value.

Type-safe data pipelines: query Braintrust with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Braintrust tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Braintrust and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Braintrust responses and write comprehensive agent tests

Braintrust MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Braintrust to Pydantic AI via MCP:

create_experiment

Establish a new historical experiment trace to record LLM pipeline tests

create_project

Create a new project environment for tracking AI evaluations and datasets

get_dataset

Retrieve a specific dataset containing exact schemas bounding LLM outputs

get_prompt

Retrieve exact variable contexts and literal text templates for a prompt

insert_dataset_row

Append new test cases into a dataset matrix targeting specific evaluations

list_datasets

List isolated Ground Truth text banks used for automated evaluation scoring

list_env_vars

Probe the Braintrust AI Gateway configurations managing model API keys securely

list_experiments

Retrieve all evaluation experiments mapping model test scores and metrics

list_projects

Retrieve the list of all AI evaluation projects in Braintrust

list_prompts

Retrieve explicitly version-controlled system prompts isolated in Braintrust

Example Prompts for Braintrust in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Braintrust immediately.

"List all active test datasets configured under Braintrust."

"Look up prompt template using specific ID XYZ."

"Analyze recent experiments across multiple models testing behavior."

Troubleshooting Braintrust MCP Server with Pydantic AI

Common issues when connecting Braintrust to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Braintrust + Pydantic AI FAQ

Common questions about integrating Braintrust MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Braintrust MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.