MCP For Claude Marketplace
Braintrust

Braintrust MCP Connector for Claude

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Automate AI evaluations with Braintrust — organize projects, test model datasets, run benchmarks, and manage prompts via any AI agent.

10 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

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

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

How it works

  1. Add this server to your AI cluster
  2. Bind your personal Braintrust API ID variables
  3. Leverage complex model tuning pipelines querying native AI logic regressions on chat

Automate LLM regression analyses effortlessly. Rather than scrolling tables, your bot handles strict semantic checking via Braintrust infrastructure logic directly.

Who is this for?

  • AI Developers — push Ground Truth evaluation text datasets on the fly testing prompt differences
  • Machine Learning Engineers — track specific variable distributions checking accurate regressions remotely
  • Product Teams — observe exact string prompts dynamically pushing features validating response styles
  • Data Scientists — construct massive matrices and evaluate test runs without pulling script queries
ai-evaluationllm-benchmarkingprompt-engineeringmodel-testingai-observabilitydata-analytics

10 tools expose this connector's capabilities to your AI agent.

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

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

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_projects

Retrieve the list of all AI evaluation projects in Braintrust

list_prompts

Retrieve explicitly version-controlled system prompts isolated in Braintrust

See how to talk to your AI agent using Braintrust.

List all active test datasets configured under Braintrust.

I've fetched your Ground Truth repositories. There's 1 dataset active under ID 4a83b9c named 'Support-Responses-Testing'. Should I list the rows nested there?

Look up prompt template using specific ID XYZ.

Prompt XYZ returns successfully. It tracks specific {{user}} tags targeting strict instructions enforcing a professional tone. The JSON mapping version is 1.0.4. Do you need further metadata?

Analyze recent experiments across multiple models testing behavior.

Extracted the historical trace boundaries. Experiment run ID V3 generated a 94% alignment score compared to the previously logged V2 base structure matrix mapping differences on false positives.

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.

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