LABSCO SUMMARY
Writing evals conversationally is the right use of this, because the bottleneck is test cases, not tooling.
Everyone agrees they should evaluate their prompts and nobody has enough test cases, because writing them is dull. Being able to describe a scenario and have the case land in the project removes the exact friction that keeps eval suites thin. Watch the loop though: an agent generating the cases that judge its own output is a closed circle worth breaking with a few cases written by a human who knows what failure looks like.
What it is
Scorecard's endpoint over their LLM evaluation platform — creating evaluation projects, configuring what is measured, and running it.
What you get
- Evaluation projects, and test cases organised by scenario
- Metric configuration such as accuracy and helpfulness
- Running a model evaluation and reading the results
Requirements
OAuth: your client opens the Scorecard sign-in itself, with nothing to configure by hand. A local install is offered as an alternative.
Setup effort
Paste a URL, then authorize — add the endpoint to your client, then approve the OAuth consent screen
USE CASES
01
Starting an eval suite
Create the project and describe test cases by scenario.
02
Deciding what counts as good
Configure the metrics before the run rather than after.
03
Running and reading
Evaluate a model against the suite and pull the results.
BEFORE YOU INSTALL
What to know first
Requires
- an MCP client that can reach a remote endpoint
- OAuth 2.0 sign-in to your Scorecard account
INSTALLATION
Connect your client
Add the endpoint to your client, then approve the OAuth consent screen.
Server setup
ENDPOINT URL
https://mcp.scorecard.io/mcpRequirements
- OAuth 2.0 sign-in to your Scorecard account
