developer-tools

Scorecard

scorecard-ai

by scorecard-ai

Scorecard: Evaluate and optimize LLM systems with thorough testing, actionable metrics, and performance insights to impr

Evaluate and optimize LLM systems with comprehensive testing and metrics

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Comprehensive LLM evaluation frameworkAutomated testing workflows

best for

  • / AI developers building LLM applications
  • / Teams implementing continuous testing for AI systems
  • / Organizations measuring LLM performance in production
  • / Researchers comparing different language models

capabilities

  • / Run automated test suites against LLM applications
  • / Collect performance and accuracy metrics
  • / Generate evaluation reports with detailed analytics
  • / Compare model performance across different versions
  • / Track quality metrics over time
  • / Export test results in multiple formats

what it does

Tests and evaluates LLM applications by running automated test suites and collecting performance metrics. Helps developers measure accuracy, reliability, and quality of their AI systems.

about

Scorecard is an official MCP server published by scorecard-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. Scorecard: Evaluate and optimize LLM systems with thorough testing, actionable metrics, and performance insights to impr It is categorized under developer tools.

how to install

You can install Scorecard in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.

license

Apache-2.0

Scorecard is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Scorecard TypeScript API Library

NPM version npm bundle size

This library provides convenient access to the Scorecard REST API from server-side TypeScript or JavaScript.

The REST API documentation can be found on docs.scorecard.io. The full API of this library can be found in api.md.

It is generated with Stainless.

MCP Server

Use the Scorecard MCP Server to enable AI assistants to interact with this API, allowing them to explore endpoints, make test requests, and use documentation to help integrate this SDK into your application.

Add to Cursor Install in VS Code

Note: You may need to set environment variables in your MCP client.

Installation

npm install scorecard-ai

Usage

The full API of this library can be found in api.md.

<!-- prettier-ignore -->
import Scorecard, { runAndEvaluate } from 'scorecard-ai';

async function runSystem(testcaseInput) {
  // Replace with a call to your LLM system
  return { response: testcaseInput.original.toUpperCase() };
}

const client = new Scorecard({
  apiKey: process.env['SCORECARD_API_KEY'],
});

const run = await runAndEvaluate(
  client,
  {
    projectId: '314', // Scorecard Project
    testsetId: '246', // Scorecard Testset
    metricIds: ['789', '101'], // Scorecard Metrics
    system: runSystem, // Your LLM system
  }
);

console.log(`Go to ${run.url} to view your Run's scorecard.`);

Request & Response types

This library includes TypeScript definitions for all request params and response fields. You may import and use them like so:

<!-- prettier-ignore -->
import Scorecard from 'scorecard-ai';

const client = new Scorecard({
  apiKey: process.env['SCORECARD_API_KEY'], // This is the default and can be omitted
});

const testset: Scorecard.Testset = await client.testsets.get('246');

Documentation for each method, request param, and response field are available in docstrings and will appear on hover in most modern editors.

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be thrown:

<!-- prettier-ignore -->
const testset = await client.testsets.get('246').catch(async (err) => {
  if (err instanceof Scorecard.APIError) {
    console.log(err.status); // 400
    console.log(err.name); // BadRequestError
    console.log(err.headers); // {server: 'nginx', ...}
  } else {
    throw err;
  }
});

Error codes are as follows:

Status CodeError Type
400BadRequestError
401AuthenticationError
403PermissionDeniedError
404NotFoundError
422UnprocessableEntityError
429RateLimitError
>=500InternalServerError
N/AAPIConnectionError

Retries

Certain errors will be automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default.

You can use the maxRetries option to configure or disable this:

<!-- prettier-ignore -->
// Configure the default for all requests:
const client = new Scorecard({
  maxRetries: 0, // default is 2
});

// Or, configure per-request:
await client.testsets.get('246', {
  maxRetries: 5,
});

Timeouts

Requests time out after 1 minute by default. You can configure this with a timeout option:

<!-- prettier-ignore -->
// Configure the default for all requests:
const client = new Scorecard({
  timeout: 20 * 1000, // 20 seconds (default is 1 minute)
});

// Override per-request:
await client.testsets.get('246', {
  timeout: 5 * 1000,
});

On timeout, an APIConnectionTimeoutError is thrown.

Note that requests which time out will be retried twice by default.

Auto-pagination

List methods in the Scorecard API are paginated. You can use the for await … of syntax to iterate through items across all pages:

async function fetchAllTestcases(params) {
  const allTestcases = [];
  // Automatically fetches more pages as needed.
  for await (const testcase of client.testcases.list('246', { limit: 30 })) {
    allTestcases.push(testcase);
  }
  return allTestcases;
}

Alternatively, you can request a single page at a time:

let page = await client.testcases.list('246', { limit: 30 });
for (const testcase of page.data) {
  console.log(testcase);
}

// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
  page = await page.getNextPage();
  // ...
}

Advanced Usage

Accessing raw Response data (e.g., headers)

The "raw" Response returned by fetch() can be accessed through the .asResponse() method on the APIPromise type that all methods return. This method returns as soon as the headers for a successful response are received and does not consume the response body, so you are free to write custom parsing or streaming logic.

You can also use the .withResponse() method to get the raw Response along with the parsed data. Unlike .asResponse() this method consumes the body, returning once it is parsed.

<!-- prettier-ignore -->
const client = new Scorecard();

const response = await client.testsets.get('246').asResponse();
console.log(response.headers.get('X-My-Header'));
console.log(response.statusText); // access the underlying Response object

const { data: testset, response: raw } = await client.testsets.get('246').withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(testset.id);

Logging

[!IMPORTANT] All log messages are intended for debugging only. The format and content of log messages may change between releases.

Log levels

The log level can be configured in two ways:

  1. Via the SCORECARD_LOG environment variable
  2. Using the logLevel client option (overrides the environment variable if set)
import Scorecard from 'scorecard-ai';

const client = new Scorecard({
  logLevel: 'debug', // Show all log messages
});

Available log levels, from most to least verbose:

  • 'debug' - Show debug messages, info, warnings, and errors
  • 'info' - Show info messages, warnings, and errors
  • 'warn' - Show warnings and errors (default)
  • 'error' - Show only errors
  • 'off' - Disable all logging

At the 'debug' level, all HTTP requests and responses are logged, including headers and bodies. Some authentication-related headers are redacted, but sensitive data in request and response bodies may still be visible.

Custom logger

By default, this library logs to globalThis.console. You can also provide a custom logger. Most logging libraries are supported, including pino, winston, bunyan, consola, signale, and @std/log. If your logger doesn't work, please open an issue.

When providing a custom logger, the logLevel option still controls which messages are emitted, messages below the configured level will not be sent to your logger.

import Scorecard from 'scorecard-ai';
import pino from 'pino';

const logger = pino();

const client = new Scorecard({
  logger: logger.child({ name: 'Scorecard' }),
  logLevel: 'debug', // Send all messages to pino, allowing it to filter
});

Making custom/undocumented requests

This library is typed for convenient access to the documented API. If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

await client.post('/some/path', {
  body: { some_prop: 'foo' },
  query: { some_query_arg: 'bar' },
});

Undocumented request pa


FAQ

What is the Scorecard MCP server?
Scorecard is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Scorecard?
This profile displays 45 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.445 reviews
  • Layla Reddy· Dec 28, 2024

    Scorecard is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Ganesh Mohane· Dec 20, 2024

    Scorecard has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Isabella Park· Dec 20, 2024

    Strong directory entry: Scorecard surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Omar Khan· Dec 16, 2024

    Scorecard reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Noah Srinivasan· Nov 27, 2024

    Scorecard has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Noah Rao· Nov 19, 2024

    According to our notes, Scorecard benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Noah Gill· Nov 7, 2024

    Useful MCP listing: Scorecard is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Isabella Haddad· Oct 26, 2024

    We evaluated Scorecard against two servers with overlapping tools; this profile had the clearer scope statement.

  • Omar Diallo· Oct 18, 2024

    Strong directory entry: Scorecard surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Layla Anderson· Oct 10, 2024

    We wired Scorecard into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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