by scorecard-ai
Scorecard: Evaluate and optimize LLM systems with thorough testing, actionable metrics, and performance insights to impr
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.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
Scorecard is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Scorecard has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Scorecard surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Scorecard reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Scorecard has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, Scorecard benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: Scorecard is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated Scorecard against two servers with overlapping tools; this profile had the clearer scope statement.
Strong directory entry: Scorecard surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We wired Scorecard into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
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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.
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.
Note: You may need to set environment variables in your MCP client.
npm install scorecard-ai
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.`);
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.
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:
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 Code | Error Type |
|---|---|
| 400 | BadRequestError |
| 401 | AuthenticationError |
| 403 | PermissionDeniedError |
| 404 | NotFoundError |
| 422 | UnprocessableEntityError |
| 429 | RateLimitError |
| >=500 | InternalServerError |
| N/A | APIConnectionError |
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:
// 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,
});
Requests time out after 1 minute by default. You can configure this with a timeout option:
// 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.
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();
// ...
}
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.
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);
[!IMPORTANT] All log messages are intended for debugging only. The format and content of log messages may change between releases.
The log level can be configured in two ways:
SCORECARD_LOG environment variablelogLevel 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 loggingAt 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.
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
});
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.
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' },
});
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.