Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/DXHeroes/mcp-devtoolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/dxheroes/mcp-devtools/testing-standards)<a href="https://agentmods.dev/rules/dxheroes/mcp-devtools/testing-standards"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/testing-standards/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/dxheroes/mcp-devtools/testing-standards"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/testing-standards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00516 | $0.00516 |
| Opus 5.5 | $0.00206 | $0.00206 |
| Sonnet 5.5 | $0.00103 | $0.00103 |
| Haiku 4.5 | $0.00052 | $0.00052 |
Grade A, and why
testing-standards scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Standards
Follow these standards when writing tests for MCP DevTools packages:
@file packages//src/**/.test.ts @file .cursor/rules/typescript-style.mdc
Test Structure
import {
describe,
it,
expect,
beforeEach,
afterEach,
jest,
} from "@jest/globals";
import { featureUnderTest } from "../path/to/feature";
describe("Feature Name", () => {
// Setup mocks and test fixtures
beforeEach(() => {
// Setup code
});
afterEach(() => {
// Cleanup code
jest.resetAllMocks();
});
describe("Specific Functionality", () => {
it("should behave as expected in normal conditions", async () => {
// Arrange
const input = {
/* test data */
};
// Act
const result = await featureUnderTest(input);
// Assert
expect(result).toEqual(/* expected output */);
});
it("should handle error cases appropriately", async () => {
// Arrange
const invalidInput = {
/* invalid test data */
};
// Act & Assert
await expect(featureUnderTest(invalidInput)).rejects.toThrow();
});
});
});
Testing Guidelines
-
Test Organization
- Use descriptive
describeanditblocks - Group related tests together
- Follow the Arrange-Act-Assert pattern
- Keep tests focused on a single functionality
- Use descriptive
-
Mocking
- Mock external dependencies
- Use jest.mock() for external modules
- Create dedicated mock factories for complex objects
- Reset mocks between tests
-
Coverage
- Aim for >80% code coverage
- Test happy paths and error paths
- Include edge cases and boundary conditions
- Test asynchronous behavior correctly
-
Test Data
- Use realistic test data
- Create helper functions for test data generation
- Avoid test data duplication
- Keep test data close to tests that use it
-
Assertions
- Make assertions specific and meaningful
- Test return values, side effects, and exceptions
- Use appropriate matchers (toEqual, toBeCalledWith, etc.)
- Write custom matchers for complex assertions
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 100 lines · 516 tokens per session scan A f8f9a1384ac4
testing-standards is a cursor rule published in the GitHub repository DXHeroes/mcp-devtools (13 stars, last pushed 1y ago), licensed MIT. It adds 516 tokens to every session, about $0.0021 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other cursor rules, from other repositories
minimax-m3-status-verification
MiniMax M3 status and verification contract: exact claim labels, proof matching, multimodal-grounded visual claims, and evidence-first closeouts.
minimax-m3-verification
Extended M3 verification playbook: proof selection, multimodal-grounded checks, shell checks, browser checks, and recovery loops.
approved-plan-execution
Guard execution of an approved plan or burndown: forbid reward hacking and feature deletion; require checkpoints, context discipline, safe terminal use, and fresh verification. Applies to approved plan-.md, complete-everything, and burndown-full runs, whatever model runs them.
verification-before-completion
Require fresh, scope-appropriate evidence before any completion claim. Distinguishes implemented, locally verified, repository-green, PR-green, deployed, and observed-stable states.
cursorrules
You are building an AI/ML project with Python. The project uses PyTorch for model training, handles data pipelines with proper validation, tracks experiments systematically, and follows production ML engineering practices. Code is type-hinted, tested, and reproducible.
propose-test-cases
Propose comprehensive test cases for PR changes.