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/mcp-server-implementation)<a href="https://agentmods.dev/rules/dxheroes/mcp-devtools/mcp-server-implementation"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/mcp-server-implementation/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/mcp-server-implementation"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/mcp-server-implementation.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 | $0.00526 | $0.00526 |
| Opus 5.5 | $0.00210 | $0.00210 |
| Sonnet 5.5 | $0.00105 | $0.00105 |
| Haiku 4.5 | $0.00053 | $0.00053 |
Grade A, and why
mcp-server-implementation 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Server Implementation Guidelines
When implementing a new MCP server or updating an existing one, follow these guidelines:
@file packages/jira/src/index.ts @file .cursor/rules/core-libraries-usage.mdc @file .cursor/rules/typescript-style.mdc
Server Setup
import { createMcpServer } from "@modelcontextprotocol/server";
import { registerTool1 } from "./tools/tool1";
import { registerTool2 } from "./tools/tool2";
// Import more tools as needed
// Create configuration with proper error handling
const getConfig = (): Config => {
const required = ["REQUIRED_ENV_VAR1", "REQUIRED_ENV_VAR2"];
for (const env of required) {
if (!process.env[env]) {
console.error(`Missing required environment variable: ${env}`);
process.exit(1);
}
}
return {
var1: process.env.REQUIRED_ENV_VAR1,
var2: process.env.REQUIRED_ENV_VAR2,
optionalVar: process.env.OPTIONAL_ENV_VAR || "default",
};
};
// Initialize server
const initServer = async () => {
try {
const config = getConfig();
const server = createMcpServer();
// Register all tools
registerTool1(server);
registerTool2(server);
// Register more tools
// Start the server
server.start();
console.log("MCP server started successfully");
} catch (error) {
console.error("Failed to start MCP server:", error);
process.exit(1);
}
};
// Start the server
initServer();
Guidelines
-
Environment Variables
- Validate all required environment variables at startup
- Provide sensible defaults for optional variables
- Document all environment variables in README
-
Error Handling
- Implement proper error handling for server initialization
- Log meaningful error messages
- Exit with non-zero code on critical errors
-
Tool Registration
- Register each tool from its own module
- Keep registration code organized and maintainable
- Use consistent patterns across all tools
-
Logging
- Log server start/stop events
- Include appropriate context in logs
- Avoid logging sensitive information
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 · 94 lines · 526 tokens per session scan A ed659fe48a5a
mcp-server-implementation is a cursor rule published in the GitHub repository DXHeroes/mcp-devtools (13 stars, last pushed 1y ago), licensed MIT. It adds 526 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
data-fetching
Data fetching patterns with TanStack Query, Server Components, and Supabase.
full-stack-ship-discipline
Require frontend changes and their DB/RPC/RLS/storage/secret dependencies to ship and be remotely verified together. Apply when implementing code that reads or writes backend state. Do not apply to plan- or present-then-stop audits; production data mutation still requires confirmation.
supabase
Supabase patterns and conventions for this project.
senior-engineer
Senior-engineer execution defaults for the Next.js + React + Supabase stack — state/data library choices, separation of concerns, security non-negotiables. Attach when building or refactoring features in a React/Next.js/Supabase codebase.
composer-fullstack-delivery
New screen, feature, endpoint, or multi-layer slice — freeze seams, ship one demoable path, failure modes, Run/Wired handoff. Skip for a single-module code change.
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.