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.
npx agentmods add rules/dxheroes/mcp-devtools/core-libraries-usagegit clone --depth 1 https://github.com/DXHeroes/mcp-devtoolsWhat 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 | $0.00641 | $0.00641 |
| Opus 5 | $0.00320 | $0.00320 |
| Sonnet 5 | $0.00128 | $0.00128 |
| Haiku 4.5 | $0.00064 | $0.00064 |
Grade A, and why
core-libraries-usage 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 yesterday.
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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core Libraries Usage Guidelines
When developing MCP DevTools packages, prefer using shared core libraries over custom implementations to ensure consistency and maintainability.
@file packages/jira/src/index.ts @file .cursor/rules/mcp-server-implementation.mdc
Required Core Libraries
| Library | Package | Purpose | Import Statement |
|---|---|---|---|
| MCP Server | @modelcontextprotocol/server |
Core server functionality | import { createMcpServer } from '@modelcontextprotocol/server'; |
| MCP Inspector | @modelcontextprotocol/inspector |
Debugging tools | import { inspector } from '@modelcontextprotocol/inspector'; |
| MCP Types | @modelcontextprotocol/types |
Shared type definitions | import { McpRequest, McpResponse } from '@modelcontextprotocol/types'; |
Common Utilities
The MCP DevTools core utilities should be used instead of reimplementing common functionality:
// PREFERRED: Import from core utilities
import { validateEnvVars, formatErrorResponse } from "@mcp-devtools/core/utils";
// AVOID: Custom implementation of utilities
const validateEnvVars = (required) => {
/* ... */
}; // Don't do this
Configuration Management
Use the shared configuration management from core:
import { loadConfig } from "@mcp-devtools/core/config";
// Load configuration with standard validation
const config = loadConfig({
requiredVars: ["API_KEY", "API_URL"],
optionalVars: {
TIMEOUT: "30000",
DEBUG: "false",
},
});
HTTP Client
Use the shared HTTP client with standard error handling:
import { httpClient } from "@mcp-devtools/core/http";
// Make HTTP requests with standard error handling
const response = await httpClient.get("https://api.example.com/data", {
headers: { Authorization: `Bearer ${config.API_KEY}` },
});
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.
- yesterday First seen · 96 lines · 641 tokens per session scan A 483abc182417
core-libraries-usage is a cursor rule published in the GitHub repository DXHeroes/mcp-devtools (13 stars, last pushed 1y ago), licensed MIT. It adds 641 tokens to every session, about $0.0032 per session on Opus 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
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.
rule
AI/ML Python development (PyTorch, scikit-learn).
language-agnostic-patterns
Language-agnostic programming patterns: SOLID, design patterns, clean code, and architecture. Load when refactoring, designing abstractions, or reviewing structure — not for everyday syntax.
cursor-tools-mastery
Cursor 3.7 runtime guide: choose the right tool, canvases, Design Mode, /worktree, /best-of-n, Await, and parallel execution where safe.
cursor-agent-orchestration
Cursor 3.7 orchestration guide: when to plan, when to delegate, nested subagents, multi-environment handoffs, /best-of-n, and Await for long-running branches.
fable5-reasoning
Fable 5 reasoning protocols: task interpretation, risk-first decomposition, approach selection, interleaved thinking, hypothesis ledgers, premortems, calibration, and the stuck-strategy ladder. Load for complex, ambiguous, or long-horizon tasks, for debugging strategy, or whenever progress stalls.