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/ai-assisted-development)<a href="https://agentmods.dev/rules/dxheroes/mcp-devtools/ai-assisted-development"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/ai-assisted-development/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/ai-assisted-development"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/ai-assisted-development.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 | $0.00000 | $0.00465 |
| Opus 5.5 | $0.00000 | $0.00186 |
| Sonnet 5.5 | $0.00000 | $0.00093 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
ai-assisted-development 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Assisted Development Guidelines
Follow these guidelines when using Cursor IDE with AI assistance for MCP DevTools development:
@url https://docs.cursor.com/ @file .cursor/rules/repository-structure.mdc
Effective AI Prompts
-
Be Specific
- Include file paths and line numbers when referencing code
- Specify the expected behavior, not just the task
- Include relevant context (e.g., "This is part of the Jira MCP integration")
-
Chunk Complex Tasks
- Break down complex tasks into smaller steps
- Ask for one implementation at a time
- Verify each step before proceeding to the next
-
Request Explanations
- Ask AI to explain its reasoning
- Request documentation alongside implementation
- Ask for alternative approaches when appropriate
Code Review Assistance
-
Request Focused Reviews
- Ask AI to review specific aspects (security, performance, etc.)
- Provide the full context of the code being reviewed
- Ask for specific improvements rather than general feedback
-
Validation Assistance
- Ask AI to help write tests for your code
- Request validation of edge cases
- Use AI to check for potential bugs or edge cases
Documentation Assistance
-
Generate Documentation
- Ask AI to create or update README sections
- Request JSDoc comments for functions
- Use AI to help document complex algorithms
-
Consistency
- Ask AI to ensure documentation follows the project standards
- Request help maintaining consistent terminology
- Use AI to check for documentation gaps
Best Practices
-
Always Review AI-Generated Code
- Check for correctness and appropriateness
- Verify complex algorithms
- Ensure it meets project standards
-
Iterative Refinement
- Start with a basic implementation
- Refine with more specific requirements
- Use AI to help optimize the code
-
Enhance, Don't Replace
- Use AI as a collaborator, not a replacement
- Maintain ownership of the code
- Understand the code AI generates
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 · 77 lines · 465 tokens per session scan A 4217846d31f5
ai-assisted-development is a cursor rule published in the GitHub repository DXHeroes/mcp-devtools (13 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 465 tokens. 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).
data-fetching
Data fetching patterns with TanStack Query, Server Components, and Supabase.
components
Component patterns and UI conventions.
git
Git workflow and commit conventions.
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.