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/package-documentation)<a href="https://agentmods.dev/rules/dxheroes/mcp-devtools/package-documentation"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/package-documentation/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/package-documentation"><img src="https://agentmods.dev/badge/rules/dxheroes/mcp-devtools/package-documentation.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 | $0.00346 | $0.00346 |
| Opus 5.5 | $0.00138 | $0.00138 |
| Sonnet 5.5 | $0.00069 | $0.00069 |
| Haiku 4.5 | $0.00035 | $0.00035 |
Grade A, and why
package-documentation 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.
What it actually says
Package Documentation Standards
Each package README.md should follow this structure:
@file packages/jira/README.md @file .cursor/rules/repository-structure.mdc
Required Sections
-
Title and Badges
# @mcp-devtools/package-name    -
Short Description
- Single paragraph explaining the purpose of the package
-
Highlights
- Bullet points of key features with emoji icons
-
Quick Start
- Installation instructions
- Configuration examples
- Minimal setup code
-
Tool Reference
- Table with columns: Tool, Description, Parameters, Aliases, Implementation
- Each parameter should list type and whether it's required
- Link each tool name to its implementation file
-
Usage Examples
- Show practical examples of using each tool
- Group related examples under headings
-
Configuration
- Environment variables
- Configuration options
- Integration examples with different platforms
Style Guidelines
- Use emoji icons in section headers
- Include code examples in markdown code blocks
- Use tables for structured information
- Keep language clear and concise
- Include links to related documentation
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 · 63 lines · 346 tokens per session scan A f1fc1a285d54
package-documentation is a cursor rule published in the GitHub repository DXHeroes/mcp-devtools (13 stars, last pushed 1y ago), licensed MIT. It adds 346 tokens to every session, about $0.0014 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
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.