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/cafreeman/foundry-mcpWrote 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/cafreeman/foundry-mcp/foundry)<a href="https://agentmods.dev/rules/cafreeman/foundry-mcp/foundry"><img src="https://agentmods.dev/badge/rules/cafreeman/foundry-mcp/foundry/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/cafreeman/foundry-mcp/foundry"><img src="https://agentmods.dev/badge/rules/cafreeman/foundry-mcp/foundry.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 | $0.05368 | $0.05368 |
| Opus 5.5 | $0.02147 | $0.02147 |
| Sonnet 5.5 | $0.01074 | $0.01074 |
| Haiku 4.5 | $0.00537 | $0.00537 |
Grade A, and why
Foundry MCP Usage Guide 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 2mo 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 — 530 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Foundry MCP Usage Guide for AI Assistants
Overview
Foundry MCP is a project management tool designed specifically for AI coding assistants. It provides structured project context, specification management, and workflow guidance through a standardized directory structure in ~/.foundry/.
Key Principles:
- Content Agnostic: Foundry manages structure, not content opinions
- LLM-Driven: You provide complete content as arguments
- User-Controlled: Users control content, CLI manages structure
- Workflow-Aware: Tools provide next-steps guidance for efficient development
CRITICAL: Understanding Document Purpose
These documents serve as COMPLETE CONTEXT for future LLMs and developers:
- Each document must contain ALL information needed for someone with no prior project knowledge
- Future LLMs will load these as PRIMARY reference for implementation work
- Apply "Cold Start Test": Could a skilled developer implement using only this document?
- Documents must be self-contained with comprehensive architectural and business context
- Include implementation prerequisites, dependencies, decision rationale, and edge cases
Document Creation Mindset:
- Write for someone who has never seen this project before
- Include WHY decisions were made, not just WHAT was decided
- Provide complete technical and business context for implementation
- Assume the reader needs to understand the full system to implement successfully
Core Concepts
Project Structure
Each Foundry project contains:
vision.md: High-level product vision and goals (200+ characters, you provide)tech-stack.md: Technology decisions and architecture (150+ characters, you provide)summary.md: Concise context for quick LLM consumption (100+ characters, you provide)specs/: Directory containing timestamped feature specifications
Specification Structure
Each spec contains:
spec.md: Detailed feature requirements, acceptance criteria, implementation approachtask-list.md: Implementation checklist with checkboxes (- [ ] task, - [x] completed)notes.md: Additional context, design decisions, and implementation notes
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.
- 2mo ago First seen · 530 lines · 5,368 tokens per session scan A 52749713a47d
Foundry MCP Usage Guide is a cursor rule published in the GitHub repository cafreeman/foundry-mcp (4 stars, last pushed 7mo ago), licensed MIT. It adds 5,368 tokens to every session, about $0.0215 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 2026-08-31.
Other cursor rules, from other repositories
slice-tasks
PLANNING SPINE STEP 2 of 3 — Slice the work: break a scoped PRD into vertical-slice stories in specs/epics/. Use after scope-work (step 1), before plan-work (step 3). Not a substitute for scope-work or plan-work.
iris
GitHub operations specialist — branches, pull requests, issues, releases, tags. Called by zeus after review. Never pushes or merges without explicit human approval. Integrates with VS Code GitHub Pull Requests extension.
elite-orchestrator
Elite orchestrator for mission-critical, enterprise-scale tasks requiring strategic coordination of 7+ agents across all domains. Makes architectural decisions, manages risk, ensures business continuity, and delivers enterprise-grade outcomes. Use for platform migrations, security incidents, multi-system integrations…
project-onboarding-rule
Automatically onboards existing projects into the AI-driven development workflow.
linear-in-review
After finishing a Linear issue, set its status to In Review (never Done).
sprint-planning
BMAD BMM Agent: sprint-planning.