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 instructions/dp065u/staad-mcp/agents-mdgit clone --depth 1 https://github.com/DP065U/STAAD-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/instructions/dp065u/staad-mcp/agents-md)<a href="https://agentmods.dev/instructions/dp065u/staad-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/dp065u/staad-mcp/agents-md.svg" alt="Measured on agentmods" 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 | $0.00880 | $0.00880 |
| Opus 5 | $0.00440 | $0.00440 |
| Sonnet 5 | $0.00176 | $0.00176 |
| Haiku 4.5 | $0.00088 | $0.00088 |
Grade A, and why
STAAD-MCP AGENTS.md 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 4d 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.
This is a copy
91% identical to openstaad-mcp AGENTS.md — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Project Overview
MCP server bridging AI agents to Bentley STAAD.Pro via the OpenSTAAD COM API.
Windows-only (pywin32/COM). Python 3.11+. See README.md for full details.
Build & Test
pip install -e ".[dev]" # Install with dev dependencies
ruff check . ; ruff format --check . # Lint + format check
ruff check --fix . ; ruff format . # Auto-fix
pytest # Unit tests (any OS, no STAAD needed)
pytest -m integration -v # Integration tests (Windows + STAAD running)
Architecture
| Component | Purpose |
|---|---|
main.py |
CLI entry point, transport setup (stdio/HTTP) |
server.py |
MCP tool definitions (discover_api, read_skills, execute_code, get_status) |
connection.py |
Multi-instance management via Windows ROT scan, STA-thread COM dispatch |
sandbox/executor.py |
AST-validated sandboxed exec() with stdout/stderr capture |
sandbox/com_proxy.py |
COM object proxy — blocks internal attrs, validates file paths |
sandbox/ast.py |
AST validation, format-string bypass detection, last-expr rewriting |
sandbox/const.py |
Allowlists for builtins, exceptions, and module attributes |
sandbox/path_validator.py |
Blocks writes to protected dirs, detects UNC paths (NTLM relay prevention) |
http_middleware.py |
SecFetchMiddleware — blocks cross-origin browser requests |
skills.py |
Skill discovery from staad_skills/ with YAML frontmatter extraction |
Conventions
- Line length: 120 characters
- Linter/formatter: Ruff (Black-compatible). Config in pyproject.toml
- Ruff rules:
E, F, W, I, UP, B, SIM, RUF(ignoringE501,RUF200) - Tests: pytest + pytest-asyncio (
asyncio_mode = "auto"). Mark integration tests with@pytest.mark.integration - 100% test coverage expected for new code
- All async tests use
pytest-asyncioauto mode — no manual event loop setup - Limit the number of if-else branches: Use polymorphism (or equivalents) when possible.
- Single responsibility: Aim for functions and classes with a single responsibility.
- Data validation: Use Pydantic models when runtime validation is needed. For static type checking, use type hints.
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.
- 4d ago First seen · 65 lines · 880 tokens per session scan A 8dacfc7dd11d
STAAD-MCP AGENTS.md is an instructions file published in the GitHub repository DP065U/STAAD-MCP (0 stars, last pushed 1mo ago), licensed MIT. It adds 880 tokens to every session, about $0.0044 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to openstaad-mcp AGENTS.md, differing in 2 lines, and is treated as a copy.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.