breedsim-mcp 是一个让 AI 代理运行 AlphaSimR 的 MCP 服务器 繁殖程序模拟。它返回复制的扩散，从不返回单一随机分布 运行，所以代理人可以判断选择方案会获得什么以及这种收益的确定性。

这有用吗？

是的，和单独的Claude相比。相比Claude自己写AlphaSimR脚本，它 但并未显著更准确，且成本更高。

克劳德作品5.5回答了34个繁殖模拟问题，每个问题分别由 AlphaSimR直接运行;名字最好的公式仅在34个服务器中出现15个。在这个服务器 Claude 答对了102题（满分102题），它自己的R和AlphaSimR题目是99题（非分开，完全正确） McNemar p = 1.0），单独102件中的51件（两种工具组合都超过了它，p< 1e-6）。这仍然需要花费 比克劳德自己的剧本还要多。2026年10月18日预注册重播，未发行 提交时，切断了服务器指令并四舍五入了输出，102个里的102个都对中了 这两种设置都将服务器的中位数每次回答成本从0.051美元降至0.033美元，但克劳德的 自制脚本价格为0.027美元，34题全部更便宜（P = 1E-10）。时间不分开。

breedsim-mcp

breedsim-mcp is an MCP server that lets AI agents run AlphaSimR breeding-program simulations. It returns the spread over replicates, never a single stochastic run, so an agent can say what a selection scheme would gain and how sure that is.

Why replicates

A single simulation run is not a result, and this API will not return one. Five seeds of an identical three-cycle program gave mean genetic gain [1.151, 1.841, 1.424, 1.429, 1.473], sd 0.247, on the very number being reported (measured once on AlphaSimR 2.1.0 while building v0.1.0, 2026-07-29). So run_program enforces a replicate floor and returns a per-cycle mean, sd and confidence interval. No flag collapses it to a point estimate.

Install

This needs more than uv pip install:

• R ≥ 4.3 with a shared library (libR.so)
• AlphaSimR, an Rcpp/RcppArmadillo compile that takes minutes
• libtirpc-dev, or rpy2 fails to link (cannot find -ltirpc)
• rpy2 <3.6, because 3.6 binds R_getVar, which needs R ≥ 4.4

sudo apt-get install -y r-base r-base-dev libtirpc-dev
R -e 'install.packages("AlphaSimR", repos="https://cloud.r-project.org")'
uv add breedsim-mcp

Do not use a conda Python: rpy2 then fails to load libR.so with GLIBCXX_3.4.30 not found, because conda ships an older libstdc++ than the system libicuuc requires. Use a system or uv-managed interpreter. Requires mcp 2.x.

Configure your MCP client

The server speaks stdio through the breedsim-mcp console script.

Claude Code

claude mcp add breedsim -- breedsim-mcp

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "breedsim": {
      "command": "breedsim-mcp"
    }
  }
}

If the executable is not on your PATH, or AlphaSimR lives in a user library, run it through uv and pass the library path:

{
  "mcpServers": {
    "breedsim": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/breedsim-mcp", "breedsim-mcp"],
      "env": { "R_LIBS_USER": "/home/you/R/library" }
    }
  }
}