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molclaw-goca-tool
Run GoCa coarse-grained protein MD pipeline and collect key simulation artifacts from a unified run directory.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Run GoCa coarse-grained protein MD pipeline and collect key simulation artifacts from a unified run directory.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | molclaw-goca-tool |
| description | Run GoCa coarse-grained protein MD pipeline and collect key simulation artifacts from a unified run directory. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Note:
drugsda-file-transfer before execution.drugsda-fix_pdb before execution./root/lwj/wll/code/drug/GoCa/GoCa.The description of tool goca_pipeline.
Runs GoCa coarse-grained setup and optional full MD workflow for protein structure relaxation and trajectory generation.
Args:
protein_pdb (str): Input protein PDB path, required.
full_md (bool): Whether to run EM, production MD, and post-processing, default True.
temperature (float): GoCa reduced temperature used for MD, default 45.0.
md_time (float): MD simulation length in ps, default 12000.0.
gpu_ids (str | None): Optional GROMACS GPU device IDs, default None.
dry_run (bool): Create tracked run directory and return normalized parameters without execution, default False.
Return:
status (str): success, partial_success, or error.
msg (str): Human-readable run summary.
output_dir (str): Run-specific directory under tool_result/goca_pipeline_result.
work_dir (str): Relative GoCa working directory under output_dir.
protein_pdb (str): Resolved input protein PDB absolute path.
full_md (bool): Effective full_md value used by wrapper.
temperature (float): Effective reduced temperature used by wrapper.
md_time (float): Effective MD time in ps used by wrapper.
gpu_ids (str | None): Effective GPU IDs used by wrapper.
dry_run (bool): Effective dry_run value used by wrapper.
key_files (dict): Key output files relative to output_dir.
analysis_dir (str | None): Analysis directory relative to output_dir when generated.
How to use tool goca_pipeline :
response = await client.session.call_tool(
"goca_pipeline",
arguments={
"protein_pdb": "/path/to/input.pdb",
"full_md": True,
"md_time": 1000.0,
"temperature": 45.0,
"gpu_ids": None,
"dry_run": False
}
)
result = client.parse_result(response)
key_output = result["output_dir"]
# 1) Main mode
{
"protein_pdb": "/path/to/input.pdb",
"full_md": True,
"md_time": 1000.0,
"temperature": 45.0,
"gpu_ids": None,
"dry_run": True
}
# 2) Variant mode
{
"protein_pdb": "relative/path/to/protein.pdb",
"full_md": False,
"md_time": 50000.0,
"temperature": 50.0,
"gpu_ids": "0",
"dry_run": False
}
After calling this tool, you MUST download all output structure files from the MCP server to the local workspace using server_file_to_base64. A tool call is NOT considered complete until its output files have been downloaded and verified locally (ls -la <file> — size must be > 0).
import base64, os
response = await client.session.call_tool(
"server_file_to_base64",
arguments={"file_path": result["output_file"]} # or relevant output field
)
dl = client.parse_result(response)
local_path = "stepNN_descriptive_name.ext"
with open(local_path, "wb") as f:
f.write(base64.b64decode(dl["base64_string"]))
assert os.path.getsize(local_path) > 0, f"Download failed: {local_path}"
Download policy: All structure output files are Category A (user-critical) — essential for user verification, downstream analysis, and reproducibility. When in doubt, download. Over-collection is always preferred over under-collection.
Specific files to download from GoCa output: All Cα trajectory PDB files, output configuration files. These are coarse-grained structures that will need full-atom reconstruction via molclaw-pulchura-rebuild and molclaw-pack-sidechains.
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