Tooluniverse runs on your own machine — the client starts it, so there is no endpoint to ping. 14 277 installs a week from pypi. Last commit 1 Nov 2026.
2,700+ scientific tools for AI scientists: life science, research, literature, and more.
We read the source, 22 h ago · rules 995398eba69f
A value the model can set ends up inside a file or shell call. That is not a flaw by itself — for a terminal server it is the job — but it is where things go wrong when it is not.
masks = model.eval(img, diameter=diameter, channels=channels)[0]
What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.
setup.py: выполняет код при установке пакета
result = subprocess.run(
output_file = os.path.join(output_dir, filename)
cache_data = pickle.load(f)
exec(source_code, module.__dict__)
"sequence": "NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN…
return argv, dict(os.environ)
Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.
That is not a flaw by itself — but it is where things go wrong when it is not the job. We re-read this code on every release. Watch it and you hear from us the day another one appears.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add tooluniverse -- uvx tooluniverse
{
"mcpServers": {
"tooluniverse": {
"command": "uvx",
"args": [
"tooluniverse"
]
}
}
}
[mcp_servers.tooluniverse]
command = "uvx"
args = ["tooluniverse"]
{
"mcpServers": {
"tooluniverse": {
"command": "uvx",
"args": [
"tooluniverse"
]
}
}
}
{
"servers": {
"tooluniverse": {
"type": "stdio",
"command": "uvx",
"args": [
"tooluniverse"
]
}
}
}
Optional settings from the registry entry, left out of the block above:
OPENAI_API_KEY (OpenAI API key for LLM-based tool finding and embedding features), ANTHROPIC_API_KEY (Anthropic API key for Claude-based features), GEMINI_API_KEY (Google Gemini API key for Gemini-based features), OPENROUTER_API_KEY (OpenRouter API key for accessing 100+ LLM models), AZURE_OPENAI_API_KEY (Azure OpenAI API key (alternative to OpenAI)), HF_TOKEN (HuggingFace token for accessing models and datasets), NCBI_API_KEY (NCBI/PubMed API key for higher rate limits (10 req/sec vs 3 req/sec). Register at https://www.ncbi.nlm.nih.gov/account/), SEMANTIC_SCHOLAR_API_KEY (Semantic Scholar API key for higher rate limits (100 req/sec vs 1 req/sec). Register at https://www.semanticscholar.org/product/api), BIOGRID_API_KEY (BioGRID API key for protein-protein interaction queries. Register free at https://webservice.thebiogrid.org/), DISGENET_API_KEY (DisGeNET API key for gene-disease association data. Register free at https://www.disgenet.org/), OMIM_API_KEY (OMIM API key for Mendelian disease data. Register at https://omim.org/api), ONCOKB_API_TOKEN (OncoKB API token for precision oncology annotations. Register at https://www.oncokb.org/apiAccess), NVIDIA_API_KEY (NVIDIA NIM API key for AlphaFold2 structure prediction, molecular docking, and genomics tools. Get key at https://build.nvidia.com), USPTO_API_KEY (USPTO API key for patent data access. Register at https://developer.uspto.gov/), UMLS_API_KEY (UMLS API key for medical terminology and concept mapping. Register at https://uts.nlm.nih.gov/uts/), BRENDA_EMAIL (BRENDA enzyme database login email. Register at https://www.brenda-enzymes.org/), BRENDA_PASSWORD (BRENDA enzyme database login password).
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