AI & Machine Learning
axenomia.cl
MCP server that exposes AX-SCI-001 agent passport, human-review gate, and traceable scientific evidence assessment and synthesis for research questions.
ENDPOINT 1
https://axenomia.cl/mcp
MCP server metadata
- Name
- AxenomIA AX-SCI-001
- Version
- 1.0.0
Known tools 3
axenomia_agent_passportRead the versioned AX-SCI-001 Agent Passport: scope, evidence rules, evaluation status, limitations and human-review requirements.
Inferred read-onlyaxenomia_human_review_gateReturn the mandatory human-review boundary for AX-SCI-001.
Inferred read-onlyax_sci_001_assess_evidenceResearch and synthesize traceable scientific evidence for a research question.
Inferred read-onlyCONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.axenomia-ax-sci-001]
url = "https://axenomia.cl/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"axenomia-ax-sci-001": {
"type": "http",
"url": "https://axenomia.cl/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: axenomia-ax-sci-001
Remote MCP URL: https://axenomia.cl/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"axenomia-ax-sci-001": {
"url": "https://axenomia.cl/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"axenomia-ax-sci-001": {
"type": "http",
"url": "https://axenomia.cl/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "axenomia-ax-sci-001",
"transport": "streamable-http",
"url": "https://axenomia.cl/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
TRUST AND VERIFICATION EVIDENCE
Loading Trust v2 evidence…
Checking the associated registrable domain. The BuiltWith key remains server-side.
Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.