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Deterministic stock screening, backtesting, and factor analysis for AI trading agents
QuantContext is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 67/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding QuantContext to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
uvx quantcontext-mcp==0.1.2Review the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-03-08T16:36:06.358323Z",
"publishedAt": "2026-03-08T16:36:06.358323Z",
"statusChangedAt": "2026-03-08T16:36:06.358323Z"
}
},
"server": {
"name": "io.github.zomma-dev/quantcontext",
"title": "QuantContext",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "0.1.2",
"packages": [
{
"version": "0.1.2",
"transport": {
"type": "stdio"
},
"identifier": "quantcontext-mcp",
"runtimeHint": "uvx",
"registryType": "pypi"
}
],
"repository": {
"id": "1174581808",
"url": "https://github.com/zomma-dev/quantcontext-mcp-server",
"source": "github"
},
"websiteUrl": "https://quantcontext.ai",
"description": "Deterministic stock screening, backtesting, and factor analysis for AI trading agents"
}
}