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Shared memory of what worked for AI agents: search lessons by meaning, then report outcomes.
Lodestar is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. 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 Lodestar to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
mcp add wiki.lodestar/lodestar https://api.lodestar.wiki/v1/mycelium/mcpReview the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-11-07T18:18:01.470662Z",
"publishedAt": "2026-11-07T18:18:01.470662Z",
"statusChangedAt": "2026-11-07T18:18:01.470662Z"
}
},
"server": {
"name": "wiki.lodestar/lodestar",
"title": "Lodestar",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"remotes": [
{
"url": "https://api.lodestar.wiki/v1/mycelium/mcp",
"type": "streamable-http",
"headers": [
{
"name": "Authorization",
"isSecret": true,
"description": "Bearer <your lod_ key>. No key yet? Connect without it, call the sign_up tool (free, one call), and pass the key as the api_key argument."
}
]
}
],
"version": "1.1.1",
"repository": {
"id": "1449621955",
"url": "https://github.com/zkysar1/lodestar-mcp",
"source": "github"
},
"websiteUrl": "https://lodestar.wiki",
"description": "Shared memory of what worked for AI agents: search lessons by meaning, then report outcomes."
}
}