Loading…
Store and retrieve text in a Qdrant vector database by similarity search.
io.github.CloudCorpRecords/vectorsearch is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 97/100. This catalog entry is the indexed repository CloudCorpRecords/vectorsearch. The command is the string recorded from the source. It is not proof of a one-click install in every client. An MCP server exposes callable tools to a client. An agent skill is an instruction package. This entry is an MCP server record. 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. Review the repository push date and repository status before adding io.github.CloudCorpRecords/vectorsearch to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
npx -y @reneturcios/vectorsearch@0.1.0The command is the string recorded from the source. It is not proof of a one-click install in every client.
Review the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-10-30T22:11:39.134932Z",
"publishedAt": "2026-10-30T22:11:39.134932Z",
"statusChangedAt": "2026-10-30T22:11:39.134932Z"
}
},
"server": {
"name": "io.github.CloudCorpRecords/vectorsearch",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "0.1.0",
"packages": [
{
"version": "0.1.0",
"transport": {
"type": "stdio"
},
"identifier": "@reneturcios/vectorsearch",
"runtimeHint": "npx",
"registryType": "npm",
"registryBaseUrl": "https://registry.npmjs.org",
"packageArguments": [
{
"type": "positional",
"value": "mcp"
}
]
},
{
"transport": {
"type": "stdio"
},
"identifier": "ghcr.io/cloudcorprecords/vectorsearch:0.1.0",
"runtimeHint": "docker",
"registryType": "oci",
"packageArguments": [
{
"type": "positional",
"value": "mcp"
}
],
"runtimeArguments": [
{
"name": "--rm",
"type": "named"
},
{
"name": "-i",
"type": "named"
}
]
}
],
"repository": {
"url": "https://github.com/CloudCorpRecords/vectorsearch",
"source": "github"
},
"description": "Store and retrieve text in a Qdrant vector database by similarity search."
}
}