Loading…
Context Limiter & Output Vetter for context bloat. It is a highly specialized, structure-aware JSON built specifically to intercept and compress MCP responses before they annihilate your LLM's context window.
clov-ai is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 30/100. Context Limiter & Output Vetter for context bloat. It is a highly specialized, structure-aware JSON built specifically to intercept and compress MCP responses b. 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 clov-ai to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/alexandephilia/clov-ai.gitReview the upstream source before running this command.
Downloads a script and pipes it straight into a shell, executing remote code with no review.
Curl)
curl -fsSL https://raw.githubusercontent.com/alexandephilia/clov-ai/refs/heads/main/install.sh | shDispute this flag?Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "alexandephilia/clov-ai"
}Context Limiter & Output Vetter for context bloat. It is a highly specialized, structure-aware JSON built specifically to intercept and compress MCP responses before they annihilate your LLM's context window.