Groundlens MCP

MCP server for groundlens — LLM hallucination detection for Claude Desktop, Cursor, Windsurf, and any MCP-compatible client. No second LLM. Deterministic. Same inputs → same scores, every time.

One-click install

Related MCP server: Root Signals MCP Server

What it does

Adds three tools to your AI assistant:

SGI (Semantic Grounding Index) measures whether the response actually used the source material or just rephrased the question. Score > 0.95 = grounded.

DGI (Directional Grounding Index) measures whether the response follows geometric patterns typical of grounded answers. Score > 0.30 = grounded.

Install

pip install groundlens-mcp

Or with uv:

uv pip install groundlens-mcp

More clients

Claude Code (CLI):

claude mcp add groundlens -- uvx groundlens-mcp

Claude Desktop, Windsurf, Cline, or any MCP client — add to its config:

{ "mcpServers": { "groundlens": { "command": "uvx", "args": ["groundlens-mcp"] } } }

Configure your client

Claude Desktop

Add to your claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "groundlens": {
      "command": "groundlens-mcp"
    }
  }
}

If you installed with uv and the command isn't on your PATH:

{
  "mcpServers": {
    "groundlens": {
      "command": "uv",
      "args": ["run", "groundlens-mcp"]
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "groundlens": {
      "command": "groundlens-mcp"
    }
  }
}

Example with Cursor:

• Cursor self-verification loop — drop-in .cursor/ config + rule that makes Cursor verify every answer with Groundlens.

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "groundlens": {
      "command": "groundlens-mcp"
    }
  }
}

How to use

Once configured, ask your ai assistant:

"Check if this response is hallucinated"

"Is this answer grounded in the document I provided?"

"Run a hallucination check on this ChatGPT output"

The tools return JSON with a plain-language CHECK check, a numeric score, and the raw components. The wording comes from groundlens.check — the same source of truth used by the library and docs, so it reads identically everywhere.

Example output

{
  "check": "Not supported by the document",
  "message": "The answer stays closer to the question than to the source, so it may not come from the document. Check it before trusting it.",
  "headline": "CHECK: Not supported by the document (Semantic Grounding Index - SGI=0.87)",
  "level": "risk",
  "method": "Semantic Grounding Index",
  "score": 0.87,
  "flagged": true,
  "detail": "distance to source 0.49, distance to question 0.43"
}

The check level is ok / review / risk (from the calibrated thresholds). For context-free DGI checks the check reads Looks grounded / Partly grounded / Not grounded, plus a note that no source was provided.

How it works

groundlens uses embedding geometry — not a second LLM — to detect hallucinations:

• SGI computes dist(response, question) / dist(response, context). If the response moved toward the context, it's grounded. If it stayed near the question, the context was likely ignored.

• DGI projects the question→response displacement onto the mean direction of verified grounded pairs. Positive alignment = grounded pattern.

Both methods run a single embedding call. No model inference for evaluation. Deterministic.

First-call latency

The first tool call downloads and loads the sentence-transformer model (~100MB). Subsequent calls are fast. The model is loaded lazily so your MCP client doesn't slow down on startup.

Running from source

git clone https://github.com/groundlens-dev/groundlens-mcp.git
cd groundlens-mcp
pip install -e .
groundlens-mcp

Or:

python -m groundlens_mcp

Links

• groundlens library — pip install groundlens

• Documentation

• Website

• Demo