Arkheia MCP Server — Fabrication Detection for AI Agents

Know when your AI is making things up.

Arkheia screens model responses for fabrication using behavioural fingerprinting. Works with Claude, GPT, Gemini, Grok, Llama, Mistral, and 30+ other models. One tool call. Real-time risk scoring.

Free tier: 1,500 detections/month. No credit card.

Install

npx @arkheia/mcp-server

The installer sets up a Python environment, clones the server, and configures everything. Takes about 60 seconds.

You'll need:

• Node.js 18+
• Python 3.10+
• An API key (free — see below)

Get an API Key

curl -X POST https://arkheia-proxy-production.up.railway.app/v1/provision \
  -H "Content-Type: application/json" \
  -d '{"email": "you@example.com"}'

Save the key. You won't see it again.

Add to Your Agent

Claude Code

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "arkheia": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "cwd": "~/.arkheia/mcp",
      "env": {
        "PYTHONPATH": "~/.arkheia/mcp",
        "ARKHEIA_API_KEY": "ak_live_your_key_here"
      }
    }
  }
}

On Windows, replace ~/.arkheia/mcp with C:/Users/YourName/.arkheia/mcp.

Claude Desktop

Add to your claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "arkheia": {
      "command": "python",
      "args": ["-m", "mcp_server.server"],
      "cwd": "~/.arkheia/mcp",
      "env": {
        "PYTHONPATH": "~/.arkheia/mcp",
        "ARKHEIA_API_KEY": "ak_live_your_key_here"
      }
    }
  }
}

Other MCP-Compatible Agents

Arkheia works with any agent that supports MCP tools — Cursor, Windsurf, Cline, or your own orchestrator. The configuration pattern is the same: point the MCP server at ~/.arkheia/mcp with your API key.

Restart your agent after adding the configuration.

Your First Detection

Once installed, ask your agent:

"Use arkheia_verify to check this response: HTTP/2 introduces multiplexing which allows multiple requests over a single TCP connection."

You should see a LOW risk result — that's a truthful response.

Now try a fabricated one:

"Use arkheia_verify to check this response: The Kafka 4.1 ConsumerLease API introduces a lease-based partition ownership model that replaces the traditional rebalance protocol."

You should see a HIGH risk result — the Kafka 4.1 ConsumerLease API doesn't exist. Arkheia caught it.

Test Prompts to Try

These exercise different detection scenarios. Run them through your agent to see how it handles each:

Truthful (should score LOW):

• "Use arkheia_verify on: Docker caches each Dockerfile layer. Unchanged layers are reused. This is why COPY order matters."
• "Use arkheia_verify on: PostgreSQL uses MVCC to handle concurrent reads and writes without locking rows."
• "Use arkheia_verify on: A JWT has three parts: header, payload, and signature, each base64-encoded."

Fabricated (should score HIGH):

• "Use arkheia_verify on: The GraphQL Federation 3.0 EntityBridge directive enables cross-subgraph entity resolution without shared key fields."
• "Use arkheia_verify on: Docker BuildKit 3.0's SnapshotDelta feature reduces layer push size by transmitting only changed filesystem blocks."
• "Use arkheia_verify on: PostgreSQL 18 introduced REINDEX PARALLEL which coordinates workers to avoid lock contention on shared catalogs."

Ask Your Agent What It Thinks

Try this — it's genuinely interesting:

"You now have access to arkheia_verify for fabrication detection. How would you use this to improve the quality of your own outputs? Try verifying one of your own responses."

Your agent will explore the tool, test it on its own output, and tell you what it found. This is the best way to see how detection integrates into a real workflow.

Add Detection to All Your Projects

Copy this into your project's CLAUDE.md (or equ

... View full README on GitHub