io.github.arkheiaai/mcp-server
Detect fabrication and hallucination in any LLM output. 30+ models supported.
Open source Open in the app JSON README (API)
About
Detect fabrication and hallucination in any LLM output. 30+ models supported.
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- arkheiaai
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.4
- Stars
- 1
- Open pull requests
- 1
- Last push
- 2026-09-06T18:55:04Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:02:26
- Updated
- 2026-08-29 03:02:26
- Origin id
io.github.arkheiaai/mcp-server
README
# 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
```bash
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
```bash
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`:
```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\`):
```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"
}
}
}
}
```
### 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.
For local Enterprise Proxy audit-log retrieval, set `ARKHEIA_PROXY_AUTH_TOKEN` to a proxy JWT session token. This is distinct from `ARKHEIA_API_KEY`: the MCP client sends it only to `ARKHEIA_PROXY_URL` as `Authorization: Bearer ...` for protected local endpoints such as `arkheia_audit_log`.
## 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 equivalent agent instruction file) to make fabrication detection automatic across every conversation:
```markdown
# Fabrication Detection
This project uses Arkheia for runtime fabrication detection.
The arkheia_verify MCP tool is available in every conversation.
## Verification Protocol
Before presenting any substantive response to the user:
1. Call arkheia_verify with the model name, prompt, and response
2. Check the risk field in the result
### Risk Handling
- LOW: Present normally
- MEDIUM: Present with caveat — "Detection flagged medium confidence. Key claims should be verified."
- HIGH: Do not present as-is. Investigate the specific claims. If unverifiable, regenerate or escalate.
### Sub-Agent Outputs
When spawning background agents or parallel workers:
- Verify each agent's output independently before merging
- A HIGH risk from any agent blocks the merge until investigated
- Log all detection results for audit
### What NOT to Do
- Do not skip verification because the response "looks correct"
- Do not suppress HIGH findings — the user needs to know
- Do not retry the same prompt expecting a different risk score
```
A ready-to-use template file is available at [CLAUDE_MD_TEMPLATE.md](CLAUDE_MD_TEMPLATE.md).
## Multi-Agent Quorum Pattern
If you use multiple AI agents (Claude + Codex, Gemini + Grok, etc.), detection becomes your quality gate:
```
1. Draft agent generates a response
2. arkheia_verify screens the response → risk score
3. If LOW: accept
4. If MEDIUM: second agent reviews the specific claims
5. If HIGH: regenerate with a different model, or flag for human review
```
This catches fabrication that individual agents miss. The draft agent is confident. The detection layer is objective. The review agent has context. Together they produce higher quality output than any single agent.
## What the Risk Levels Mean
| Risk | What it means | What to do |
|------|--------------|------------|
| **LOW** | Response fingerprint is consistent with grounded content | Use normally |
| **MEDIUM** | Some statistical signals triggered — the model may have interpolated or substituted | Review key claims. Check references, API names, version numbers. |
| **HIGH** | Strong evidence of fabrication — multiple detection signals agree | Don't trust this output. Verify everything. Consider regenerating. |
| **UNKNOWN** | No detection profile for this model yet | [Let us know](mailto:dmurfet@arkheia.ai) — we'll add it |
## Model Coverage
35+ models with detection profiles:
- **OpenAI:** GPT-4o, GPT-5.4, GPT-5-Codex family
- **Anthropic:** Claude Opus 4.6, Sonnet 4.6, Haiku 4.5
- **Google:** Gemini 2.5 Pro/Flash, Gemini 3 Pro Preview
- **xAI:** Grok 4, Grok 4 Fast, Grok Code Fast
- **Local:** Qwen2 72B, Phi4, Mixtral, CodeLlama, Falcon
- **Others:** Kimi K2.5, Ouro
If your model isn't listed, [let us know](mailto:dmurfet@arkheia.ai) and we'll characterise it. We add new models regularly.
## Direct API Access
The MCP server provides the richest detection because it captures the full inference signal during model calls. If you have a specific workflow where you need to call the detection API directly (CI/CD pipelines, custom orchestrators, batch processing), the REST endpoint is available:
```
POST https://arkheia-proxy-production.up.railway.app/v1/detect
```
Direct API calls without inference data provide structural analysis only. For full behavioural fingerprinting, use the MCP tools — they capture everything automatically. If you're building a custom integration and want full detection quality, [get in touch](mailto:dmurfet@arkheia.ai) and we'll help you set it up.
## MCP Tools
| Tool | Description |
|------|-------------|
| `arkheia_verify` | Score a model response for fabrication risk |
| `arkheia_audit_log` | Review your detection history |
| `run_grok` | Call Grok + screen for fabrication |
| `run_gemini` | Call Gemini + screen for fabrication |
| `run_ollama` | Call local Ollama model + screen |
| `run_together` | Call Together AI (Kimi, DeepSeek) + screen |
## Pricing
| Plan | Price | Detections | Concurrent |
|------|-------|------------|------------|
| Free | $0 | 1,500/month | 5 |
| Single Contributor | $99/month | Unlimited | 5 |
| Professional | $499/month | Unlimited | 20 |
| Team | $1,999/month | Unlimited | 50 |
No credit card for free tier. Upgrade when you're ready.
## Feedback
We built this because we needed it. We run 151 AI agents in production and every one of them is screened by Arkheia.
If you're using it — whether you love it, hate it, or wish it did something different — we want to hear from you:
- **GitHub Issues:** https://github.com/arkheiaai/arkheia-mcp/issues — bugs, feature requests, questions
- **Email:** dmurfet@arkheia.ai — anything at all
Every message is read. Every piece of feedback shapes what we build next.
## Requirements
- Python 3.10+
- Node.js 18+ (for npx install)
- Git
## Links
- Website: https://arkheia.ai
- GitHub: https://github.com/arkheiaai/arkheia-mcp
- Support: dmurfet@arkheia.ai