This server provides a hallucination detection service that verifies a draft text against a source of truth, identifying unsupported factual claims.
Core capability: Submit a source (trusted reference) and a draft (text to check) to receive a structured JSON report ({passed, failures, uncertain, mode}) with a pass/fail verdict and details on every flagged claim — including claim, reason, source_fact_checked, category, and severity.
Additional options:
Two modes: sampling (default, uses the host model via MCP — no API key needed) or api (uses a pinned Anthropic model directly when ANTHROPIC_API_KEY is set, benchmarked at 97.9% precision / 98.5% F1).
Reliability mode: Set consistency to N (e.g., 3) to run verification multiple times, keeping only majority-confirmed failures and flagging unstable ones as uncertain.
Pipeline-friendly: The machine-readable JSON output is suitable for CI/CD, content review, or document generation workflows.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-verify Verify this text against the provided source."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A benchmarked hallucination detector. Give it a SOURCE (the source of truth) and a
DRAFT; it returns the specific claims the source doesn't support, each with
{claim, reason, source_fact_checked, category, severity}.
Benchmarked: 106 labeled cases across 4 domains + an adversarial red-team tranche. Over 3 independent runs: precision 97.9% [96.6, 99.1], recall 99.1%, F1 98.5% (95% CIs) — with every residual failure adjudicated and documented. See BENCHMARK.md.
from mcp_verify import build_default_client, verify
client = build_default_client() # reads ANTHROPIC_API_KEY
report = verify(client, source="...source of truth...", draft="...text to check...")
for f in report.failures:
print(f.severity, "|", f.claim, "—", f.reason)
# Reliability mode: run N times, keep only majority-confirmed claims;
# unstable ones land in report.uncertain instead of report.failures.
report = verify(client, source="...", draft="...", consistency=3)One tool, verify(source, draft, consistency=1), over stdio:
pip install mcp-verify # or: pip install -e . from a checkout
claude mcp add verify -- mcp-verifyTwo modes, auto-selected (MCP_VERIFY_MODE=api|sampling overrides):
sampling (default, no key): the server asks the HOST's model to do the verification via MCP sampling — no ANTHROPIC_API_KEY, no extra cost.
api: if ANTHROPIC_API_KEY is set in the server's environment, it calls
the pinned benchmark model directly — the exact path BENCHMARK.md measures.
claude mcp add verify -e ANTHROPIC_API_KEY=sk-... -- mcp-verify
Both return the same JSON: {"passed", "failures": [...], "uncertain": [...], "mode"}.
Related MCP server: Grounding Enforcer
The eval suite (106 labeled cases across 4 domains plus a red-team tranche, a 15-type hallucination taxonomy, and a confusion matrix) travels with the detector. See BENCHMARK.md.
# Offline checks (no API key, no cost):
bash check.sh # lint + full test suite
python -m eval.analysis --sample # failure analysis on a sample report
# Live eval (needs ANTHROPIC_API_KEY):
python -m eval.run # full suite
python -m eval.run --smoke # 5 representative cases
python -m eval.run --judge # LLM-judge matcher (meaning, not tokens)
python -m eval.ablation # model/thinking/token-budget ablationUnclaimed servers have limited discoverability.
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