Advanced Fraud Detection MCP (success-meta-1/fraud-detection-mcp) is an MCP server listed on the M8ven Trust Index. It scores 74 out of 100, grade C. It declares 28 tools. No publisher has claimed this listing.

Back to MCP Trust IndexM8ven
C
Emerging
74/100

Advanced Fraud Detection MCP

Detects financial fraud and AI agent transaction risks using machine learning, behavioral biometrics, network graph analysis, and agent-to-agent protection.

→ success-meta-1/fraud-detection-mcp· listed on glama

Emerging. No concerning findings. Grades remain capped until the project builds reputation through adoption. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

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Who stands behind it

success-meta-1

Source: Glama

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Install from

Glamanot scored here

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
✅
Open source with a license and README
Anyone can audit the code, the license is declared, and the publisher documents what it does.
// tools this server exposes28 tools

These names and descriptions are the publisher's own, read from the source code. We print them as written. Our assessment is the findings above, not this list.

analyze_transaction

Comprehensive transaction fraud analysis with optional behavioral biometrics.

detect_behavioral_anomaly

Analyze behavioral biometrics for anomaly detection.

assess_network_risk

Analyze network patterns for fraud ring detection.

generate_risk_score

Generate comprehensive risk score combining all analysis methods.

explain_decision

Provide explainable AI reasoning for fraud detection decisions.

classify_traffic_source

Classify whether a transaction originates from a human, AI agent, or unknown source.

verify_agent_identity

Verify an AI agent's identity using available credentials.

analyze_agent_transaction

Analyze an AI-agent-initiated transaction for fraud.

verify_transaction_mandate

Check whether a transaction falls within an agent's authorized scope.

detect_agent_collusion

Detect coordinated agent behavior using graph analysis.

score_agent_reputation

Compute longitudinal reputation score for an AI agent.

verify_agent_signature

Verify an RFC 9421 HTTP Message Signature on an agent commerce request.

check_idempotency_key

Stripe-ACP-compatible Idempotency-Key check for agent transactions.

validate_nonce

Visa-TAP-compatible nonce replay PEEK (safe, non-mutating by default).

consume_nonce

Atomically check + record a nonce in the Visa-TAP 8-minute replay window.

analyze_batch

Analyze a batch of transactions for fraud detection.

get_inference_stats

Get inference engine statistics including cache performance metrics.

health_check

System health check with model status, cache stats, and system metrics.

get_model_status

Get current fraud detection model status and configuration.

train_models

Train fraud detection models using the ML training pipeline.

generate_synthetic_dataset

Generate a synthetic fraud detection dataset for testing and evaluation.

analyze_dataset

Analyze a stored dataset (CSV or JSON) for fraud patterns.

run_benchmark

Run a performance benchmark of the fraud detection pipeline.

assess_insider_threat

Run an insider threat assessment on a user's activity data per EO 13587 and NITTF guidance.

generate_siem_events

Export fraud and insider threat events in defense-grade SIEM formats.

evaluate_cleared_personnel

Run cleared personnel analytics per SEAD 4/6 for users with security clearances.

get_compliance_dashboard

Get defense compliance metrics and dashboard data.

generate_threat_referral

Generate a formal insider threat case referral or personnel security action report.

// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configFRAUD_DETECT_METRICS_PORT
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

28/28 tools missing one or more hints — analyze_transaction (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); detect_behavioral_anomaly (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint); assess_network_risk (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint), +25 more. OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 2 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/success-meta-1/fraud-detection-mcp)](https://m8ven.ai/mcp/success-meta-1/fraud-detection-mcp)
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commit: f518d57085565abe15886f778a610ca640d09886
code hash: f26ddf5ae4dda3944f268d9be63874434ffb4da97e8aff5d699b8052f4439f7a
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