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Audit financial data quality and evaluate AI inference, bias, and KPIs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "findata-mcp" yet — see the docs or source repo.
Audit this financial dataset for data quality issues. Identify missing values, outliers, duplicate records, and abnormal field distributions, then provide remediation suggestions and a risk summary.
A data quality audit report with issue lists, outlier scores, risk notes, and remediation recommendations.
Evaluate this AI model’s inference results in a financial risk-control scenario. Detect bias across user groups, analyze accuracy, stability, and potential risks, and summarize improvement directions.
A model evaluation report including bias findings, key metrics, risk analysis, and optimization suggestions.
Using experiment results from two financial model versions, compare A/B test performance, measure key KPI changes, highlight significant differences, and generate a presentation-ready summary.
An A/B test analysis and KPI report with comparison conclusions, significance notes, and a presentation-ready summary.
Build production-ready AI tools with security, auditability, data quality, and testing.
Analyze MCP tool security risks, detect malicious behavior, and provide risk scores.
Validate AI-generated code with browser tests, evidence capture, and smart diagnostics.
Find and compare MCP servers for AI finance agent workflows.
Use safe MCP tools for calculation, search, drift checks, eval comparison, and grading.
Enable AI to safely analyze business metrics without improvising raw SQL.