findata-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| audit_data_qualityA | Audits a financial dataset for completeness, consistency, and machine-readability. Returns a quality score, field-level breakdown, and a list of remediation actions. |
| detect_biasB | Detects demographic or categorical bias in a financial dataset by comparing approval rates, average amounts, or outcome distributions across cohort groups. Returns disparity ratios and a bias risk label. |
| evaluate_model_inferenceA | Evaluates the accuracy of AI model inferences against ground-truth labels. Computes precision, recall, F1, AUC approximation, and a pass/fail verdict against enterprise performance thresholds. |
| score_outliersA | Scores each record in a financial dataset for outlier risk using Z-score and IQR methods across specified numeric fields. Returns per-record risk flags and a summary of flagged anomalies. |
| run_ab_comparisonB | Compares two model variants (A and B) on the same dataset using precision, recall, F1, and AUC. Returns a winner recommendation and statistical delta. |
| generate_kpi_reportB | Generates a structured KPI performance report for a financial data pipeline or AI agent run. Computes latency, throughput, error rate, and SLA adherence — formatted for senior stakeholder delivery. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool targets a distinct aspect of financial data analysis: data quality, bias detection, model evaluation, KPI reporting, A/B comparison, and outlier scoring. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., audit_data_quality, detect_bias), making naming predictable and easy to understand.
With 6 tools, the server is well-scoped for its domain of financial data evaluation and reporting. Each tool serves a clear purpose without unnecessary redundancy.
The tool set covers core evaluation and analysis tasks comprehensively. Minor gaps exist (e.g., no data ingestion or visualization tools), but these are likely out of scope for the server's intended function.