"author:alphaparkinc" matching MCP servers:

Audits extracted balance sheets, income statements, and cash flow reports for mathematical consistency and formula balancing, reconstructing complex financial tables and resolving cross-references from document or OCR payloads. Also performs spatial layout parsing and PII redaction, returning structured matrices and validation telemetry over MCP.

Enables deterministic reconstruction of complex tables from OCR bounding-box payloads, recovering multi-column layouts, merged headers, and grid hierarchies. It also supports formula consistency auditing and sensitive PII redaction for document processing pipelines.

Enables multi-provider waterfall enrichment of B2B prospect data, combining firmographic details and verified decision-maker emails with automated fallback and deliverability filtering.

GenPark MCP server for normalizing and translating structured data schemas.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Enables AI agents and MCP clients to convert ASCII/OCR text dumps, aligned spacing, and CSV/TSV data into clean GitHub Flavored Markdown tables and typed JSON deterministically.

Enables agents to deterministically extract structured entities from input payloads and validate or coerce them against JSON schemas, returning a standardized response dossier with execution telemetry. It runs as a zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP clients.

Enables AI agents to compute and apply minimal JSON Patch (RFC 6902) operational diffs for atomic state synchronization between structured documents. Runs as a zero-dependency Python MCP server with deterministic outputs and sub-millisecond execution overhead.

Enables predictive B2B outbound buying propensity scoring by aggregating time-decayed hiring, funding, and technographic signals, then classifying accounts into outbound action tiers.