"author:GillMacedo" matching MCP servers:

Enables finding recurring charges, subscriptions, price increases, and possible double charges from a bank or card statement CSV, along with yearly cost estimates, without any bank login or uploads. It also inspects a CSV to show detected columns and row counts before analysis.

Enables checking OpenAI, Claude, Gemini and other LLM model IDs against a live catalog to identify deprecated or unknown names and suggest newer replacements. Also lets users search current models by provider, price, context size or tool support, and scan files or folders for outdated IDs.

Enables agents to find duplicate customers and contacts in CSV files by comparing emails, phone numbers, names, and companies, returning confidence levels and the evidence behind each match. It also inspects CSV files to reveal detected columns and row counts without exposing row contents.

Enables users and AI agents to reconcile CSV bank statements against invoices or bills by matching amounts and references, then sorting each line into matched, likely, review, or unmatched groups with reasons. It exposes read-only tools for matching files and inspecting detected CSV columns and row counts.

Enables analyzing sales and stock CSV files to identify what to reorder, how much to buy, when products will run out, and how much inventory is tied up in dead stock. Provides read-only inventory checks and CSV column inspection without uploading data.

Enables AI agents to analyze an orders CSV to identify at-risk and lost customers, estimate yearly revenue at risk, and report repeat-purchase rates and top-customer dependence. It also inspects CSV column structure, with read-only tools that keep data local and never contact customers.