"Answer" matching MCP servers:

Exposes a fictional B2B CRM database (companies, contacts, deals) as callable MCP tools, enabling AI agents to answer natural-language questions about company records, contacts, and pipeline data.

Enables an AI assistant to answer customer-success questions by synthesizing simulated billing, support ticket, and product usage data into composite churn-risk scores, bands, and plain-language explanations for fictional accounts. It exposes raw-data tools, a synthesis tool, and a methodology resource so users can list at-risk accounts or get a per-account health summary.

A read-only MCP server for the Little Green Light donor CRM. It lets Claude answer questions about donor data, gifts, and fundraising metrics by querying LGL directly.

Enables Claude to answer plain-English questions about a GTM engineer agent's own work — client ICPs, run history and costs, buying-signal alerts, lists, and ranked prospect fit scores — by reading the research files that agent writes, with every answer citing its source file. It also exposes one guarded action tool that starts a new prospect research run only after a cost preview, an explicit confirmation, and daily run/spend cap checks.

Read-only MCP server that lets Claude, ChatGPT, or Cursor answer pest-business questions from your GorillaDesk account in plain English—looks up customers, invoices, schedules, and technicians without creating or changing data.

Give your AI the full context of how your business runs: CRM, finances, tasks, and memory in one self-hosted MCP server." Short + long descriptions lead with cross-domain reasoning ("which customers are behind on payment and what did we last promise them" reads the ledger and CRM in one answer) and self-hosted data ownership (your own Supabase, stdio, MIT).

Enables AI assistants to answer growth questions in plain English by connecting to marketing and product data via 49 MCP tools.