AsyncPraiseRebuke MCP Server

AsyncPraiseRebuke MCP Server

The AsyncPraiseRebuke-MCP server fills a niche that many AI‑enabled developers face: the ability to pull, filter, and enrich business feedback data from disparate sources while keeping that information centrally stored for future analysis. By exposing a collection of stateless tools and an agent‑driven workflow, it lets Claude Desktop or any MCP‑compliant client turn raw customer reviews and website data into actionable insights without writing custom integration code.

At its core, the server offers a set of reusable tools that can be invoked individually or composed into more complex sequences. Developers can list all public reviews, search for feedback by business name, or retrieve the highest and lowest rated establishments in a single call. A dedicated tool lets users discover business contact emails through web scraping and heuristics, while another logs those contacts into a Cosmos DB directory. This combination of search, discovery, and persistence gives teams an end‑to‑end pipeline for managing their business reputation data.

A standout feature is the discoverAndLogBusinessEmail agent. Rather than calling each tool separately, this agent orchestrates the entire process: it first scrapes a target website for contact information, checks the existing Cosmos DB directory to avoid duplicates, and finally logs new businesses with placeholder values when necessary. This automation removes the manual overhead that would otherwise be required to keep a contact database up‑to‑date, saving developers hours of repetitive work and reducing human error.

Use cases for this MCP server span a variety of real‑world scenarios. Customer support teams can quickly pull the latest negative reviews and route them to escalation workflows. Marketing analysts may aggregate top‑rated businesses in a region to identify industry leaders for partnership outreach. Small business owners can maintain an up‑to‑date directory of their own contacts or those of competitors, enabling targeted outreach campaigns. Because the server communicates via standard MCP endpoints, any AI assistant that understands the protocol can leverage these tools without needing custom connectors.

Integration into existing AI workflows is straightforward. A Claude Desktop user can add the server’s configuration to their settings, then issue natural‑language prompts that trigger specific tools or the agent. For example, “Find all reviews for Acme Corp and log any new emails” will automatically chain the search, discovery, and logging steps. The stateless nature of most tools means they can be reused across multiple sessions or combined with other MCP services, making the AsyncPraiseRebuke server a flexible building block in larger AI‑driven data pipelines.