# Datashake MCP > Datashake MCP lets users ask an AI assistant for review and social data in plain English. It returns structured results from over 10 social networks for direct analysis. This tool is for analysts and teams who need on-demand brand mentions without... - Type: AI tool - Page: https://completeaitraining.com/ai-tools/datashake-mcp/ - Published: 2026-11-03 - Updated: 2026-11-05 - Author: Jeroen Erne - Jobs: Marketing, PR and communications, Management, IT and development - Topics: Analytics, MCP (Model Context Protocol), Research, Productivity, API - Reading time: 2 min ### About Datashake MCP Datashake MCP is a connector that lets users pull public review and social media data directly into their AI assistant using plain English queries. It builds on Datashake's existing API, which aggregates structured data from over 160 sources across social networks and review platforms. The MCP (Model Context Protocol) integration means the data arrives already clean and structured, ready for follow-up questions in the same chat thread. ### Review Datashake MCP addresses a specific workflow bottleneck: the gap between knowing review or social data exists and actually getting it into a format where someone can ask questions about it. Instead of requiring a developer to fetch data, a place to store it, and a dashboard to visualize it, the MCP routes the data straight into a conversational interface. The tool is in its launch week, so real-world usage patterns and edge cases are still emerging. #### Key Features - Plain English queries to retrieve mentions, reviews, and social posts from 160+ sources - Structured data output that integrates with AI assistants supporting the Model Context Protocol - Same-chat follow-up capability for drilling into unexpected findings or anomalies - Free tier with 20 searches available at signup #### Pricing and Value Datashake MCP launched with a free option that includes 20 searches. The product page does not detail pricing beyond this free tier, so the cost structure for ongoing use or higher volumes is not yet defined publicly. Users evaluating it for regular workflows will need to inquire about limits and paid plans directly. #### Pros - Reduces the time from question to structured answer compared to manual data export and dashboard setup - Works across review sites and social networks, not siloed to one platform category - Lets users interrogate data iteratively without pre-defining metrics or charts - No separate storage or technical setup required on the user's side for basic queries #### Cons - Pricing beyond the 20 free searches is not publicly documented, making cost projection difficult - Relies entirely on the AI assistant's ability to interpret queries correctly; ambiguous prompts may return unexpected results - Not well suited for teams that need scheduled, automated reporting or dashboard-style monitoring without manual prompting The MCP appears most practical for analysts, product researchers, and marketers who need to spot-check public sentiment or compare review and social narratives on demand. It fits ad-hoc investigative workflows where the question isn't fully formed until the first batch of data arrives. Teams with heavy, recurring reporting needs may find the prompt-based model less efficient than a dedicated dashboard until usage limits and automation options become clearer. --- This is the Markdown form of https://completeaitraining.com/ai-tools/datashake-mcp/. The whole site is mapped at https://completeaitraining.com/llms.txt.