"Product Hunt" matching MCP servers:

Provides a governed analytics layer over synthetic product data, exposing MCP tools for investigating product metrics, slicing by dimensions, and comparing periods while preventing unrestricted SQL access.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

Enables local, read-only product-data checks for reviewing product records, comparing feed snapshots, and comparing dimensions against source rules, returning match or hold decisions without fetching references or modifying feeds.

Enables AI agents to query self-hosted product analytics, investigate changes with evidence and coverage checks, and use optional management tools for saved reports, goals, and keys.

A Model Context Protocol server enabling product searches across e-commerce platforms, price history tracking, and product specification-based searches using natural language prompts.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

Remote MCP server for read-only Sorftime Enterprise API access, enabling product, category, and keyword data queries with per-user JWT authentication and audit logging.

Enables AI assistants to query product footprints, inspect transport activity data, and submit events from iLEAP-conformant endpoints through natural language, using MCP tools over Streamable-HTTP or stdio.

Enables asking product analytics questions in English and receiving answers as real SQL along with the resulting numbers, through read-only MCP tools for funnels, retention, trends, paths, and event counts.

Enables agents to explore a project's captured events and properties, then run trend, funnel and retention analyses and create or list saved insights. Tools are model-oriented, returning trimmed, budgeted results and actionable errors, with the project resolved from the API key or bearer token rather than passed as an argument.

Enables AI assistants to manage SAP Business Data Cloud operations including data shares, Delta Sharing, and data product publishing through an MCP interface.

Transforms high-level business intents into structured Data Product Requirement Prompts through AI-powered conversational refinement. Guides users through clarifying questions to gather comprehensive requirements for automated Business Intelligence dashboard generation.

Exposes product analytics metrics from an internal data warehouse via MCP, enabling agents to query metrics, product summaries, and event breakdowns.

An MCP server that enables Claude Desktop to interact with Toast restaurant data, including sales summaries, top items, and product mix analysis.

Enables manufacturing traceability queries and analysis through GraphRAG, supporting semantic search, graph traversal, natural language to Cypher, defect chain retrieval, requirement traceability, and product health dashboards.

Enables validation of Shopify product CSV exports before import, detecting duplicate or whitespace-colliding SKUs, missing variant data, repeated option combinations, and conflicting metadata, returning a SHA-256-bound JSON diagnostic report.

Normalizes messy product catalog attributes and maps products to taxonomies like Google and Shopify, with retrieval-grounded category classification.

Enables interaction with Real Canadian Superstore to extract order history, browse products, and export purchase data. Supports authentication via bearer token and provides comprehensive order management and product discovery capabilities.

Web extraction MCP server for AI agents. Extract structured data from any URL with built-in Cloudflare bypass, JavaScript rendering, and intelligent parsing. Returns clean markdown or JSON.

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.