"React Query" matching MCP servers:

A Python-based MCP server that enables AI agents to query the Microsoft 365 Roadmap programmatically.

An MCP server for accelerator programs to query, review, and manage startup applications via natural language. It enables searching applications by sector or impact score, fetching full application details with reviewer notes, listing pending reviews, and flagging applications for panel discussion.

Enables MCP clients to query the IndeCX public API for survey data, including actions, questionnaires, answers, invites, non-respondents, categories, blocklists, and branches.

A Model Context Protocol server for FeaturePulse feedback management, enabling AI assistants to query feature requests, analyze MRR impact, and manage product roadmaps through natural language.

Enables AI agents to query real-time SaaS pricing tiers, compare developer tools side-by-side, and retrieve recent product changelogs.

MCP server for reading public Featurebase feedback boards, enabling agents to query user feedback, posts, comments, and statistics.

Model Context Protocol server that enables AI assistants to query and analyze project feedback and bug reports from FeedbackBasket projects, with filtering by category, status, sentiment, and search capabilities.

Enables AI assistants to query and segment event data from Amplitude Analytics via the MCP protocol.

Enables AI assistants to query live App Store and Google Play data — keyword demand, rank history, top charts, competitor sets, reviews and ratings — across 100+ countries, directly inside a chat instead of a dashboard. Exposes 35 read-only tools covering app search, ASO keyword profiling, ranking trajectories, and worldwide market comparisons through a remote OAuth-authenticated endpoint.

Enables AI agents to query Mixpanel event data, funnel conversion rates, and retention cohorts via MCP tools or natural language.

Enables AI applications to query customer feedback, customer profiles, and analytics through the Medallia Query API.

Enables Claude Code to query a trained sentiment model and Amazon Electronics review analytics through typed, read-only tools. Supports sentiment classification, statistics, keyword and similar-review search, product summaries and comparisons, negative aspect analysis, and model information.

Enables autonomous agents to calculate dynamic price elasticity of demand and derive marginal revenue maxima from query inputs. It returns structured JSON outputs with execution telemetry for economic modeling and pricing optimization.

Enables users to query and compare luxury car brand insights from owner reviews, including average ratings, common complaints and praises, rating trends, and review searches via six deterministic tools.

Query PainSpotter to discover validated business opportunities and trending pain points mined from Reddit, Hacker News, and Product Hunt.

Enables MCP-compatible assistants to query, analyze, and summarize Playloop playtest data such as sessions, builds, insights, funnels, crashes, feedback, and experiments through typed tools.

Enables AI agents to query SensorTower's APIs for mobile app market intelligence, including download and revenue estimates, store leaderboards, App Store featuring and ASO keywords, reviews, Apple Search Ads, and audience insights across roughly 76 endpoints. It normalises the API's unit, shape, and error quirks so results arrive clean, capped for model context, and with actionable error hints.

MCP server that provides demand-signal scanning tools from GitHub, Hacker News, npm, and PyPI to validate digital-product and dev-tool ideas. Enables agents to query repo signals, trends, and download counts via stdio JSON-RPC.

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.

Turns the five Playing to Win boxes into queryable data over MCP: list all positions, open one position with its supporting, opposing, and complicating evidence, adopt or reject it with a note, record new evidence, add positions into a box, and organize several cascades of boxes. Read and write tools share the same core and query layer as the CLI and web pages, so every surface stays consistent.