"System capable of integrating with ERP and other information system databases" matching MCP servers:

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Connects Claude Code, Codex, Cursor, and other coding agents to a project's idea, feature topology, and ordered task queue over MCP, so they can answer planning jobs, claim, build, and tick off implementation tasks live in your repository, or assemble a shareable PRD.

Feedback collection for AI agents. Create surveys from JSON schema, collect responses from groups of people, and retrieve structured results.

Enables AI assistants to identify and quantify value leaks in organizations using Melt's methodology, providing structured estimates instead of generic vendor lists.

An MCP server that reads a team's interviews, tickets, and docs to build a model of the product, exposing tools to ask questions, list contradictions and gaps, and draft documents, with all answers citing their source fragments.

Enables fair random picks, team splits and wheel spins inside chat assistants and other MCP clients, with results shown in chat. It also runs certified draws that seal the winner before the spin and publish a verifiable public proof page, so anyone can check the outcome.

Validates startup ideas with a deterministic scorecard, evidence brief, and verdict before code is written, integrating with MCP-aware build agents to avoid building dead-on-arrival products.

Enables natural language control of OmniPlan 4 on macOS, allowing creation and manipulation of tasks, resources, dependencies, and project metadata through MCP-compatible agents.

Lets an AI agent read a Pyxis dashboard's analytics — overviews, funnels, devices, acquisition, backend requests, visits and project settings — using a read-only access token created by an admin and revocable at any time. It runs locally over stdio, holds no state, and exposes only GET-style reads of the live Pyxis API.

Mines public App Store and Steam reviews to surface the complaints, rating drops per version, and common pain points across one app or a set of competitors, with no API keys required. Enables natural-language comparison of up to eight apps or games by country and category so users can spot product opportunities and gaps.

Connects Huawei AppGallery Connect operational data to any MCP-capable AI client, letting users analyze report trends across 16 report types, crash and AppFreeze metrics, review and rating insights, and generate Markdown daily or weekly digests. All 13 tools are strictly read-only, run with zero dependencies, and keep credentials in memory only.

Prevents premature AI coding by transforming vague product ideas into structured specifications, architecture decisions, and acceptance criteria through a series of interrogation and compilation tools.

An opinionated MCP server for App Store Connect that provides 13 curated tools, slash-command workflows, and a Claude Skill to manage apps, reviews, sales, and pre-submission audits via natural language.

Enables customer feedback management by integrating with Canny.io, supporting board listing, post retrieval, search, and creation.

An MCP server that enables AI tools to interact with the Lalaleap project management system using natural language, allowing creation and management of requirements, bugs, todos, and projects.

An AI-powered Product Management Operating System that provides tools for PRDs, user stories, prioritization, competitor analysis, roadmaps, KPIs, meeting notes, and product strategy via MCP clients.

Enables management of Azure DevOps work items including Epics, Features, User Stories, Tasks, and Bugs through natural language. Supports CRUD operations, WIQL queries, work item relationships, and retrieval of project metadata such as iterations and area paths.

MCP server that transforms Voice of Customer data into actionable customer experience insights through structured analysis and synthesis.

TruePPM MCP servers ask real questions of your self-hosted TruePPM instance: the critical path, a Monte Carlo slip forecast, sprint status, the risk register, My Work. Answers are computed server-side by the same CPM/Monte Carlo engine the web UI uses, never guessed by a model, and nothing leaves your box.

Enables reading and editing Avo tracking plans through natural language, with skills for designing new plans and working with existing ones.