"A resource for understanding Claude's context window and capabilities" 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.

Automates Cole Medin's context engineering methodology to generate high-quality Product Requirements Prompts (PRPs) by analyzing existing codebases, asking contextual questions, and synthesizing insights.

A Python MCP server that gives Claude access to PM workflow data such as sprints, roadmap, blockers, and workload, using synthetic fixture data (no API keys required) and can be forked for real Linear/Jira integration.

Enables conversion of business briefs or authenticated saved projects into structured handoffs for the Lovable plugin, with both anonymous basic mode and authenticated full mode including knowledge base, SEO, WCAG, security, and roadmap guidance.

It enables solo builders to automatically log product changes to PostHog and later get statistically honest verdicts on whether those changes moved key metrics, without needing enough traffic for A/B tests.

A Model Context Protocol server that provides Monte Carlo forecasting tools for project duration, cost, completion dates, and sensitivity analysis using three-point estimates and historical throughput.

Enables agents to evaluate whether a product-adoption message is worth sending by applying a fixed, evidence-based rubric to a draft, recipient context, evidence, and the agent's own assessment, returning a send, revise, or hold recommendation with a score and reasons.

Provides market-intelligence tools for researching AI employee opportunities across Egypt, Saudi Arabia, and UAE using Meta ads evidence, with capabilities for competitor analysis, comparison, and evidence retrieval.

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 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.

A hypothesis-driven B2B positioning engine with 8 tools for strategic positioning, competitive analysis, and go-to-market messaging.

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.

A Model Context Protocol (MCP) server that connects Trello to Claude Code. Manage your boards, lists, cards, checklists, and more — all through natural language.

Enables AI assistants to work with Linear via the Model Context Protocol, covering issue/project management, resource and prompt access, webhooks, and OAuth app setup.

Enables goal-driven plan simplification by tracing each step's necessity, detecting cycles, broken links, and transitive waste, and classifying items as required or pointless with review and recheck capabilities.

MCP server for GoalT, a multi-parent value-propagating goal graph. It enables building and querying goal trees, listing priorities, and opening a live dashboard that highlights the active goal during Claude Code sessions.

Official MCP server for FeatureJet, enabling agents to read and act on customer feedback boards: list/search posts, file feature requests, and pull top-voted planned items.

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

Enables AI agents to research mobile apps, compare onboarding and paywall flows, explore App Store reviews, and find visual UI references, bringing real screens and market context into their research.

Collect and analyze user feedback from any app via a single API endpoint, with MCP tools for listing, searching, and stats.