"Resources or assistance for coding help" matching MCP servers:

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.

Pre-build reality check for AI coding agents. Scans GitHub, Hacker News, npm, PyPI & Product Hunt in parallel — returns a 0-100 reality signal with real competitor data.

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 read-only queries across App Store Connect and Google Play from one toolset, letting users list apps, track release states, and review merged store feedback without extra dependencies or credentials in demo mode.

Enables AI agents and coding assistants to access live Steam market intelligence, including app search, store details, player-count trends, review mining, and news digests.

Enables AI assistants to validate drafted user stories against INVEST and acceptance-criteria rules, suggest splits for oversized stories, and export finished backlogs to Markdown, CSV, or Jira CSV.

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

MCP server for Tracetify — trace how any product actually grew, without leaving Claude Code or Cursor.

Decision memory for teams building with AI coding agents. Captures every engineering decision once, ranks it by blast radius, and briefs every MCP-compatible agent before it acts, so agents stop making conflicting changes across repos.

Enables traceable requirement discovery, technical alignment, and ISO-aligned process checking through deterministic MCP tools and resources, without requiring an embedded LLM.

Lets AI assistants query a live feedback collection database in plain language, retrieving entry counts broken down by type and classification status, and listing individual submissions filtered by type, urgency, or sentiment. Access is read-only and privacy-preserving, so answers can be produced without exposing submitter email addresses.

Enables a coding agent to keep one JSON feature specification per feature, governed by a progressive schema, and to render it as editable Markdown documents for a product owner. Answers and direct Markdown edits are reconciled back into the validated model through stdio MCP tools, applying coherent changes and reporting those that widen scope or contradict the model.

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.

Generate SWOT analyses, competitive comparisons, and browse 100+ pre-built industry examples for any company, brand, or topic. Supports 12 languages.

MCP server for IDEA Base — AI-powered project management. Manage projects, tasks, and time tracking directly from Claude Code, Cursor, or any MCP-compatible AI tool.

Enables coding agents to generate software engineering documents — PRD, SRS, ERD, SDD, design spec, traceability matrix, and agent instructions — as Markdown so they build from written specifications instead of guessing. Unsupplied inputs are marked [NOT SUPPLIED] or UNMAPPED rather than filled with invented data.

Enables agents to create and drive instrumented Design Thinking sessions, inspect the append-only journal, and retrieve dossiers and handoff specs through MCP tools and resources.

Enables coordinating multiple AI coding sessions by tracking features, decisions, and approval gates in a per-project Postgres database, with atomic IDs and commit-stamped append-only history.

Guides product design through mandatory phases—discovery, definition, prioritization, flows, and verification—so an AI assistant can run a project from problem to verified scope, with strict or free progression modes.

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.