"Information and Resources About Marketing" matching MCP servers:

Provides brutally honest code reviews, explanations, and refactoring suggestions via CLI and MCP tools.

Exposes 31 high-assurance agentic coding skills as MCP tools and resources, enabling AI agents to invoke structured workflows across DISCIPLINE, TECHNIQUE, KNOWLEDGE LAYER, and REFERENCE registers.

Enables coding agents to search and retrieve persistent repository memory created by Cliper, providing context, gaps, architecture, and timeline information via MCP tools.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Enables coding agents to navigate and query source code by providing context, symbols, and call graph information through a graph index.

A FastMCP integration with Hyperskill that allows AI agents to explain programming topics and search for programming resources using Hyperskill's learning materials.

Enables users to run shell commands with compressed output, extract information from large files, drive local opencode and Codex agents, perform browser-automation tasks, and monitor runs via a live dashboard.

Enables coding agents to query a parsed graph of a repository and to recall what earlier sessions learned about it, both stored in FalkorDB behind one traversal. It exposes eight MCP tools over stdio for locating symbols, tracing calls and impact paths, and writing or recalling user- and repo-scoped facts, procedures, and handoffs.

Connects Claude, Cursor, VS Code, and other MCP clients to OpenCode's headless API, enabling users to ask questions, implement features, monitor background work, respond to questions and permissions, and review changes across projects. It provides tools, resources, and prompts for setup, multi-project workflows, and asynchronous coding task management.

dev-brain-mcp

Enables coding agents like Cursor to access Figma design data, fetch layout and styling information, and implement designs in any framework.

Connects MCP clients such as Claude, Cursor, and VS Code to OpenCode's headless API so users can ask questions, implement features, run and monitor background coding jobs, supply replies and permissions, and review changes across multiple projects. Supports async job tracking, structured outputs, and project-scoped resources and prompts.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

Connects MCP clients such as Claude, Cursor, and VS Code to OpenCode's headless API so users can ask questions, implement features, run background coding jobs, answer permission and question prompts, and review changes across multiple projects. It also exposes project-aware resources, prompts, and job monitoring tools with configurable provider and model defaults.

Provides AI coding agents with access to Figma design data through the Figma API. Enables accurate one-shot implementation of designs in any framework by translating Figma files into simplified layout and styling information.

Local-first MCP server that indexes all projects on your machine and provides coding agents with briefings about each project's stack, services, and run instructions, so they can start sessions with context across all repos. No cloud, no accounts, no telemetry.

MCP server that enables Claude Code to communicate with other Claude Code agents over HTTP, allowing users to ask questions about remote codebases or delegate coding tasks.

MCP server to provide Jira Tickets information to AI coding agents like Cursor

Bridges Large Language Models with Language Server Protocol interfaces, allowing LLMs to access LSP's hover information, completions, diagnostics, and code actions for improved code suggestions.

An autonomous code-reasoning agent that parses repositories using tree-sitter, indexes symbols into SQLite, and answers questions about the codebase through an agentic loop powered by Claude via OpenRouter.

An MCP server that provides tools and resources for managing a coding project's todo list, allowing users to view, add, update, and delete todo items with details like priority, status, and tags.

Generates project features (HTML pages, backend APIs) from prompts and project-specific business rules stored as resources, with support for interchangeable LLM providers (local Ollama or cloud Gemini).

Enables AI coding agents to access real-time VSCode LSP diagnostics, type information, and code navigation without slow tsc/eslint checks, plus symbol renaming and file operations.

Plori runs AI agents in Workspaces. A Workspace is a project folder in the cloud. Several agents can work in it at the same time, each on its own copy of the files, and Plori merges their work back into the folder. Every version of the files is kept.

MCP server that delegates bounded subtasks to NVIDIA NIM sub-agents, enabling context offload and parallel fan-out for coding assistants like Claude Code and Cursor.

An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.

Enables structured, step-by-step problem-solving through dynamic thinking processes that can be revised, branched, and adjusted as understanding deepens. Supports breaking down complex problems into manageable steps with the ability to revise previous thoughts and explore alternative reasoning paths.

Enables LLMs to apply Martin Fowler's 71+ refactoring patterns to codebases through a pluggable, language-agnostic architecture. Supports previewing and applying refactorings, analyzing code smells, and inspecting code structure with safe-by-default operations.

Provide a human vision statement. The AI autonomously researches, audits your codebase, generates specs and plans, implements, and validates — with bi-directional verification at every junction to ensure nothing is missed or invented.

Enables AI coding agents to retrieve relevant execution context before a task, compare candidate paths, log outcomes, and search past experiences stored locally in SQLite. It lets agents reuse what worked on similar tasks and get live alerts when a run stagnates or repeats failures.