"Exploring the term 'make' and related resources" matching MCP servers:

Long-term memory for Claude Desktop and Claude Code over plain markdown files on your machine. It returns nothing when the corpus has no answer, naming the terms that appear nowhere — no least-bad match dressed as an answer. And it verifies cited commit SHAs against git, so an agent can tell what actually shipped from what was only discussed and abandoned. Local; nothing is transmitted.

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 AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

Unified capability catalogue with formally verified cartridges, distributed community hosting, and the Teranga menu system for single gateway access to a vast rabbfe of MCP, LSP, NeSy and related servers.!

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

Enables ChatGPT to inspect selected local projects, make edits, run commands/tests, and review diffs through an authenticated MCP endpoint backed by a containerized workspace.

Enables AI coding assistants to drive spec-driven development through slash-command workflows for planning, exploring, implementing, updating, and finalizing changes, with local daemon and project registry support.

Enables MCP clients to read a project's component tree and design tokens, edit React/TSX canvas frames, set props, apply auto layout and variants, add shader backgrounds, and capture screenshots, so agents can make design changes that are written back as reviewable code diffs and pull requests.

Provides persistent long-term memory for AI coding agents by storing entities, relations, and observations across different sessions. It enables users to manage and query structured knowledge like coding preferences, project patterns, and technical solutions via a graph-based storage system.

dev-brain-mcp

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).

A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.

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 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.

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.