"A guide for reducing token count in AI requests" matching MCP servers:

Persistent memory and pattern learning for AI coding assistants via MCP.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Provides sandboxed code execution and data processing for CSVs and logs to achieve over 95% token savings. It enables secure multi-language execution and progressive tool disclosure to optimize LLM context usage.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

Enables AI agents to program MicroPython boards such as Raspberry Pi Pico and ESP32 through Thonny, with visible shell commands and optional human approval.

Stop stitching context for Copilot and Cursor. With Sequa MCP, your AI tools know your entire codebase and docs out of the box.

MCP server for AI-driven CAD operations, enabling parametric modeling, build, inspection, measurement, rendering, and diff comparison with STEP, DXF, and GLB export.

MCP server for secure, session-based Python code execution in Docker containers, enabling LLM applications to run code, manage state, and access files.

Enables AI-native statistical analysis and reproducible research workflows through MCP, including natural language planning, protocol-based analysis, Python/R cross-validation, and publication-ready figure generation.

An MCP server that exposes 252 algorithms from Knuth's TAOCP to AI agents, with safety guards and structured outputs for tasks like combinatorial generation, SAT solving, number theory, string analysis, graph algorithms, and symbolic math.

A starter template for creating MCP servers that work with Puch AI, featuring ready-to-use tools for job searching and image processing. Includes examples for Bearer token authentication, OAuth integration with Google and GitHub, and demonstrates user-scoped data management.

A Model Context Protocol (MCP) server that supports STDIO, SSE and Streamable HTTP protocols for AI model interactions.

MCP server for portable context management across AI assistants, providing tools to store and retrieve persistent context, instructions, and execute sandboxed bash commands with automatic git commits, using OAuth 2.1 and magic link authentication.

Provides a secure workspace for AI-driven file management and Python script execution designed to run on Vercel. It enables tools for full file operations and code execution within a sandboxed environment.

Enables AI-powered coding assistance including file editing, code generation, explanation, review, and shell command execution, using multiple free AI providers with automatic fallback.

Remote-control Unity Editor from any AI IDE via file-based IPC — 62 tools, zero dependencies

Enables AI models to dynamically create and execute their own custom tools through a meta-function architecture, supporting JavaScript, Python, and Shell runtimes with sandboxed security and human approval flows.