"Nexus Mods" matching MCP servers:

NEXUS enhances AI coding agents with semantic code indexing, context engineering, change impact analysis, persistent memory, safe execution, and deterministic orchestration through 31 MCP tools.

Provides mathematical operations (expression evaluation, arithmetic, advanced calculations) through MCP, backed by Temporal workflows for reliability and durability. Demonstrates integration between Model Context Protocol tools and Temporal's Nexus RPC framework with full observability.

Composes arbitrary MCP tools using JavaScript, enabling discovery, composition, and execution of other MCP servers through a single 'exec' tool.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

A Model Context Protocol server for Zoho Site24x7 that uses a JavaScript sandbox to execute API calls, enabling management of monitors, accounts, and MSP/BU tenancy through natural language.

A lightweight Model Context Protocol (MCP) orchestrator designed for efficiency at scale. It features TOON compression (reducing token usage by 30-90%) and Lazy Loading, making it the ideal solution for complex, multi-tool agentic workflows.

An MCP server for Arma Reforger and Enfusion engine modding that enables users to create mods, search API classes, and generate scripts through natural language. It provides a comprehensive suite of tools for scaffolding addons, generating prefabs, and building projects using the Workbench CLI.

A lightweight bridge extension that enables Claude Desktop to connect to VSCode workspaces via Model Context Protocol, allowing Claude to read and write files, create directories, and list contents in your workspace.

Enables AI assistants to securely inspect, search, edit, build, and test explicitly authorized local development projects over MCP, with sandboxed filesystem access and sensitive-file protection.

Lets agents run a small JavaScript program in a sandboxed worker that composes typed Jev judgments — claim checking, text screening, best-item picking, label classification, passage comparison, and yes/no questions — in a single call, returning only schema-validated results. A companion search tool exposes each judgment's JSON input and output schemas.

Enables AI agents to inspect and modify running Flash SWF files through a patched Ruffle player, including getting/setting display object properties, calling methods, and editing SharedObject data without reloading.

Executes TypeScript code in a secure sandbox with IPC-bridged access to MCP tools like filesystem operations and network requests.

Enables safe execution of Python code with AI assistance and integration with Model Context Protocol tools.

Provides a stateless MCP gateway that lets AI agents execute Python in an isolated AST sandbox, run guarded read-only SQL queries, and automatically route tasks to cheaper or more capable models.

A Code Mode MCP server for the e2b API, giving agents three tools (search, execute_read, execute_write) to write and run JavaScript functions inside sandboxed environments for API discovery and calls.

An MCP server implementing a multi-phase backend for structured model interactions, bounded compilation, exact-hash confirmation, and Codex-run provenance, with trust-separation hardening.

Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.