Gives AI coding agents on-demand security playbooks, a code scanner they can run on their own output, and live lookups against CISA KEV, NVD and OSV so they verify threats instead of guessing. Runs locally with no API keys or accounts, exposing skills, tools and a secure-build prompt to Claude Code, Codex, Cursor or any MCP client.

Enables MCP clients to reverse-engineer binary files — hex viewing, disassembly, function/call-graph recovery, syscall and RTTI naming, string/xref search, and symbol export for Ghidra and IDA — across Switch NSO/NRO/NSP and many other formats. Tool output is persisted as annotations so analysis carries across sessions.

Turns a long tennis recording into a labeled highlight reel: keyframe-aligned lossless rally clips, hit count / venue / timestamp burned in, and a self-check on every output. Runs locally with ffmpeg.

Run efficiency for coding agents: share of paid tokens that became output, not context re-reading.

Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.

Run Linux-style commands on Windows via deterministic bash→PowerShell translation. No VM, no WSL. MCP server + CLI built for AI agents (Claude Code, Codex, OpenCode...). GNU-style output, bash-style errors, UTF-8/GBK handled.

Opt-in, token-authenticated MCP server built into the Shelly Android terminal app: read-only tools for terminal output, git status, registered agents and repos on the phone, plus off-by-default run_command and write_file that need an on-device approval tap per call.

Enables coding agents to record product demos, changelog GIFs, help-center guides, and bug repros by driving a signed-in browser or Mac window, then rendering branded MP4 and GIF outputs with delivery checks.

Enables coding agents to navigate and verify a contract-backed semantic graph of TypeScript backends, inspect change impact, run checks, explain failures, and draft human-reviewable contracts through bounded MCP tools and matching JSON CLI output.

Enables MCP clients to create and control persistent, PTY-backed terminal sessions on macOS: launch commands and interactive programs, read output incrementally via byte cursors, send keystrokes, wait on conditions, and detect running/awaiting-input/exited states. Sessions run inside a Seatbelt sandbox with command policies, human approvals, secret redaction, and hash-chained audit logging.

Enables MCP clients such as ChatGPT to execute shell commands, manage files, and view or capture images on Mac and Linux hosts. Supports terminal-based connection approval, time-limited access, and output controls.

Enables Codex to delegate subtasks to concurrent background coding agents that can each run on any model provider supported by Pi, such as DeepSeek, Anthropic, Gemini, OpenRouter, or local models. Exposes MCP tools for listing models, spawning and waiting on agents, sending follow-ups, and aborting, backed by a live local web panel showing status, token usage, and streaming output.

Enables local MCP clients to expose and execute typed tools through a bounded native runtime with Ed25519 admission, per-session budgets, rate limits, schemas, and cached outputs.

Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.

ChannelTerm is an open-source MCP server that enables humans and AI agents to share live serial and SSH terminal sessions for embedded systems development and debugging. It allows AI agents to read device output, execute commands, assist with GDB debugging, and transfer files while keeping developers in control.

Enables AI clients to build, solve and inspect LS-DYNA analyses, either by driving Ansys Mechanical through PyMechanical or by submitting and monitoring standalone keyword-deck runs. It also reads and audits solver output files such as glstat, binout, d3plot and ASCII histories, with results comparison and deck checks.

A task-aware transparent proxy that wraps stdio MCP servers (such as git, filesystem, and terminal) and compresses large tool outputs field-by-field using declarative YAML profiles, passing narrow intent queries through untouched. Discarded data is stored in SQLite and can be losslessly retrieved via an injected tool, either in full, by line range, or by grep, while errors are always preserved and failures fall back to the raw result.

Enables MCP clients to inspect local Git repositories through five focused, read-only tools for repository trees, literal text search, numbered file reads, bounded git diffs, and project summaries while confining paths, skipping secret-like and binary files, and enforcing output limits.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.