Enables AI agents to manage to-dos, lists, schedules, recurring tasks, and notes through standard stdio MCP tools, sharing authentication with the companion CLI and discovering business tools after login.

Enables AI agents to queue, commit, push, and track durable Git transactions through a shared daemon, preserving other agents' unstaged work and allowing recovery by request ID.

Enables an external AI client to discover tender portal APIs, collect and verify tender listings, combine sections, and select relevant lots from a user request through local MCP tools.

Lets AI coding agents build, test, and lint Android projects with Gradle, control devices and emulators via adb, automate UI through screenshots and input injection, and use a live IDE bridge for refactoring, code inspection, and run configurations. Works with any MCP client over stdio or authenticated HTTP.

Equips AI coding agents with searchable production incident scenarios, outside-in diagnostic runbooks, interview elevator pitches, hardened Kubernetes manifest generation, CLI cheat sheets, and cloud architecture blueprints.

Provides a shared, repo-local memory that lets coding agents recall relevant decisions, conventions, gotchas, and open threads, save new entries, coordinate file claims, and cache file summaries across tools and sessions.

Enables agents to reach pump.fun's full API surface — official developer routes, bonding-curve reads, Solana RPC, and unsigned trade building — by writing JavaScript against one merged OpenAPI document, using just two tools (search and execute). Model-authored code runs in a network-less sandbox, so multi-step questions resolve in a single round trip while credentials stay on the host Worker.

Enables a coding agent to read a freeform Excalidraw sketch of a UI and build it in the user's project with their chosen component library, then draw its changes, notes, and links back onto the canvas. Lets users target parts of a drawing, adjust the project setup, and wait for updates over streamable HTTP, all locally.

Enables AI coding agents to query locally installed language servers over stdio MCP for read-only code intelligence, including definitions, references, implementations, hover types, symbols, and diagnostics.

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.

Exposes the Jupyter notebooks open in VS Code to MCP-compatible AI agents, letting them edit cells in the open editor and execute them in the live kernel without file conflicts. Agents can make partial edits, track stale cells, inspect variables, and manage kernels while preserving the user's interpreter choices and unsaved work.

Enables MCP-capable agent CLIs on macOS to reuse an installed, unmodified OpenAI Browser Runtime to drive a connected Chrome or Edge browser (or a host's in-app browser backend) through js, js_reset, and turn_ended tools. It stays a thin bridge that preserves the Runtime's approval and security boundaries, failing closed when the client cannot handle approval requests.

Enables AI coding agents to capture deliberately deferred issues in a plain-text markdown ledger, then list, status-update, and archive them across sessions. All operations are stateless stdio tools with no network or database, and the ledger remains directly readable and editable by humans.

Enables LLM-powered developer environments to discover and evaluate potentially beginner-friendly GitHub issues using explainable heuristics, clear rationale, and direct evidence links over stdio.

Enables AI clients to drive local video editing projects through a CEP bridge, covering media ingest, sequence and timeline editing, effects, keyframes, captions, markers, audio, color, and exports across hundreds of discoverable tools. Ships context resources, guided prompts, and higher-level assembly workflows for agent-driven editing.

Enables coding agents to run taste heuristics on MapLibre style JSON, catching rainbow fills, low-contrast labels, crowded text layers, and light-only palettes via a check tool, plus basic structure validation and locating a local showcase for preview.

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 AI agents to print HTML, web pages, PDFs, and images to local printers by bridging to an existing client over WebSocket, including previews, printer discovery, batch jobs, and customizable PDF/print settings.

Enables AI agents to open URLs in a persistent headless browser and interact with pages by clicking, typing, selecting, reading content, evaluating JavaScript, waiting for elements, and capturing screenshots.

Exposes a federated, typed knowledge graph to AI clients, letting agents traverse nodes and relationships while scope-based access rights are checked on every hop and provenance is tracked for each fact. This keeps shared knowledge consistent across teams while keeping private and team-scoped content out of reach of unauthorized consumers.

Enables coding agents to surgically harvest licensed upstream code, scaffold adapters behind canonical contracts, audit seams, verify live behavior, and promote or mount proven components while preserving the host architecture.

Enables AI coding agents to read GitHub repositories, files, commits, issues, pull requests, releases, Actions, and search results through 18 read-only MCP tools. It reuses existing credentials, routes around unreliable networks, and caches responses on disk to stay within rate limits.

Enables MCP clients and AI agents to author, inspect, validate, and render PromoShot .promo projects over stdio, delegating rendering to the promo CLI.

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating