Enables agents to capture real browser interactions over CDP, compile them into deterministic replay scripts with parameterized inputs and stored secrets, and execute those workflows with real input events — no page re-reading or LLM in the replay loop. Also emits portable Agent Skills from recorded workflows.

Enables coding agents to explore a database through the project's Drizzle schema, with tools for schema inspection, row browsing, counting, read-only SQL, query plans, drift and migration checks.

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.

This MCP server lets an agent run local binary inspection on ELF, Mach-O, PE, APK, IPA, ASAR, WebAssembly, and firmware targets — pulling symbols, sections, classified strings, disassembly, and mitigation posture, and flagging dangerous imports and CWE patterns. It exposes eight stdio JSON-RPC tools (with no external API keys) that feed a multi-agent swarm for attack-surface mapping and exploit-chain synthesis.

Exposes 16 local stdio MCP tools that give Codex-style clients project-isolated persistent memory, local ONNX-based semantic search, and durable task tracking that survives across sessions and concurrent clients, without executing agents, hosting a model, or contacting model providers.

Enables AI agents to debug Commodore software in a locally running VICE emulator through its text remote monitor, inspecting registers, memory and screens, disassembling code, setting breakpoints and watchpoints, controlling execution, and working with disk images, snapshots and exported graphics. Runs locally over stdio and includes safety guidance for state-changing operations.

Enables agents to analyze OpenAPI contracts for deterministic integration risks, plan auth, sync, action, and webhook candidates, and diff contract versions for likely breaking changes through read-only tools.

Enables AI agents to discover and work with a user's permitted Excalidraw boards through a single MCP endpoint, reading and adding elements, images, and snapshots while respecting owner, view, edit, and guest-link access controls.

Enables AI agents to build, validate, and submit ledger-native XRPL transactions against public nodes — escrows, XRP and token payments, DEX/AMM swaps and liquidity, issuer clawback, and x402 pay-per-request — with validated-ledger proof of results. State-changing calls go to the account owner for browser signing, while an owner-funded agent account allows autonomous spending bounded by on-ledger balance and local policy limits.

An MCP proxy that sits in front of bank tool servers, letting AI agents make read-only calls like balances and transactions while blocking payments, writes, renamed tools and argument smuggling. Each denial produces an Ed25519-signed receipt that can be verified offline.

Enables AI agents to inspect why React components re-rendered, replay recorded browser interactions as performance scenarios in headless Chrome, and verify fixes against performance budgets.

Connects MCP clients to a public CVE activity feed and private product watches, letting agents search advisories, save watched products, check and acknowledge matches, and track new or updated vulnerability records.

A read-only server that enables AI agents to triage Kubernetes CronJobs in a single tool call: whether runs happened and stayed on schedule, whether durations are trending slower or overrunning, which jobs have silently stopped succeeding, and why a given run failed, with evidence and stated basis for each verdict. It also lists active runs and recent failures while enforcing namespace allowlists and no write access.

This MCP server connects AI agents to antidetect browser profiles. It enables vision-based element interaction and complex automated workflows within the RATA/GHM Automate v2 ecosystem.

Enables agents to execute shell commands and upload/download files on remote Linux hosts over SSH/SFTP with just two tools, returning structured, verifiable results such as exit codes, separated stdout/stderr, timeout and truncation flags, and the current working directory. Supports connection reuse, host alias resolution, host-key verification, and cross-call session state so multi-step work needs fewer calls.

Enables AI agents to run free, no-signup Ethereum on-chain lookups over a remote HTTP endpoint: wallet ETH balance, contract/EOA detection, outbound transaction counts and simple risk heuristics, plus NFT metadata (name, description, image URLs, IPFS gateway links and raw tokenURI/uri JSON) for ERC-721 and ERC-1155 tokens. Reads only, with cached results and no key, account, or wallet connection required.

It enables AI agents to access live web data through page fetching, search, prebuilt scrapers, datasets, and remote browsers, using free direct requests and local Chrome rendering with Bright Data only as a fallback for bot walls. It also adds budget controls, caching, pagination, cancellation, retries, SSRF protection, and both stdio and HTTP transports.

AI agent skills for production grade applications

Mounts a headless, low-memory browser for agents with a text-first tool interface, enabling navigation, semantic tree/markdown extraction, structured-data scraping, screenshots, form interaction, and session capture. Reads and interactions are returned as semantic trees, markdown, or CSS-selector JSON rather than pixels, which is cheaper for a model to reason over.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

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.