"A server providing context 7" matching MCP servers:

前端ai主导的全流程，自动化的预开发；旨在解决前端开发在联调前期的各种繁琐工作

基于MCP架构的微信公众号文章爬虫系统，支持AI智能体通过Selenium自动抓取微信公众号文章内容和图片，实现文章内容的智能分析。

A reusable MCP server providing shared, versioned context across AI agents and devices via a private GitHub workspace, with tools to discover projects, bootstrap, query, and close out task state.

An AI personality collaboration tool based on Model Context Protocol (MCP) that enables users to summon and collaborate with multiple AI personas for intelligent analysis and problem-solving.

MCP server providing virtual memory for AI context with durable SQLite storage, retrieval, and bounded prompt assembly to manage context windows.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Enables AI agents to securely retrieve pre-compiled bounded context and memory state over a zero-network Unix Domain Socket, ensuring tenant isolation and sub-millisecond rehydration in Kubernetes environments.

Listens for Slack @mentions and publishes them as real-time events to the KĀDI event bus, enabling event-driven architectures with low latency.

Comprehensive quest orchestration system with KĀDI broker integration, multi-channel approval workflow, and Git-backed data storage.

Enables production lines to self-service register data sources and tools, and downstream AI agents to discover and invoke them through the standard MCP protocol.

An authenticated OpenAI-compatible inference gateway with an async job broker that bridges a coding agent to a ChatGPT MCP worker, enabling ChatGPT to process inference requests via a tunnel.

Enables real-time user intervention for MCP agents through a web UI, allowing users to review context and provide feedback when AI agents drift from intent, keeping them on track.

Context window usage estimation for AI coding agents via MCP, enabling proactive state preservation before compaction.

Discover and connect your agents with other agents via A2A protocol. Share resources for free or fee.

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.

Proxy for OpenAI-compatible backends that compacts conversation history before the context window fills, with read-only MCP tools to search and read back the archived originals.

Enables AI coding agents like Claude Code or Codex to delegate tasks to a DeepSeek Harness subagent with its own context window, providing tools for task delegation, result waiting, continuation, and supervision with sandboxed execution.

Enables agents to delegate browser exploration to Jev by providing natural-language objectives, receiving compact evidence and source context, and continuing the same live browser session through supervisor handoffs.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

A scoped aggregator MCP server that routes intent to the 4-7 relevant finance tools from a 43-tool catalog, reducing token cost and improving tool-selection accuracy.

prevents llm from overload, freeze and oscillation

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

Drive the real, logged-in Chrome you already have open — cross-platform, token-budgeted reading (90%+ savings), and 24/7 headless Ubuntu support.

Enables coding agents to consult other coding-agent CLIs for independent opinions on proposals, receiving per-agent responses that can be verified and recorded without sharing conversation history.

Enables collaboration across Claude, GPT, and Gemini through a unified MCP interface, with configurable execution policies for synthesizing or directly delivering worker outputs.

Enables a host AI agent to trigger a multi-agent debate, running N-rounds where agents critique each other's answers, then synthesizes a consensus recommendation with ranked options.

A meta-MCP server that aggregates authoritative web search and browser research tools, providing a unified interface with a cost-efficient fetch ladder for web research.

A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity. This server implements the same progressive disclosure architecture that Anthropic describes in their Agent Skills enginee

Lets separate coding-agent sessions coordinate through a shared, role-addressed mailbox instead of a human relaying every message, over a single local SQLite file or a hosted server with many isolated channels. Messages can be sent as tracked obligations that stay open until one side resolves and the other confirms, alongside threads, full-text search, work statuses, and an append-only versioned pin system where protected changes require recorded agreement from every declared role.