"Coding in Python and R" matching MCP servers:

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。

Routed is a universal local router for Agent Skills across AI coding environments.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Production-ready MCP server starter templates in TypeScript and Python. Includes tool, resource, and prompt patterns with Claude Desktop integration configs.

Zero-dependency token optimizer, test-failure triage gate, and System 1.5 semantic guardrail for AI coding agents. Intercepts compiler errors, missing dependencies, and doom loops in < 500µs.

In-memory polyglot AST pruning and deterministic context firewall for coding agents, eliminating up to 72.4% token bloat with zero telemetry egress.

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 AI coding agents to request human-in-the-loop approvals, choices, refinements, and notifications via MCP, with fail-closed semantics and no remote approval surface.

MCP server that pings 130+ free coding LLM models across 17 providers in real-time, ranks them by latency, and helps AI agents pick the fastest available model.

Lets a frontier coding agent delegate research, cataloguing, and long-running computation to a local LLM with guarded filesystem, web, and Python execution tools, preserving the agent's context and tokens.

Allows two AI coding agents on different machines to securely pair and share files, context, and conventions through an end-to-end encrypted peer-to-peer channel with human-in-the-loop consent.

Enables coding agents to run multiple approaches or tasks in parallel on isolated Daytona sandboxes, returning tested diffs and benchmark rankings so the best outcome can be applied.

Speeds up coding agents by predicting and pre-executing upcoming tool calls, then safely returning validated cached results to eliminate wait time.

A multi-engine web search MCP server that integrates with coding agents, offering parallel search, page fetching, X/Twitter search, deep research, and configurable free/API layers.

MCP server that launches and supervises coding-CLI subagents (claude, opencode) in detached tmux windows, collapsing the ceremony of starting a TUI coding assistant into a single tool call.

Enables coding agents to run persistent, budgeted work sessions with dependency graphs, human decision queues, required validation, and clean handoffs for autonomous or human-in-the-loop execution.

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

DevTrack is a local developer-context MCP server that gives AI coding assistants access to your active ticket, commits, pending actions, writing voice profile, ticket history, and end-of-day summary. It exposes six read-only, non-destructive SQLite-backed tools over stdio. DevTrack runs locally and does not require the optional Python backend or a hosted MCP endpoint. https://devtrack.cloud.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.