TokRepo
Agent-native MCP server with session_init, install_plan trust_gate, and handoff_plan for Claude Code, Codex, Cursor, Cline, Roo, Aider, OpenHands, Gemini CLI.
Agent-native infrastructure
TokRepo for AI Agents
Package manager + memory + handoff layer for every agent runtime. Pick your framework below for the shortest path.
npx -y tokrepo@latest init-agent --target all npx -y tokrepo@latest session-init --json npx -y tokrepo@latest agent-check "" --json
Anthropic CLI
Claude Code
The first-party Anthropic terminal coding agent. CLAUDE.md is its project memory; MCP servers are first-class.
CLAUDE.md + .claude/agents/
OpenAI CLI
OpenAI Codex
OpenAI Codex CLI. AGENTS.md is the project memory; ~/.codex/skills/ hosts installable skills.
~/.codex/skills/
IDE
Cursor
Cursor reads .cursor/rules/*.mdc as workspace rules and ~/.cursor/mcp.json for MCP servers.
.cursor/rules/
VS Code
Cline
Cline is a VS Code extension. .clinerules/*.md inject project rules; native MCP marketplace.
.clinerules/
VS Code Fork
Roo Code
Roo Code (Cline fork) reads .roo/rules/*.md and supports the same MCP protocol.
.roo/rules/
CLI
Aider
Aider is a terminal pair-programmer. CONVENTIONS.md is read on every session start.
CONVENTIONS.md
Autonomous
OpenHands
OpenHands (formerly OpenDevin). .openhands/microagents/repo.md is loaded as repo-scoped microagent.
.openhands/microagents/
Google CLI
Gemini CLI
Gemini CLI reads GEMINI.md as project instructions and supports MCP via extensions.
GEMINI.md
Machine-readable surfaces
Agent-discoverable endpoints
- /.well-known/agent.json
- /.well-known/mcp.json
- /.well-known/tool-catalog.json
- /.well-known/tokrepo-trust.json
- /agent-stats
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관련 서버
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Model Context Protocol servers
A collection of reference implementations for the Model Context Protocol (MCP), demonstrating secure and controlled access to tools and data sources for Large Language Models (LLMs).
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An MCP server that focuses on providing useful tools for Azure DevOps Boards
AIC
Local-first MCP server that sits transparently between your AI editor and any model, classifying intent, selecting the right files, and compiling focused context — with zero manual invocation