ToolBartender▌

by sungminwoo0612
ToolBartender turns goals into clear, step-by-step action plans so you can execute faster and achieve results.
Transforms goals into executable step-by-step plans.
github stars
★ 0
best for
- / AI agent developers building goal-oriented systems
- / Workflow automation with multi-step processes
- / Planning systems that need tool orchestration
capabilities
- / Parse natural language goals to extract intents and parameters
- / Generate step-by-step execution plans with tool sequences
- / Validate plans against available tool sets
- / Create execution prompts with safety rules and error handling
- / Explain plans in Korean with usage guidance
what it does
Converts natural language goals into structured, executable plans with step-by-step instructions and tool usage. Validates plans against available tools and generates execution prompts for AI agents.
about
ToolBartender is a community-built MCP server published by sungminwoo0612 that provides AI assistants with tools and capabilities via the Model Context Protocol. ToolBartender turns goals into clear, step-by-step action plans so you can execute faster and achieve results. It is categorized under developer tools, productivity. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install ToolBartender in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
license
MIT
ToolBartender is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
ToolBartender 🍸
Planner MCP that turns natural-language goals into safe, structured execution plans (JSON)
ToolBartender는 사용자의 자연어 요청을 하나의 goal로 받아,<br> 어떤 MCP 도구를 어떤 순서로 호출해야 하는지 실행 계획(plan)을 생성하는 Planner MCP입니다.<br>
실제 도구 실행은 하지 않고, LLM/실행 에이전트가 안전하고 예측 가능하게 실행하도록<br>
steps / assumptions / required_confirmations / execution_hint를 포함한 JSON을 반환합니다.<br>
한 줄 요약: ToolBartender = “도구 실행”이 아니라 “도구 조합 계획”을 만드는 MCP
PoC Endpoint
http://mcp.toolbartender.dev/mcp (Streamable HTTP / SSE)<br>
Keywords
planner, safe execution, tool orchestration, PlayMCP
What it does
다음과 같은 복합 요청을 하나의 goal로 받아 plan을 생성합니다.
- 이동 계획 (예: “오늘 오후 8시 판교에서 강남으로 이동”)
- 일정 조회/조정
- 정보 탐색 및 요약
- 결과 전달(이메일/메신저 등)
Output Plan includes
steps: 사용할 MCP 도구 + 실행 순서assumptions: 전제 조건required_confirmations: 사용자 확인이 필요한 작업(특히 write 계열)execution_hint: 실행 에이전트용 가이드
Exposed MCP tools (this server)
PlayMCP 등록 제약(정규식) 때문에 ToolBartender가 노출하는 tool name은 ASCII로 고정을 권장합니다.
plan_create: goal + available_tools → plan 생성plan_validate: plan이 현재 컨텍스트(available_tools)에서 실행 가능한지 검증plan_render_prompt: LLM 실행 에이전트가 steps를 “순서대로” 호출하도록 프롬프트 생성plan_explain: 사용자에게 plan을 쉽게 설명
내부
plan.steps[*].tool_name은 다른 MCP들의 도구 이름이므로 (예:calendar.read,map.route) 그대로 두는 구조가 자연스럽습니다.
도구 I/O, 스키마, 예시는 docs/02_tools.md 참고.
Quickstart
1) Local run
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python src/main.py
# Open http://localhost:3333/mcp in your browser
2) Inspector로 로컬 테스트
npx @modelcontextprotocol/inspector
# http://localhost:6274
Inspector UI에서 서버 URL을 http://localhost:3333/mcp로 연결합니다.
3) PlayMCP 통합 테스트
- public URL 필요(ngrok/배포)
- PlayMCP → “새로운 MCP 서버 등록” → https://<public-host>/mcp 입력 → 임시 등록 → AI 채팅에 적용 흐름은 기존 README에 이미 있습니다.
Docs
- docs/00_quickstart.md : 5분 셋업
- docs/01_concept.md : Planner MCP 컨셉 / 철학 / 로드맵
- docs/02_tools.md : tool별 입력/출력/예시 + naming 규칙
- docs/03_playmcp.md : PlayMCP 등록 팁 / 심사 관점 체크리스트
- docs/04_deploy.md : 배포(nginx/SSE 포함) + 도메인/Cloudflare 포인트
- docs/05_troubleshooting.md : 405/핸드셰이크/SSE 관련 이슈 모음
License
MIT
FAQ
- What is the ToolBartender MCP server?
- ToolBartender is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for ToolBartender?
- This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
ToolBartender is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Piyush G· Sep 9, 2024
We evaluated ToolBartender against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Useful MCP listing: ToolBartender is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Jul 7, 2024
ToolBartender reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend ToolBartender for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Oshnikdeep· May 5, 2024
Strong directory entry: ToolBartender surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Apr 4, 2024
ToolBartender has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Rahul Santra· Mar 3, 2024
According to our notes, ToolBartender benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Pratham Ware· Feb 2, 2024
We wired ToolBartender into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Yash Thakker· Jan 1, 2024
ToolBartender is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.