mcp-runrun
MCP server that gives Claude access to Runrun.it tasks, projects, clients, users, boards, and more via the Runrun.it REST API v1.0, including listing, creating, updating, deleting, and time-tracking operations.
README
mcp-runrun
MCP server for Runrun.it — gives Claude access to tasks, projects, clients, users, boards and more via the Runrun.it REST API v1.0.
Requirements
- Python 3.11+
- Runrun.it App-Key and User-Token (Settings → Integrations → API)
Setup
# 1. Create & activate venv
python -m venv .venv
.venv\Scripts\activate # Windows
# source .venv/bin/activate # Mac/Linux
# 2. Install
pip install -e .
Credentials
Copy .env.example to .env in the project root and fill in your keys:
RUNRUN_APP_KEY=your_app_key
RUNRUN_USER_TOKEN=your_user_token
.env is gitignored — credentials never leave your machine and never go
into Claude's config files.
Configure in Claude
Mac/Linux — register the wrapper script (it loads .env and starts the server):
claude mcp add --scope user runrun -- /path/to/mcp-runrun/run.sh
For the Claude Desktop app, add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"runrun": { "command": "/path/to/mcp-runrun/run.sh" }
}
}
Windows — register run.bat (same idea: loads .env, starts the server).
Add to %APPDATA%\Claude\claude_desktop_config.json:
{
"mcpServers": {
"runrun": { "command": "C:\\mcp-runrun\\run.bat" }
}
}
Security
- Never commit
.env— it is gitignored on purpose. Don't paste real keys into README, issues, commits, or pull requests. - The User-Token acts as your Runrun.it user: anyone with it can read and change tasks on your behalf. Treat it like a password.
- If a token leaks, revoke and regenerate it immediately at Runrun.it → Settings → Integrations → API.
- Each person must use their own User-Token — don't share tokens between teammates (actions are logged as the token's owner).
Available Tools
Performance & filtering notes
Runrun.it task objects are huge (125 fields, ~5.5 KB each). To stay fast and avoid
flooding the model's context, list endpoints (list_tasks, list_projects,
list_users, list_clients) return a concise summary by default plus
pagination metadata (total, has_more, next_page). Pass
detail_level: "full" when you genuinely need every field.
Two distinct people are attached to each task:
- Creator →
user_idfilter ("tasks I created") - Responsible / assignee →
responsible_idfilter ("tasks assigned to me")
For "my cards / tasks I created", use list_my_tasks — it resolves the current
user automatically and filters in a single fast call.
Extracting card content & building spreadsheets
The core workflow ("filter cards → read each card's content → standardized spreadsheet") is served by two tools:
get_task_content— reads ONE card and returns a clean flat record: status, board/stage/project/client/type, creator, responsible, all assignees, dates, time (raw seconds +H:MM:SS), tags, custom fields with human labels resolved (e.g."Área": "Growth"), and the description converted from HTML to plain text (tables → tab-separated cells, lists → bullets, links preserved astext (url)).export_tasks— the workhorse. Same filters aslist_tasks, then extracts a standardized record for every matching card (descriptions fetched in parallel, paging past the 100-per-request API ceiling up tomax_tasks). Passflatten_custom_fields: trueto get onecf: <label>column per custom field plus a deterministiccolumnslist — drop straight into a spreadsheet. Returns pagination metadata (total,capped,capped_by,errors).
Custom fields that share a label across the org are kept distinct (the field id is appended). Rate-limited (429) requests are retried with backoff.
Tools
| Tool | Description |
|---|---|
list_tasks |
List tasks with filters (creator user_id, responsible_id, project, board, status). Summary + pagination |
set_task_fields |
Write custom field values on a task. Keys accept field ids or labels; option labels are resolved to ids automatically; null clears |
list_field_options |
List the selectable options (id + label) of a custom field |
list_my_tasks |
List the authenticated user's own tasks (created or responsible) in one call |
get_task_content |
Read one card → standardized record (description as text, tags, resolved custom fields) |
export_tasks |
Filter + extract every matching card into uniform, spreadsheet-ready rows |
list_boards / get_board |
Discover boards / board IDs for filtering |
get_task |
Get a task by ID |
create_task |
Create a new task. Optional board_id/board_stage_id move it to the right board immediately (the API always creates on the default board) |
update_task |
Update task fields |
delete_task |
Delete a task |
play_task |
Start timer on a task |
pause_task |
Pause timer on a task |
close_task |
Close/complete a task |
reopen_task |
Reopen a closed task |
list_task_comments |
List comments on a task |
add_task_comment |
Add a comment to a task |
list_task_attachments |
List attachments on a task |
list_projects |
List projects |
get_project |
Get a project by ID |
create_project |
Create a project |
update_project |
Update a project |
list_clients |
List clients |
get_client |
Get a client by ID |
create_client |
Create a client |
list_users |
List organization users |
get_user |
Get a user by ID |
get_current_user |
Get the authenticated user |
list_activities |
List activity history |
list_boards |
List boards |
get_board_stages |
Get stages of a board |
list_task_types |
List task types |
list_teams |
List teams |
Authentication
Get your credentials at Runrun.it → Settings → API:
- App-Key: the application key for your organization
- User-Token: the personal token for the acting user
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。