Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Connect AI assistants to ITM Platform projects, tasks, budgets, risks, and team workload.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
ITM_API_URL, ITM_COMPANY, ITM_API_KEY필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://api.itmplatform.com/v2/_/mcp/Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"itmplatform": {
"command": "npx",
"args": [
"-y",
"@itm-platform/mcp-server"
],
"env": {
"ITM_API_URL": "your-value",
"ITM_COMPANY": "your-value",
"ITM_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add --transport http itmplatform https://api.itmplatform.com/v2/_/mcp/claude mcp add itmplatform --env ITM_API_URL=your-value --env ITM_COMPANY=your-value --env ITM_API_KEY=your-value -- npx -y @itm-platform/mcp-server실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"itmplatform": {
"url": "https://api.itmplatform.com/v2/_/mcp/"
}
}
}프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"itmplatform": {
"command": "npx",
"args": [
"-y",
"@itm-platform/mcp-server"
],
"env": {
"ITM_API_URL": "your-value",
"ITM_COMPANY": "your-value",
"ITM_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"itmplatform": {
"type": "http",
"url": "https://api.itmplatform.com/v2/_/mcp/"
}
}
}프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"itmplatform": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@itm-platform/mcp-server"
],
"env": {
"ITM_API_URL": "your-value",
"ITM_COMPANY": "your-value",
"ITM_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://api.itmplatform.com/v2/_/mcp/메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
ITM_API_URL envITM_COMPANY envITM_API_KEY env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
아직 이 서버의 도구 목록을 읽지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 itmplatform 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트를 점검할 때 도구 목록을 읽어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
둘 다 가능해요. 내 컴퓨터에 설치해도 되고, 클라우드 버전에 연결해도 돼요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.0.22)은 39일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, AI Deprecations
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
AI model shutdown dates, replacements, change feed and webhook alerts for 8 providers.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Connect ITM Platform to AI assistants through the Model Context Protocol. The ITM Platform MCP server lets MCP-compatible clients search projects, inspect budgets, summarize portfolio health, create tasks, log risks and issues, and update project details using your ITM Platform permissions.
It works with Claude, VS Code, Cursor, OpenAI Codex, Windsurf, JetBrains AI Assistant, and any other client that supports MCP.
https://api.itmplatform.com/v2/_/mcp/Use the hosted server if your AI client supports remote MCP servers. There is nothing to install: add the URL, sign in with your ITM Platform account, and approve the requested access.
claude mcp add --scope user --transport http itm-platform https://api.itmplatform.com/v2/_/mcp/
For other MCP clients, use this remote URL:
https://api.itmplatform.com/v2/_/mcp/
OAuth is the recommended setup for most users because your AI client never sees your ITM Platform password or API key.
After adding the server, open your AI client, type /mcp where slash commands are supported, select itm-platform, and complete the ITM Platform OAuth login when prompted.
Use the npm package if you prefer to run the server locally, work behind a firewall, or need to connect to a self-hosted ITM Platform instance.
npx @itm-platform/mcp-server
Your MCP client must pass these environment variables to the server:
| Variable | Value |
|---|---|
ITM_API_URL | https://api.itmplatform.com |
ITM_COMPANY | Your company/account slug |
ITM_API_KEY | Your personal API key from ITM Platform |
Example stdio configuration:
{
"mcpServers": { "itm-platform": { "command": "npx", "args": ["@itm-platform/mcp-server"], "env": { "ITM_API_URL": "https://api.itmplatform.com", "ITM_COMPANY": "{your-account}", "ITM_API_KEY": "your-api-key" } } } }
To create an API key, log in to ITM Platform, open My Profile, and generate a key from the API Key section.
After configuring the local server, restart your AI client and use /mcp or the client's MCP server list to confirm itm-platform is connected.
From simple lookups to fully automated cross-system workflows, MCP unlocks progressively more powerful use cases.
Quick lookup -- Ask a question, get an answer:
"What risks are open across my portfolio?"
Multi-step analysis -- The agent chains multiple tools and synthesizes results:
"Review every project ending this quarter. Flag any with budget overruns, open high-impact risks, or task completion below 60%."
Automated bulk actions -- The agent reads, decides, and writes across projects:
"For every project still in Planning status with a start date in the past, update the status to Execution and create a kick-off checklist task assigned to the project manager."
Scheduled intelligence -- An agent runs on a schedule with no human prompt, pulling overdue tasks every Monday and posting a summary to Slack grouped by project manager.
Cross-system orchestration -- Combine ITM Platform's MCP with other MCP servers (GitHub, Slack, Google Calendar, email). When a developer merges a PR, an agent finds the matching ITM Platform task, marks it complete, and if the project hits 100%, drafts a closure summary and emails the program manager.
The MCP server authenticates as you, calls ITM Platform APIs, and returns only the data your ITM Platform account is allowed to access.
The server exposes 47 MCP tools, 6 resources, and 4 prompt templates.
| Tool | What it does |
|---|---|
search_projects | Find projects by name, status, type, or date range |
get_project | Retrieve project details with subcomponent counts and optional budget |
search_services | Find services by name, status, type, or date range |
get_service | Retrieve service details with subcomponent counts and optional budget |
list_project_tasks | List tasks for a project with pagination |
get_task | Retrieve full detail of a single task |
search_tasks | Search tasks across all projects by name, status, assignee, kind, or date range |
get_project_budget | Get budget, actuals, revenue, cost, and margin information |
get_project_purchases | List purchase orders for a project with pagination |
get_project_revenues | List revenue items for a project with pagination |
get_project_risks | List project risks with pagination |
get_project_issues | List project issues with pagination |
get_risk | Retrieve full detail of a single risk, including mitigation and contingency plans |
get_issue | Retrieve full detail of a single issue, including resolution and impact fields |
list_task_progress | List the progress (follow-up) history for a task |
get_task_effort | Get a task's effort breakdown per team member and per professional category; doubles as the task team list |
get_project_progress | Get project progress report: expected, baseline, and actual curves; optionally the full project Seguimiento entries |
list_service_activities | List activities for a service with pagination |
get_service_purchases | List purchase orders for a service with pagination |
get_service_revenues | List revenue items for a service with pagination |
aggregate_portfolio | Group and summarize portfolio data |
query_datamart | Run validated DataMart queries for advanced analysis |
search_users | Find users and team members; returns IsNonLoginUser so non-login users (empty EmailAddress) can be addressed by UserId |
get_user | Retrieve user details |
get_reference_data | Retrieve statuses, types, priorities, and other reference lists |
get_custom_fields | Retrieve the account's custom field definitions for projects, tasks, risks, issues, services, activities, purchases, or revenues |
get_custom_field_options | Retrieve the selectable options of a dropdown custom field |
| Tool | What it does |
|---|---|
create_project | Create a project (Waterfall or Kanban); the project starts with the account default status and the creating user as project manager |
create_task | Add a task, milestone (KindId 1), or summary task (KindId 2); ParentId builds Gantt hierarchy on Waterfall projects; TaskManagers/TaskMembers assign users by username or numeric UserId (the id is the only option for non-login users) |
update_task | Update task fields such as status, dates, kind, and parent; TaskManagers/TaskMembers add assignees by username or numeric UserId (add-only, never removes) |
create_task_progress | Report progress on a task (percentage, assessment, notes) with full side effects |
update_task_progress | Update an existing task progress entry |
update_task_effort | Set the estimated (planned) hours of a task per assigned user; accepted effort and billing data are preserved |
log_time_entry | Log actual worked hours on a task for one user and date; adds to or replaces the day's total, echoing previous and new totals |
create_project_progress | Create a project-level progress (Seguimiento) entry: percentage, assessment, and status description; 100% auto-closes the project |
update_project_progress | Update an existing project progress entry |
create_risk | Log a project risk |
update_risk | Update risk fields such as status, probability, impact, level, and mitigation or contingency plans |
create_issue | Log a project issue with a required issue type and status |
update_issue | Update issue fields such as status, type, and resolution |
update_project | Update project fields such as name, status, dates, and priority |
create_service | Create a service; it starts with the account default status |
update_service | Update service fields such as name, status, dates, and priority |
create_activity | Add an activity to a service (activities form a flat list) |
update_activity | Update activity fields such as status and dates |
bulk_update_task_status | Apply one status to up to 100 tasks of a project in a single call |
bulk_update_activity_status | Apply one status to up to 100 activities of a service in a single call |
Write operations confirm the saved state from the ITM Platform REST API. DataMart-backed search results may take up to 60 seconds to reflect recent writes. Validation failures include the actionable message returned by REST instead of only the HTTP status.
Reads come from DataMart whenever DataMart holds the data (quota-free, portfolio-wide); REST is reserved for data DataMart lacks, single-item authoritative reads (get_task), write readbacks, and reference data. Writes always go to REST. When DataMart gains a dataset, the matching GET tools must migrate to DataMart. Full rule and current routing map: zz_Specifications/progress-history-reads-from-datamart.md.
When the account defines custom fields, each session is enriched with per-account context: the server lists the DataMart customFields keys actually in use in the MCP initialize instructions and in the query_datamart tool description, so agents can read and filter custom field values without prior discovery.
Resources give AI clients read-only context such as DataMart schemas and project calendars. Prompt templates provide guided workflows for common analysis tasks:
| Prompt | What it helps with |
|---|---|
/project_status | Summarize health, tasks, risks, issues, and budget for one project |
/portfolio_overview | Analyze portfolio status, methodology, budget, and delivery patterns |
/team_workload | Review assignments and workload patterns |
/risk_analysis | Assess risk exposure, issues, and budget impact |
The MCP server uses the same identity and permission model as ITM Platform.
| Connection method | Authentication | Best for |
|---|---|---|
| Hosted HTTP | OAuth 2.1 with PKCE | Most users and managed AI clients |
| Local stdio | ITM Platform API key | Local execution, firewalled networks, self-hosted environments |
OAuth sessions use scopes:
| Scope | Allows |
|---|---|
mcp:read | Read-only tools such as search, get, list, aggregate, and query |
mcp:write | Read tools plus create and update tools |
API key sessions use the full permissions of the ITM Platform user who generated the key.
License access:
| License | MCP access |
|---|---|
| Company Admin | Full read and write access |
| Full User | Full read and write access |
| Project Manager | Read and write access scoped to managed projects |
| Team Member | Blocked |
Your AI assistant does not receive your ITM Platform password or API key. Project data is returned to the AI client you choose, so the AI provider's data-handling policy applies to any data it processes.
Use the public docs for client-specific setup:
For any MCP-compatible client, the two connection values are:
| Method | Value
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요