Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Connect AI assistants to ITM Platform projects, tasks, budgets, risks, and team workload.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
ITM_API_URL, ITM_COMPANY, ITM_API_KEY你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
适用于网页版、桌面版和手机版 Claude:
https://api.itmplatform.com/v2/_/mcp/打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"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"
}
}
}
}在终端中运行这条命令
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-server运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"itmplatform": {
"url": "https://api.itmplatform.com/v2/_/mcp/"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"itmplatform": {
"type": "http",
"url": "https://api.itmplatform.com/v2/_/mcp/"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"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"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
ChatGPT 只能连接互联网上的服务器:
https://api.itmplatform.com/v2/_/mcp/菜单名称可能因套餐和版本不同而略有差异。
你的选择通过 Cookie 保存在这台设备上,无需账户。
ITM_API_URL envITM_COMPANY envITM_API_KEY env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日
注册名称已通过 GitHub 账户 itmplatform 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
尚未检查;我们会轮流检查各个端点
尚未检查;我们会轮流检查各个端点
尚未检查;我们会在检查端点时读取工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(1.0.22)于39天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
同一分类中有:Spec Driven Development, Remote Desktop Commander, Safe Fetch
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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.
MCP server for fetching URLs, safe against SSRF, DNS rebinding, and redirect-to-internal attacks.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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
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