agentic-sdlc-mcp

agentic-sdlc-mcp

Enables AI coding agents to orchestrate the full software development lifecycle on GitHub, including planning, issue creation, code review, security triage, and release readiness checks.

Category
访问服务器

README

agentic-sdlc-mcp

An MCP (Model Context Protocol) Server that acts as an Agentic SDLC Control Plane — helping AI coding agents plan, create, test, review, secure, and release software following GitHub Agentic AI best practices.


What Is This?

agentic-sdlc-mcp is not a simple GitHub API wrapper. It is a SDLC orchestration layer that exposes structured, agent-friendly tools aligned to the full software development lifecycle:

Plan → Create → Test → Review → Optimize → Secure

It is designed to be used by AI coding agents (Claude, GPT-4, Codex, etc.) to:

  • Read repository context before starting work
  • Generate structured SDLC plans
  • Create tracked issue sets
  • Prepare agent-ready work briefs
  • Monitor CI/quality gate status
  • Summarise and review pull requests
  • Triage security alerts
  • Run pre-release readiness checks
  • Generate handoff packets between agents

Safety first: All write operations default to dryRun: true. Destructive or irreversible operations are never silently executed.


Installation

# Clone or copy the project
cd agentic-sdlc-mcp

# Install dependencies
npm install

# Build
npm run build

Environment Variables

Copy .env.example to .env and fill in your values:

cp .env.example .env
Variable Required Description
GITHUB_TOKEN ✅ Yes GitHub PAT or App token
GITHUB_OWNER Optional Default owner (org or user)
GITHUB_REPO Optional Default repository name
SDLC_DEFAULT_BRANCH Optional Default branch (default: main)
TRANSPORT Optional stdio (default) or http
PORT Optional HTTP port when TRANSPORT=http (default: 3000)

Required GitHub Token Scopes

Scope Purpose
repo Read/write issues, PRs, file contents
read:org Read org membership (optional)
security_events Code Scanning alerts
vulnerability_alerts Dependabot alerts
secret_scanning_alerts Secret Scanning alerts

For read-only workflows, repo:read is sufficient for most tools.


MCP Client Configuration

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "agentic-sdlc": {
      "command": "node",
      "args": ["/absolute/path/to/agentic-sdlc-mcp/dist/index.js"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token",
        "GITHUB_OWNER": "your-org",
        "GITHUB_REPO": "your-repo"
      }
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "agentic-sdlc": {
      "command": "node",
      "args": ["/absolute/path/to/agentic-sdlc-mcp/dist/index.js"],
      "env": {
        "GITHUB_TOKEN": "ghp_your_token",
        "GITHUB_OWNER": "your-org",
        "GITHUB_REPO": "your-repo"
      }
    }
  }
}

Kiro CLI / Other MCP Clients

{
  "mcpServers": {
    "agentic-sdlc": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/absolute/path/to/agentic-sdlc-mcp",
      "env": {
        "GITHUB_TOKEN": "ghp_your_token"
      }
    }
  }
}

Tools Reference

repo_context

Read repository metadata, README, package.json, open issues, and open PRs.
Use at the start of every workflow.

plan_from_context

Generate a phase-by-phase SDLC plan (Plan→Create→Test→Review→Optimize→Secure) from a goal and repo context. Template-based — no LLM call needed.

create_issue_set

Batch-create GitHub issues from a plan.
⚠️ dryRun defaults to true — pass dryRun: false to actually create issues.

prepare_work_item

Generate an agent-ready brief for a specific issue: goals, non-goals, acceptance criteria, risks, recommended commands, and a handoff prompt.

quality_gate_status

Read check run and commit status results for a PR or git ref.
Use to verify CI before merging or releasing.

create_pr_summary

Generate a structured PR summary: change overview, affected files, test coverage signals, risks, review checklist, and release notes draft.

review_pr_against_standard

Review a PR against SDLC standards (basic / strict / security-focused).
Returns sorted findings, missing tests, security concerns, and a conclusion.

security_triage

Read Code Scanning, Dependabot, and Secret Scanning alerts, triage them by severity, and recommend fix order.

release_readiness_check

Pre-release assessment: CI status, open bugs, CHANGELOG, and a release checklist + rollback template.

agent_handoff_packet

Generate a compact handoff packet so another agent can continue work without losing context.


Resources

URI Description
sdlc://standards/agentic-sdlc Full Agentic SDLC standard with phases and human gates
sdlc://templates/issue Standard issue template
sdlc://templates/pr-summary Standard PR summary template
sdlc://templates/release-readiness Pre-release checklist template
sdlc://templates/handoff Agent handoff template

dryRun Safety Model

All tools that write to GitHub implement a dryRun parameter:

dryRun Effect
true (default) Preview mode — returns what would be created/changed, makes no GitHub API writes
false Live mode — actually writes to GitHub

The default is always dryRun: true. Agents must explicitly pass dryRun: false to trigger writes. This prevents accidental mutations during exploration or planning phases.


Usage Examples

1. Start a new feature

1. Call repo_context to understand the codebase
2. Call plan_from_context with your feature goal
3. Call create_issue_set (dryRun: true) to preview issues
4. Review the preview, then call create_issue_set (dryRun: false)
5. Call prepare_work_item for each issue before implementation

2. Review a pull request

1. Call create_pr_summary to get a diff overview
2. Call quality_gate_status to check CI
3. Call review_pr_against_standard with standard: "strict"
4. Address findings, then re-check quality_gate_status

3. Pre-release check

1. Call security_triage to check for open alerts
2. Call release_readiness_check on the release branch
3. Fix blocking issues
4. Get human approval before tagging the release

Security Considerations

  • Never commit your GITHUB_TOKEN — use environment variables only
  • All tokens are read at startup; the server never logs them
  • dryRun defaults protect against accidental writes
  • No auto-merge, no force-push, no branch deletion — ever
  • Secret scanning alerts are always rated critical severity
  • The server does not make outbound requests beyond the GitHub API

Development

# Type check
npm run typecheck

# Watch mode
npm run dev

# Build
npm run build

Smoke Test with MCP Inspector

GITHUB_TOKEN=ghp_xxx npx @modelcontextprotocol/inspector node dist/index.js

License

MIT

推荐服务器

Baidu Map

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。

官方
精选
JavaScript
Playwright MCP Server

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。

官方
精选
TypeScript
Magic Component Platform (MCP)

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。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

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。

官方
精选
本地
TypeScript
VeyraX

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。

官方
精选
本地
graphlit-mcp-server

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。

官方
精选
TypeScript
Kagi MCP Server

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。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

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。

官方
精选