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Computer or cloud
Agentic SDLC MCP
Overview
Description from the maker (original English)
Agentic SDLC governance and security controls for AI coding agents working with GitHub.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 85/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Developer tools
- You need to fill in: GITHUB_TOKEN
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
GITHUB_TOKEN,GITHUB_OWNER,GITHUB_REPO,SDLC_DEFAULT_BRANCH
Values marked secret are keys to your account. Never share them or paste them into a chat.
You need
- npmNode.js installed (for the npx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"agentic-sdlc": {
"command": "npx",
"args": [
"-y",
"agentic-sdlc-mcp"
],
"env": {
"GITHUB_TOKEN": "your-value",
"GITHUB_OWNER": "your-value",
"GITHUB_REPO": "your-value",
"SDLC_DEFAULT_BRANCH": "your-value"
}
}
}
}Run this in your terminal
claude mcp add agentic-sdlc --env GITHUB_TOKEN=your-value --env GITHUB_OWNER=your-value --env GITHUB_REPO=your-value --env SDLC_DEFAULT_BRANCH=your-value -- npx -y agentic-sdlc-mcpFill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"agentic-sdlc": {
"command": "npx",
"args": [
"-y",
"agentic-sdlc-mcp"
],
"env": {
"GITHUB_TOKEN": "your-value",
"GITHUB_OWNER": "your-value",
"GITHUB_REPO": "your-value",
"SDLC_DEFAULT_BRANCH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"agentic-sdlc": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"agentic-sdlc-mcp"
],
"env": {
"GITHUB_TOKEN": "your-value",
"GITHUB_OWNER": "your-value",
"GITHUB_REPO": "your-value",
"SDLC_DEFAULT_BRANCH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
- GitHub token with the least privileges required by the tools you call.
GITHUB_OWNERenvOptional default GitHub organization or user name.GITHUB_REPOenvOptional default GitHub repository name.SDLC_DEFAULT_BRANCHenvOptional default branch used when repository metadata is unavailable.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account SakuraCianna, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Developer toolsi
- More stars than 0% of the other 5567 servers in this category
- Published to the registry more recently than 55% of the other 5567 servers in this category
- Basic checks equal to or better than 100% of the other 5567 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Agentic SDLC MCP safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
You need to fill in: GITHUB_TOKEN
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (1.11.0) reached the registry 25 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
What can I use instead?
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Desktop Commander
MCP server for terminal commands, file operations, and process management
Runs on your computer
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
agentic-sdlc-mcp
Governance and evidence controls for AI coding agents working in real GitHub repositories.
Let Claude Code, Cursor, and other Model Context Protocol (MCP) clients work with repository context, review gates, security evidence, and human approval points.
中文 · npm · MCP Registry · Roadmap
agentic-sdlc-mcp is a software development lifecycle (SDLC) governance layer for teams that already let AI coding agents change production repositories. It turns GitHub context, policy, checks, reviews, security alerts, and release signals into 13 workflow-level MCP tools. Twelve tools are read-only. The only GitHub write tool previews changes by default.
Version 1.11.0 also provides agentic-sdlc, a read-only CLI for PR assessment, portable evidence, snapshot comparisons, and acceptance traceability. agentic-sdlc-mcp remains the MCP server entry point. Verify the exact package and release metadata when installing this version.
What changes with this MCP
AI coding agents can create code and pull requests without understanding every repository rule. This server gives the agent bounded context and gives reviewers explicit evidence gaps instead of another free-form summary.
| Concern | Without this MCP | With agentic-sdlc-mcp |
|---|---|---|
| Repository context | The agent starts from the prompt and guesses project conventions | repo_context reads bounded metadata, scripts, policy, issues, pull requests, and agent instructions |
| High-risk work | Authentication, payment, migration, and workflow changes receive a generic plan | prepare_work_item adds risk reasons, defensive requirements, negative scenarios, rollback, and observability |
| Issue planning | A human reformats the plan into GitHub work items | plan_from_context creates structured drafts and create_issue_set previews the exact write |
| Pull request gates | A green continuous integration (CI) badge may be treated as sufficient evidence | quality_gate_status separates checks, reviews, ownership, protection, labels, and missing evidence |
| Secret risk | Scanner names or keyword matches may be accepted without provenance | PR review separates trusted scanner evidence, bounded patch heuristics, and unverified gaps |
| Release and handoff | Readiness depends on free-form status summaries | Release and handoff tools preserve blockers, policy obligations, evidence warnings, and human approval points |
This server does not write code, merge pull requests, force-push, create releases, deploy software, or replace human security review.
How it fits into a production agent workflow
The MCP sits between an AI coding agent and GitHub evidence. Repository changes still happen through the agent's normal development environment, and high-impact decisions remain with your team.
flowchart LRPolicy["Engineering policy and repository rules"] --> MCP["agentic-sdlc-mcp"] Agent["AI coding agent Claude Code · Cursor · MCP client"] --> MCP MCP --> GitHub["GitHub API evidence"] GitHub --> MCP MCP --> Reports["Briefs · plans · gates reviews · release reports"] Reports --> Agent Reports --> Human["Human review and approval"] Agent -. "Code · commits · pull requests" .-> GitHub Human -. "Merge · release · deploy" .-> GitHub
Where it helps
Use the tools as decision support at the points where an autonomous agent would otherwise guess or rely on stale prose.
| Production scenario | Recommended tools | Decision artifact |
|---|---|---|
| Onboard an agent into an unfamiliar repository | repo_context | Repository briefing with scripts, workflows, policy, open work, and known gaps |
| Turn a feature, bug, or security goal into reviewable work | plan_from_context → create_issue_set | Work-type-aware plan, issue drafts, and preview-first GitHub issues |
| Prepare auth, payment, migration, or infrastructure work | prepare_work_item | Risk-aware brief with defensive requirements, negative tests, rollback, and observability |
| Detect dynamic credential construction in a patch | review_pr_against_standard | Patch-local findings for concatenation, interpolation, decoding, aliases, and auth-header sinks |
| Decide whether a pull request is ready for human review | create_pr_summary → quality_gate_status → review_pr_against_standard | Diff summary, merge-gate evidence, findings, blockers, and next actions |
| Audit repository governance | branch_protection_status → workflow_permissions_audit | Branch/ruleset evidence and GitHub Actions least-privilege findings |
| Assess release readiness | security_triage → release_readiness_check | Security-alert summary, CI evidence, release blockers, changelog status, and rollback requirements |
| Transfer work to another agent | agent_handoff_packet | Bounded continuation packet that labels caller assertions and evidence warnings |
| Archive a decision snapshot | sdlc_evidence_packet | Versioned Issue, PR, or release evidence with provenance, freshness, completeness, and a stable content digest |
Review a PR without an agent
Collect a human-readable decision and a JSON snapshot with one command. The assessment combines existing gate and rule-based review evidence, binds it to the PR head/base and policy digest, and preserves blockers, missing evidence, request counts, and collection duration.
Build from a reviewed checkout with Node.js 22 or newer:
npm cinpm run build node dist/cli.js doctor --repo acme/widget node dist/cli.js assess-pr --repo acme/widget --pr 42 --output artifacts/pr-42-first.json --summary artifacts/pr-42-first.md
Inject GITHUB_TOKEN through your process or secret manager before online collection. The CLI reads environment variables only; it does not load a home configuration or .env. doctor is offline unless --online is supplied, and missing configuration returns exit code 2. Once the package is installed, agentic-sdlc runs the same CLI as node dist/cli.js.
| Command | Result |
|---|---|
doctor --repo acme/widget --online --json | Read-only endpoint diagnostics; no credential values in the report |
assess-pr --repo acme/widget --pr 42 --standard strict --json | ready_for_review, blocked, or needs_evidence, with a versioned packet |
evidence --repo acme/widget --subject pr --number 42 --output artifacts/pr-42-evidence.json | Existing Issue, PR, or release evidence packet; use --subject issue --number N or --subject release --ref REF for other subjects |
diff --previous artifacts/pr-42-first.json --current artifacts/pr-42-next.json --json | Added, removed, changed, unchanged, and invalidated evidence; both files must already exist |
trace --packet artifacts/pr-42-evidence.json --acceptance acceptance.md --source openspec --json | Explicit checklist/numbered conditions linked to supplied evidence |
assess-pr and evidence also accept --previous, --acceptance, and optional --mappings for one collection plus local analysis. Mapping files use the strict array schema described in evidence continuity. --json changes stdout; --output always creates a JSON file. Existing files are never overwritten. --summary creates a new Markdown file, or appends when it points to GitHub Actions' exact GITHUB_STEP_SUMMARY path.
Assessments are advisory by default: a completed report returns 0 even when its decision is blocked or needs evidence. assess-pr --enforce returns 1 for blocked, 2 for needs_evidence, and 0 for ready_for_review. Configuration/input/unavailable-source errors return 2; cancellation returns 130. A successful diff or trace command means the analysis completed, not that the evidence or acceptance conditions passed.
Online collection defaults to a 30-second timeout and 120-request budget; supported overrides are --timeout-ms from 1 to 120000 and --max-requests from 2 to 500. doctor has its own bounded probes and accepts the timeout option. File inputs and serialized outputs are limited to 1 MiB.
An unchanged snapshot never inherits an earlier verification. Head/base, policy, producer version, freshness, and completeness changes can require recollection. Checked boxes, agent claims, and ordinary green CI do not prove business acceptance; the CLI has no trusted acceptance producer configured by default. Neither a ready report nor --enforce approves, merges, or deploys a PR.
For MCP review sessions, set SDLC_CONFIG_MODE=envonly to avoid home/.env configuration and SDLC_READ_ONLY=true to reject live Issue creation while retaining previews. Use a token restricted to the intended repositories and read permissions. Client configuration and the portable review skill are in client recipes; the unexecuted small-sample validation plan is in user pilot.
Install from npm
You need Node.js 22 or newer. Node 22 and 24 are tested in GitHub Actions. Run the published package directly from npm:
npx -y agentic-sdlc-mcpFor a global CLI installation:
npm install -g agentic-sdlc-mcpagentic-sdlc-mcp
The default transport is stdio. Most MCP clients should start the package for you instead of running it in a separate terminal.
These npm commands resolve published releases. Pin agentic-sdlc-mcp@1.11.0 to reproduce this version, verify its release/package metadata, and restart any existing MCP connection after an upgrade.
Let a coding agent set it up
Paste this prompt into Codex, Claude Code, or another coding agent:
Use npm install -g agentic-sdlc-mcp to install and configure this MCP globally. Repository: https://github.com/SakuraCianna/agentic-sdlc-mcpConfigure GITHUB_TOKEN and optional repository defaults through the MCP client's secret or environment configuration. On a trusted single-user machine, you may instead run agentic-sdlc-mcp configure or write them to ~/.agentic-sdlc-mcp.json. Never expose the token in chat, logs, or repository files; ask me for missing non-secret details. Then verify the connection with the read-only repo_context tool and summarize its capabilities, required GitHub permissions, and safety boundaries.
Review every command and configuration change before approving it. Node.js 22 or newer is required.
Connect an MCP client
Add the server to Claude Desktop, Cursor, Windsurf, or another MCP client. Inject the GitHub token through the client's secret or environment configuration.
{"mcpServers": { "agent
Only the beginning is shown; read the rest in the repo