Install
$ agentstack add mcp-rathore97aditya-mcp-starter-kit ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
MCP Starter Kit
Templates and install scripts for wiring up any MCP-compatible AI client to a useful set of Model Context Protocol servers — so your assistant can talk to AWS, Jenkins, Bitbucket, Atlassian, and your databases directly instead of through copy-paste.
Who this is for
Engineers who want their AI coding assistant to actually do work against real systems — read pod logs, look up a Jira ticket, inspect a queue's backlog, check a DB schema — not just chat about code. Wiring each MCP server up by hand is fiddly: every server has its own auth, env vars, and gotchas, and config snippets you find online are usually pinned to one specific AI client. This kit ships an opinionated, client-agnostic bundle of the MCP servers most teams need, plus reference docs written for the AI to read so it knows when to reach for which tool.
After setup (~15 min):
$ claude mcp list
Checking MCP server health…
aws-cloudwatch: uvx awslabs.cloudwatch-mcp-server@latest - ✓ Connected
atlassian: npx -y mcp-remote https://mcp.atlassian.com/v1/mcp - ✓ Connected
jenkins: uvx mcp-jenkins - ✓ Connected
aws-sqs: uvx awslabs.amazon-sns-sqs-mcp-server@latest - ✓ Connected
aws-athena: uvx awslabs.aws-dataprocessing-mcp-server@latest - ✓ Connected
aws-rds: uvx awslabs.aws-api-mcp-server@latest - ✓ Connected
bitbucket: uvx atlassian-bitbucket-mcp@latest - ✓ Connected
mysql-db: mysql-mcp-server - ✓ Connected
mssql-db: mcp-sqlserver - ✓ Connected
(Output shown for Claude Code; equivalent status views exist in Cursor, Continue.dev, Cline, etc. — see [SETUP.md § "Verify"](SETUP.md).)
Works with:
- Claude Code
- Cursor
- Continue.dev
- Cline
- Windsurf
- Zed (via its MCP support)
- VS Code MCP extensions
- Any custom app built with the official MCP SDKs
The MCP server config block ({"mcpServers": {...}}) is a cross-client standard; the per-tool wrapping differs only in where you paste it (see [SETUP.md](SETUP.md) for the per-client mapping).
What you get
A base layer everyone needs, plus independent database modules so you only install what you use.
Base layer
| MCP server | What it does | Backed by | |---|---|---| | jenkins | List jobs, read build console output, trigger builds | mcp-jenkins | | bitbucket | Read PRs, branches, commits, diffs (Bitbucket Cloud) | atlassian-bitbucket-mcp | | atlassian | Jira issues + Confluence pages (OAuth, no token to manage) | mcp-remote → official Atlassian endpoint | | aws-cloudwatch | CloudWatch Logs Insights + metrics + alarms | awslabs.cloudwatch-mcp-server | | aws-athena | Athena SQL + Glue Data Catalog (named here aws-athena, but covers Glue/EMR too) | awslabs.aws-dataprocessing-mcp-server | | aws-sqs | SNS topics + SQS queues | awslabs.amazon-sns-sqs-mcp-server | | aws-rds | Generic AWS CLI runner (named aws-rds for legacy reasons; runs any aws ... command) | awslabs.aws-api-mcp-server |
Database modules (optional, independent)
| Module | Backed by | Use it for | |---|---|---| | databases/mysql/ | npm mysql-mcp-server | Reading MySQL tables / schemas | | databases/mssql/ | npm @bilims/mcp-sqlserver | Reading SQL Server tables / schemas |
Different npm packages, different env-var conventions — shipped as independent modules so you install zero, one, or both.
Repo layout
mcp-starter-kit/
├── README.md ← you are here
├── SETUP.md ← step-by-step base setup + per-client wiring
├── LICENSE ← MIT
├── install.sh ← installs base layer (uvx, AWS CLI, MCP packages)
├── AGENTS.md.template ← drop-in instructions for your AI assistant —
│ copy to your tool's instruction-file location
│ (CLAUDE.md, .cursorrules, AGENTS.md, etc.)
├── templates/
│ ├── aws-config.template ← AWS SSO config skeleton
│ └── mcp-servers.json ← cross-client MCP config block (no DB)
├── databases/
│ ├── mysql/ → README.md + install.sh + mcp-block.json
│ └── mssql/ → README.md + install.sh + mcp-block.json
└── docs/ ← per-MCP reference, written FOR your AI to read
├── jenkins.md
├── bitbucket.md
├── atlassian.md
├── aws-cloudwatch.md
├── aws-athena.md
├── aws-sqs.md
└── aws-rds.md
Why ship docs FOR the AI?
When you invite an AI assistant into your workflow, the limiting factor isn't "can the tool reach the system" — it's "does the assistant know which tool to reach for, and how." The docs/ directory is plain markdown your assistant can read; combined with AGENTS.md.template, it ends up knowing things like:
- "For application logs, use the CloudWatch MCP — don't shell out to
aws logs." - "If an AWS call returns
Token has expired, runaws sso loginand retry once before asking the user." - "The SQS MCP defaults to
us-east-1— pass an explicitregion=if your queues live elsewhere."
These aren't novel insights — they're the kind of operational knowledge a human teammate accumulates after a week. Front-loading it for your AI on day one is what an AGENTS.md / CLAUDE.md / .cursorrules / .clinerules file is for.
Quick start
# 1. Install base
chmod +x install.sh && ./install.sh
# 2. Configure (see SETUP.md for details)
cp templates/aws-config.template ~/.aws/config # then edit
# Edit templates/mcp-servers.json — fill in placeholders
# Wire it into your AI client (per-client steps in SETUP.md)
# 3. Add a database (only if you need one)
bash databases/mysql/install.sh # for MySQL
bash databases/mssql/install.sh # for MS SQL Server
# 4. Verify
# Claude Code: claude mcp list
# Cursor: Settings → Features → MCP Servers (UI shows status)
# others: check your client's MCP server / tool list
Full walkthrough: [SETUP.md](SETUP.md).
License
MIT — see [LICENSE](LICENSE). Use it however you like.
Contributing
PRs welcome. Especially:
- Additional MCP modules (Postgres, Redis, GitHub, GitLab, Kubernetes, …)
- Per-client wiring examples (more clients, Windows install scripts)
- Per-MCP doc improvements
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: rathore97aditya
- Source: rathore97aditya/mcp-starter-kit
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.