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MCP verified MIT Self-run

Mcp Starter Kit

mcp-rathore97aditya-mcp-starter-kit · by rathore97aditya

Templates and install scripts to wire any MCP-compatible AI client (Claude Code, Cursor, Continue.dev, Cline, Windsurf, etc.) to Jenkins, Bitbucket, Atlassian, AWS (CloudWatch/Athena/SQS), MySQL, MS SQL Server.

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Install

$ agentstack add mcp-rathore97aditya-mcp-starter-kit

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No 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.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
4mo ago

Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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 →
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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:

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, run aws sso login and retry once before asking the user."
  • "The SQS MCP defaults to us-east-1 — pass an explicit region= 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.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.