MCP for your engineering team
Engineers spend hours every week context-switching between Claude or Cursor and the systems they actually need: Jira, GitHub, Datadog, internal docs, the deploy console. The lookup overhead compounds across the team and quietly takes a real chunk of senior engineering time.
A single MCP layer that puts every internal tool one prompt away from the LLM the engineer is already using. No new app to learn. The agent reads the issue, pulls the deploy log, fetches the doc, drafts the change.
- 01
MCP servers built per system (Jira, GitHub, Datadog, internal wikis, deploy tooling).
- 02
Per-engineer scoping wired into your SSO - the agent sees exactly what the human would.
- 03
Read paths exposed first; write paths gated behind a human-in-the-loop confirmation.
- 04
Audit log captures every tool call, with prompt and response, for security review.
- 05
The same MCPs work in Claude Desktop, Cursor, ChatGPT or any agent your team adopts next.
per engineer, across a team of 30 to 200.
Ranges drawn from production deployments and public enterprise benchmarks. For a specific rupee or dollar figure tailored to your volume, use the calculator below.
Prerequisites for a clean deployment.
- SSO with per-user identity (Okta, Azure AD, Google Workspace)
- Read access to the systems being wrapped
- A platform engineering owner to ship and maintain the servers
- Security sign-off on the scoping and audit model
Put your own numbers on it.
“At 2,000 records a month and a loaded monthly cost of ₹1,50,000 per person, mcp for your engineering team would typically save ₹13 L to ₹17 L a year.”
Range uses this use case’s typical automation rate against the baseline time per task for data-entry work, with your cost per person converted at 160 working hours a month.
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