Mission

Standardize how AI apps (ChatGPT, Claude, in-house agents) safely act on your systems: files, tickets, code, dashboards, calendars, DBs. One interface, permissioned actions, full audit. Think “USB-C for AI tools.” Model Context Protocol+1

What the MCP layer should do

1. Expose a menu, not the whole kitchen
   Each MCP server publishes a capability menu: tools (actions), resources (readable things), and prompts (guided workflows). The client can only use what’s on the menu. Model Context Protocol
2. Enforce least privilege
   Map identities (SSO/OIDC) to scopes per action: read-only by default; writes go through whitelisted procedures or human approval. All secrets in a vault. Full audit log to SIEM. Model Context Protocol
3. Speak the standard wire protocol
   Use MCP’s transports (JSON-RPC over stdio for local tools, streamable HTTP for remote). Keeps clients interchangeable. Model Context Protocol
4. Be observable and governable
   Emit metrics/traces/logs per call, tag with user/session, and keep data handling aligned with data-classification policy (PII, finance, prod-only).
5. Stay vendor-portable
   Same servers should work with multiple clients: Claude Desktop, ChatGPT/Agents, future Windows integrations. Model Context Protocol+2OpenAI GitHub+2

Minimum viable MCP server catalog (by department)

Collaboration

• Files/Docs (Drive/SharePoint/local FS): search_files, read_doc, create_doc_from_template.
• Chat (Slack/Teams): send_message, start_thread, post_incident_update.
• Calendar: find_free_slot, create_meeting, invite_attendees.

Engineering/IT

• GitHub/GitLab: list_open_prs, create_branch, open_pr, merge_with_checks.
• Jira/ServiceNow: create_ticket, link_ticket_to_pr, transition_status.
• CI/CD: trigger_pipeline_readonly_status, rollback_release_with_approval.
• Observability (Grafana/Splunk): snapshot_dashboard, get_alert_status, fetch_logs(query).
• Kubernetes/Cloud: get_deploy_state, scale_deployment_limited, start_stop_sandbox.
• DB/Warehouse (Postgres/BigQuery/Snowflake): run_parameterized_query (R/O), call_whitelisted_procedure (R/W gated).

Business

• CRM/Support: lookup_account, get_open_cases, create_case_from_summary.
• Finance: yesterday_revenue, refund_status_by_order, export_pnl_snapshot.

All of the above are MCP tools with explicit inputs/outputs, permission scopes, and rate limits. Model Context Protocol

Security model that non-techs can trust

• Allow-list everything (tools, parameters, datasets).
• Human-in-the-loop for destructive actions (e.g., merges, refunds, prod changes) with Slack approval.
• Data minimization: redact PII in logs; pass references not blobs where possible.
• Per-environment scoping (dev/stage/prod).
• Audit: every call stamped with user, tool, args, result; stream to Splunk.

Reference architecture (simple mental model)

[AI clients: ChatGPT, Claude, internal agent]
            │
            ▼
[Gateway/Policy] — OIDC, rate limits, approvals
            │
            ├─► MCP Server: Files/Docs  ─► Google Drive/SharePoint
            ├─► MCP Server: Git         ─► GitHub/GitLab
            ├─► MCP Server: Tickets     ─► Jira/ServiceNow
            ├─► MCP Server: Observability─► Grafana/Splunk APIs
            └─► MCP Server: DB/Warehouse ─► Postgres/Snowflake/BigQuery
                (stdio for local, streamable HTTP for remote)

Transports and capability discovery are defined by the MCP spec; multiple clients can plug in. Model Context Protocol+1

Concrete day-1 use cases (show value fast)

• Daily revenue briefing to Slack: DB server run_query → format → Slack send_message.
• PR triage: Git server list_open_prs + Jira create_ticket for those lacking an issue.
• Incident warm-start: Observability server snapshot_dashboard + Splunk fetch_logs → post to incident channel.
• Customer reply kit: CRM lookup + Docs template to draft a response, routed to support for approval.
  These mirror how MCP is used in real clients (e.g., Claude connecting to files/Slack/Canva). Model Context Protocol+1

SLOs and ops discipline

• Availability: 99.9% per server (monthly).
• P50/P95 latency: <300 ms / <1.5 s per tool call, excluding external API time.
• Change safety: canary new tool versions; contract tests; backward-compatible schema.
• Abuse safety: rate limits per user/tool, payload size caps, allow-listed SQL, and prompt-injection filtering at the gateway.

Rollout plan (4 sprints)

Sprint 1 – Files + Slack + Read-only DB. One daily briefing, one incident playbook.
Sprint 2 – Git + Jira with approvals; Observability snapshots.
Sprint 3 – Calendar + basic CRM lookups; add human-approval workflow.
Sprint 4 – Finance read models; controlled write actions (refund via stored proc).

KPIs

• Tasks automated/week, median cycle-time saved, % actions requiring approval, user adoption per department, incidents prevented (alerts acted), cost per 1k calls.

Why this standard, not ad-hoc bots

• Interoperability across AI clients (works in Claude, ChatGPT, Windows ecosystem). Model Context Protocol+2OpenAI GitHub+2
• Spec-backed: JSON-RPC messaging, formal capability discovery, server/client separation. Model Context Protocol

If you want, I’ll generate a starter MCP server set with: Files+Slack+Postgres (read-only), GitHub, Jira; plus policy templates (scopes, approvals), and a Grafana/Splunk read pack.