The 6 MCPs Every Developer Should Have (And How to Govern Them)
Git repos, email, Slack, meeting transcripts, calendar, docs — these six MCP connections transform developer productivity. Here's what each one unlocks, what risks they carry, and how to govern them properly.
In Part 1 of this series, we made the case: developers without Claude Code + secured MCPs are operating at legacy speed. Now let's get specific. These are the 6 MCP connections that transform a developer from "using AI" to "working with an AI that actually knows your company."
For each, we'll cover what it unlocks, what risks it carries, and exactly how to govern it. Because the goal isn't just connectivity — it's governed connectivity.
The 6 MCPs at a Glance
| # | MCP Connection | What It Connects | Risk Level | Rollout Order |
|---|---|---|---|---|
| 1 | Git Repositories | GitHub, GitLab, Bitbucket | Medium | Start here |
| 2 | Documentation | Confluence, Notion, SharePoint | Low | Week 2 |
| 3 | Calendar | Google Calendar, Outlook | Low | Week 2 |
| 4 | Team Chat | Slack, Microsoft Teams | Medium | Month 2 |
| 5 | Meeting Transcripts | Otter, Fireflies, Copilot | Medium | Month 2 |
| 6 | Gmail, Outlook, Exchange | High | Month 3+ |
Now let's break each one down.
1. Git Repositories — The Foundation
Connects to
GitHub, GitLab, Bitbucket — full repo access, pull requests, issues, commit history, code search.
What it unlocks
- Claude Code navigates your entire codebase, not just the file you have open
- PR reviews with full historical context — "this pattern was introduced in PR #847 to fix the race condition in the order service"
- Cross-repo understanding: how service A calls service B, how the shared library is consumed
- Automated refactoring that actually respects your team's conventions and patterns
- Instant onboarding: new developers ask questions about the codebase and get real answers
Risks
Exposure of proprietary code to AI providers. Developers accessing repos outside their team's scope. IP leakage if MCP traffic routes through third-party infrastructure.
How to govern it
- Scope access per team — backend team sees backend repos, frontend sees frontend. Same tool, different visibility.
- Audit every repo access — which developer's AI accessed which repo, when, and what it retrieved.
- Self-host the MCP server — code never leaves your infrastructure. No data sent to third-party MCP hosts.
- Read-only by default — AI can read code and PRs. Write access (creating PRs, committing) requires separate authorization.
This is the highest-impact, lowest-friction MCP to start with. Most developers already use AI for coding — this just gives the AI actual project context instead of working blind.
2. Documentation — Institutional Knowledge on Tap
Connects to
Confluence, Notion, SharePoint, Google Docs — wikis, runbooks, RFCs, Architecture Decision Records (ADRs), onboarding guides.
What it unlocks
- "How do we deploy to production?" — AI finds the runbook instantly instead of you searching Confluence for 15 minutes
- RFC and ADR context: AI understands why architectural decisions were made, not just what they are
- Onboarding superpower: new developers ask questions about anything and get answers from actual company docs
- Living documentation: AI can flag when code changes contradict existing docs
Risks
Outdated docs leading to wrong AI answers. Access to restricted docs (executive strategy, HR policies, M&A plans). Stale information presented as current truth.
How to govern it
- Mirror existing permissions — if a Confluence space is restricted, the AI can't see it either. Same access model, new interface.
- Freshness indicators — AI warns developers when citing docs older than 6 months. "This runbook was last updated in 2024 — verify before following."
- Role-based visibility — engineering docs for engineering teams. Finance docs stay with finance.
- Content classification — tag sensitive doc spaces as restricted. The governance layer enforces it automatically.
Documentation MCPs have the best risk-to-reward ratio. Low sensitivity (mostly engineering docs), high impact (eliminates the #1 developer complaint: "I can't find anything in Confluence").
3. Calendar — Time Awareness
Connects to
Google Calendar, Outlook Calendar — events, availability, RSVPs, meeting schedules.
What it unlocks
- "When is the team free for a deployment review?" — AI checks calendars, suggests slots
- Deadline awareness: AI factors in upcoming releases, code freezes, and PTO when estimating work
- Meeting prep: AI reviews relevant context before your next meeting
- Sprint capacity: AI knows team availability (vacations, conferences, on-call rotations)
Risks
Low risk overall. Calendar data reveals organizational structure, meeting patterns, and priorities — but this is rarely classified as sensitive data.
How to govern it
- Free/busy vs. full details — configurable per role. Most developers need full event titles; restrict executive calendars to free/busy only.
- Team scope — developers see their own team's calendars, not the entire organization.
- Private events excluded — events marked "private" in the calendar are invisible to the AI.
- Minimal governance overhead — this is a great "starter MCP" for organizations testing the waters.
Calendar is the easiest MCP to approve from a governance perspective. Low data sensitivity, clear use cases, and it makes the other MCPs more useful (AI can correlate meeting times with transcripts, prep before events).
4. Team Chat — Decision History
Connects to
Slack, Microsoft Teams — channel history, threads, reactions, bookmarks. Optionally DMs (with explicit consent).
What it unlocks
- "What did the team decide about the caching strategy?" — AI finds the Slack thread in seconds, not the 20 minutes you'd spend scrolling
- Cross-functional context: product discussions, design decisions, ops incidents — all searchable by AI
- Onboarding acceleration: new hires ask about tribal knowledge, AI surfaces the original discussions
- Decision archaeology: understand why something was built a certain way by finding the original conversation
Risks
Sensitive internal communications. HR discussions. Casual conversations taken out of context. Personal messages in DMs. Off-the-record venting about projects or people.
How to govern it
- Channel-level access control — public engineering channels: accessible. HR channels, leadership channels, random: not accessible. Granular.
- No DMs by default — DM access requires explicit opt-in from both parties. Most orgs keep DMs completely out of scope.
- PII filtering — sensitive content (SSNs, credentials, personal info) stripped before it reaches the AI.
- Full audit trail — every message the AI reads is logged. Complete transparency for compliance teams.
Slack is where decisions live at most companies. Connecting it to the AI is high-impact but requires thoughtful channel-level governance. Start with public engineering channels only, then expand based on comfort.
5. Meeting Transcripts — Decisions, Captured
Connects to
Meeting AI services — Otter.ai, Fireflies.ai, Microsoft Copilot transcripts, Google Meet transcripts, Zoom AI summaries.
What it unlocks
- "What were the action items from Thursday's architecture review?" — instant, accurate answer
- Decisions are never lost in someone's memory or buried in notes nobody reads
- Sprint planning context: AI knows what was discussed, who committed to what, and what's still unresolved
- Pattern recognition: "We've discussed this migration 4 times in the last 2 months without resolving it — here are the blockers mentioned each time"
Risks
Sensitive meeting content — performance reviews, compensation discussions, M&A planning, layoff discussions. Recording consent varies by jurisdiction. Informal comments taken as formal commitments.
How to govern it
- Meeting-type classification — engineering standups and architecture reviews: accessible. HR reviews and board meetings: restricted. Classify at the calendar-event level.
- Participant-based access — only attendees' AI can access the transcript. If you weren't in the room, your AI doesn't get the notes.
- Retention policies — transcripts available to AI for 90 days, then archived. Keeps the context fresh and limits exposure.
- Consent enforcement — AI only accesses transcripts from meetings where all participants consented to recording.
Meeting transcripts are the most underrated MCP. The amount of context lost in meetings that nobody takes notes on is staggering. This MCP turns ephemeral conversations into persistent, searchable organizational memory.
6. Email — The Sensitive One
Connects to
Gmail, Outlook, Exchange — inbox, sent mail, drafts. Read-only by default.
What it unlocks
- Stakeholder context: "What did the client say about the deadline in their last email?" — AI knows without you forwarding the thread
- Draft responses with proper context and tone — AI has the conversation history
- Cross-reference email decisions with code changes and meeting discussions
- Vendor communication tracking: "What did AWS support say about that performance issue last month?"
Risks
This is the most sensitive MCP. Personal emails mixed with work emails. Confidential negotiations. Legal holds. Privileged communications. Salary discussions. Personal health information in benefits emails. Treat this one with the most care.
How to govern it
- Read-only, always — AI can read email for context. It cannot send, draft, or modify emails without explicit user action.
- Folder/label-based scoping — only the "Work" folder or emails with specific labels. "Personal," "Benefits," and "Legal" folders excluded automatically.
- Strict audit trail — every email the AI accesses is logged with full attribution. No exceptions.
- Legal hold awareness — emails under legal hold are automatically excluded from AI access.
- Opt-in only — email MCP is not enabled by default. Each developer explicitly opts in and selects which folders/labels to share.
Email is the last MCP to roll out for good reason. The value is real — but the governance bar is the highest. Get the other five right first. Build organizational confidence. Then tackle email with a proven governance playbook.
Start With One. Scale to Six.
Don't try to roll out all 6 at once. Here's the recommended sequence:
Each MCP you add multiplies the value of the others. Git repos + Docs means the AI understands both the code and the reasoning behind it. Add Calendar and it knows when the deployment window is. Add Chat and it can trace decisions back to their origin. The compound effect is enormous.
The constant across all 6:
The governance layer. Same access policies. Same audit trail. Same cost tracking. Whether it's a Git repo or an email, every MCP connection goes through the same governance controls. Palma.ai provides this governance layer — one platform to manage policies, visibility, and cost across every MCP your developers use.
Continue Reading
This is part 2 of our series on unlocking Claude Code + MCPs for enterprise development teams:
Read More
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