GitRevio MCP Server for AI Intelligence and Engineering Data Platform
GitRevio connects your AI tools directly to engineering intelligence through a powerful engineering data platform. Access real-time insights, delivery analytics, team health metrics, and reports inside Claude, Cursor, Windsurf, ChatGPT, or any MCP client.
What Is GitRevio MCP Server?
GitRevio MCP Server is a secure Model Context Protocol (MCP) integration powered by an engineering data platform. It allows AI assistants to access real-time engineering insights, productivity metrics, delivery trends, onboarding signals, code quality data, and risk forecasts.
All answers are generated from a structured developer analytics infrastructure, ensuring accuracy and traceability.
Try Gitrevio MCPWhy Engineering Teams Need MCP-Based AI Tools
Most AI tools lack context. They write code, summarize text, or answer generic questions. GitRevio changes that by giving your AI tools a real engineering data platform.
Your AI becomes useful because it understands how your engineering org actually works — not just what you type.
Set up in 60 seconds
Access your engineering data platform instantly — one JSON block or one CLI command.
One JSON block or one CLI command
Your AI tool immediately has access to a full engineering data platform backed by GitRevio.
Works in 7 clients on day one
Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose, and Aider — plus ChatGPT via the MCP bridge. Setup guides for each.
API key scoped to your role
ICs see me_* tools. Team leads see team_*. CTOs see exec_*. No shared-access loopholes.
Works With All Major AI Tools
One engineering data platform. Every AI environment.
Install once, use everywhere.
Claude Desktop
Persistent MCP context across every conversation. Your engineering data follows every chat.
Claude Code
CLI-native engineering intelligence in your terminal. Ask about your team, your code, your risks.
Cursor
Real delivery metrics behind every AI code suggestion. Ground Cursor in live team data.
Windsurf
Agentic coding grounded in live delivery data. Cascade agents that know your team's reality.
Cline
Open-source agent with full GitRevio data access. Full tool access, zero extra configuration.
Any MCP Client
ChatGPT, Continue.dev, Goose, Aider and more. If it speaks MCP, GitRevio works inside it.
Secure Role-Based Access Controls
Every API key is scoped to permissions. Your AI only sees what the user is allowed to see — secured through a robust developer analytics infrastructure.
21 structured tools, composable outputs
Not a single "query" endpoint that returns raw text. Each tool is purpose-built, returns structured data, and can be composed by your AI agent.
Sprint velocity, trend, and breakdown by contributor
Composite score with drillable components
New hire ramp-up curve and benchmarks
Risk scores per individual, with contributing signals
What-If analysis for team member changes
Plan vs reality, predictability score, root causes
Who's blocking, wait times, queue depth
Quality trends by repo, team, or file type
Per-PR risk score based on historical patterns
Ownership map, bus factors, knowledge silos
Debt hotspots, accumulation rate, priority ranking
What people are working on, context switching
Cross-team blocking, wait time heat map
AI tool adoption, code quality comparison
Side-by-side team metrics with context
Individual contribution patterns, growth trajectory
Generate a report from any query, schedule delivery
Set up alerts on any metric or pattern
Formal reachability analysis for any file change. Affected files, services, teams.
Lognormal probability distribution for project/epic completion. P50/P75/P90.
Shapley-based decomposition of any metric change into contributing factors.
50+ pre-built prompts for engineering leaders
Don't start from scratch. Our prompt library covers the most common engineering intelligence questions, tested and refined to produce reliable outputs. Every prompt is backed by real data from your engineering data platform.
GROUP A — READ (19 tools)
Direct queries over canonical entities — repos, contributors, teams, PRs, issues, sprints, expertise, AI impact, review queues.
gitrevio_repos_list · gitrevio_team_health · gitrevio_contributors_expertise · gitrevio_org_ai_impact · gitrevio_pr_review_queue · …
GROUP B — SKILLS
Every builtin skill is executable from MCP via gitrevio_skill_run. Custom skills installed by your org appear automatically.
attrition_risk · sprint_autopsy · what_if · board_report · optimal_reviewer · onboarding_cohort · forecast · …
GROUP C — PERSONA DASHBOARDS (17 tools)
One-call dashboards for each role. me_today for ICs, team_standup for EMs, exec_board_snapshot for CTOs.
gitrevio_me_today · gitrevio_team_standup · gitrevio_team_1on1 · gitrevio_portfolio_teams · gitrevio_exec_ai_impact · gitrevio_company_engineering · …
GROUP D — WRITE (10 tools)
Where most competitors stop. Your AI agent can act — schedule a report, register a webhook, install a skill, share a chat.
gitrevio_alert_create · gitrevio_report_schedule · gitrevio_webhook_create · gitrevio_skill_install · gitrevio_skill_run · gitrevio_chat_share · …
Persona-aware filtering
An IC's MCP surface is tactical: today, this week, this PR. A CTO's is strategic: the quarter, the org, the AI ROI. The server filters what's visible based on the API key's role — same protocol, five different surfaces.
| Persona | Primary questions | Tool surface | Horizon |
|---|---|---|---|
| IC (Engineer) | What should I review? Why is my PR stuck? Who should review this? | me_*, release-risk, optimal-reviewer | Day, week, sprint |
| EM | Who's stuck? At risk? Slipping? How's the sprint? | team_*, sprint-autopsy, plan-vs-reality, attrition-risk | Sprint, quarter |
| VP | Which teams are healthy? Cross-team bottlenecks? Hiring gaps? | portfolio_*, what-if, org-health, board-report | Quarter, half-year |
| CTO | Board-ready update? AI ROI? Plan adherence? Strategic risks? | exec_*, Shapley causal analysis, AI impact | Half-year, year, board cycle |
| CEO | Will engineering deliver the strategy? | company_engineering, exec-board-snapshot | Year, fundraise |
Five MCP resources
Beyond tools, the server exposes read-only resources your AI agent can fetch as context.
customer_schema
Data dictionary for the customer's actual data model. Lets the agent reason about which entities and fields exist before it queries.
customer_report_latest
Most recent generated report. Useful for follow-up questions like 'why did velocity drop in last week's digest?'
customer_audit_last_24h
Admin-only audit excerpt of recent MCP and API activity. Surface for compliance reviews and 'who did what' debugging.
api_openapi
Full OpenAPI 3.1 spec for the REST API. Lets the agent compose calls for things the MCP doesn't expose directly.
skills_catalog
Installed skill registry — builtin and custom. Drives discovery; the agent can `gitrevio_skill_run` any skill listed here.
Audit-logged. Rate-limited. SHA-256-keyed.
Every tool call is logged.
Hashed API key, tool name, parameters with PII redacted, status, duration. Surfaced to your admin via GET /api/v1/audit/mcp. Audit events also flow to a durable sink — SIEM export ships Q1 2027.
Rate-limited per session via token bucket.
RPM is configurable per API key and visible in the dashboard. No surprise throttling, no opaque quotas.
API keys SHA-256 hashed at rest.
Plain-text keys are shown once at creation and never recoverable — rotate, don't recover.
# Audit log entry
{
"ts": "2026-05-04T14:12:03Z",
"key_hash": "sha256:9c4f...",
"persona": "team_lead",
"tool": "gitrevio_team_sprint",
"params_redacted": {
"team_id": "tm_backend",
"sprint": "current"
},
"status": "ok",
"duration_ms": 312
}
Why GitRevio is better than generic MCP servers
The gap isn't whether you have an MCP server — it's whether your AI agent can act through it.
| Feature | GitRevio | LinearB | Swarmia | Others |
|---|---|---|---|---|
| MCP tools | 136 (read + write + dashboards + skills) | ~10 read-only | ~20 read-only | None or read-only |
| Persona-aware filtering | 5 personas (IC, EM, VP, CTO, CEO) | — | — | — |
| Pre-built prompts | 50+ | ~10 | — | — |
| Write actions (alerts, reports, skills) | ✓ | — | — | — |
| Audit log of every tool call | ✓ (PII-redacted, SIEM export Q1 2027) | — | — | — |
| Custom skill execution | ✓ via `gitrevio_skill_run` | — | — | — |
| Distribution | npm + PyPI (Docker Q1 2027) | Hosted only | Hosted only | Varies |
| Supported clients | 7 + ChatGPT bridge | Limited | Limited | Varies |
| Pricing | Free – $40/mo (no MCP gate) | Included at $29/mo | €20–39/mo | — |
Also available as an AI Skill for automation
The same skill that runs in MCP runs in chat, reports, alerts, and the API. Author once, deliver everywhere. 49 builtin skills ship today; custom skills land via .gskill packaging.
# Agentic workflow example
every Monday at 8am:
1. gitrevio_exec_board_snapshot()
2. gitrevio_skill_run("attrition_risk")
3. gitrevio_skill_run("plan_vs_reality")
4. gitrevio_report_schedule({)
template: "weekly_digest",
deliver_to: "slack:#eng-leads"
})
before each PR merge:
1. gitrevio.get_release_risk(pr_id)
2. if risk > 0.7: request additional review
3. if risk > 0.9: block merge, notify tech lead
Benefits of GitRevio MCP for engineering teams
For CTOs
For Engineering Managers
For Team Leads
For Developers
Frequently asked questions
Model Context Protocol lets AI tools securely connect to external systems and an engineering data platform.
Because generic AI lacks engineering context. GitRevio connects AI to a real developer analytics infrastructure with live engineering data.
Yes. GitRevio works with ChatGPT and any MCP-compatible client.
Yes. Data access is governed through role-based controls within the engineering data platform.
Usually under 60 seconds.