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One MCP connection. Full Confluence context. No more tab-switching — just ask.
Stop searching through nested spaces and outdated pages. Ask your AI agent to surface documentation, summarize runbooks, find decisions, or compare specs across spaces — without opening a single Confluence tab.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Create pages, update documentation, add labels, and manage spaces — all through natural language. Eliminate the manual effort of maintaining documentation hygiene across dozens of spaces.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Track documentation staleness, missing ownership, and critical page changes automatically. Get notified when key runbooks are modified, spaces go stale, or documentation coverage gaps emerge.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Create pages, update documentation, add labels, and manage spaces — all through natural language. Eliminate the manual effort of maintaining documentation hygiene across dozens of spaces.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
Engineering orgs accumulate thousands of Confluence pages over years. The runbook for a rare but critical failure scenario exists — it was written in 2022 — but finding it under an incident requires 15 minutes of keyword searches, false starts, and guessing which space it lives in. In a live incident, that delay is unacceptable.
Your AI agent searches across all Confluence spaces simultaneously, understands page content semantically, and surfaces the exact runbook with the relevant action steps extracted — in under 30 seconds, regardless of how it was titled or where it lives.
Teams write documentation when a system is built. Two years later, that documentation still ranks high in Confluence search — but it describes an architecture that no longer exists. New engineers follow stale instructions, incidents happen, and the broken process that allowed outdated docs to survive goes unaddressed.
Run automated documentation audits through your AI agent. Identify pages that reference deprecated systems, haven't been touched in 12+ months, and lack an owner. Surface them in a weekly report so the right teams can update or archive them.
A new team member needs to understand how the company's data pipeline works end-to-end. The answer spans the Engineering space, the Data Platform space, the Infrastructure space, and three separate spaces owned by different product teams. Synthesizing this into a coherent understanding requires days of reading, not hours.
Ask your AI agent to trace a concept or system across all Confluence spaces, extract relevant sections, and synthesize a unified explanation. What took days of onboarding reading now takes a two-minute conversation.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Confluence MCP is a Model Context Protocol server that connects your Confluence spaces, pages, and documentation to AI agents like Claude, ChatGPT, and Gemini. You can search, summarize, create, and update Confluence content in natural language — without navigating the Confluence interface manually.
Pages and their full content, spaces and space metadata, labels, page hierarchy (parent-child relationships), comments, attachments, page versions and revision history, and user and permission metadata. The AI can read page content, extract structured information, and surface related pages across your entire Confluence instance.
Yes. Write operations include creating new pages (with or without templates), updating existing page content, adding labels, moving pages between spaces, and managing page restrictions. All changes appear in Confluence's version history exactly like manual edits, and can be undone.
Yes. Improvado's MCP server supports both Confluence Cloud (using the REST API) and Confluence Data Center/Server deployments. For self-hosted instances, you provide your Confluence URL and API credentials during setup.
Efficiently. The MCP server uses Confluence's API with targeted search queries and space filters — it does not index all pages to answer a question. Semantic search is handled via Confluence's CQL (Confluence Query Language), and results are returned in seconds regardless of instance size.
Yes. Improvado stores all API tokens in an encrypted vault certified to SOC 2 Type II. Your AI agent never receives raw credentials — all requests go through Improvado's secure proxy. Access is scoped to the permissions of the API token you configure during setup.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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