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Manage projects, tasks, and knowledge in ATLAS with deep research support.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "atlas-mcp-server" yet — see the docs or source repo.
Use atlas-mcp-server to create a project called "Launch an enterprise knowledge assistant". Break it into four phases: requirements analysis, architecture design, data integration, and evaluation/release. Create tasks, dependencies, suggested owners, and completion criteria for each phase, and organize key knowledge entries.
A structured project tree with phased tasks, dependencies, suggested ownership, and supporting knowledge records.
Use atlas-mcp-server to review the current status of the "Q3 Product Optimization" project. List in-progress, blocked, and completed tasks, explain blockers, and suggest next priorities based on existing knowledge.
A task status summary including risks, blocker analysis, and actionable priority recommendations.
Use atlas-mcp-server to run deep research on "open-source vector database selection". Compare performance, cost, ecosystem, and operational complexity; save the findings to the knowledge layer and create follow-up validation tasks.
A research summary with structured knowledge entries and a list of follow-up validation tasks.
Connect coding agents to Atlas for context retrieval and work capture.
Automate task breakdown, dependency tracking, and smart project recommendations.
Connect Confluence and Jira to query docs, issues, and project work.
Connect Confluence and Jira for project context, issue management, and documentation.
Query and retrieve data across GitHub, Neo4j, PostgreSQL, and Milvus.
Connect to MongoDB and Atlas to query, manage, and debug data.