Everpod
Server Details
Your own AI agent on an always-on cloud computer: Claude Code, Codex and more, or managed OpenClaw.
- Status
- Healthy
- Uptime
- 100.0% over the last 24 hours
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- Repository
- everpod-ai/mcp
- GitHub Stars
- 0
- ChatGPT
- –
- Claude
- –
TDQS
Scored across 3 tools
Each tool has a clearly distinct action: get_pod reads one, list_pods reads many, start_pod creates. No overlap in purpose.
Consistent verb_noun snake_case pattern across all three tools (get_pod, list_pods, start_pod).
At 3 tools it is on the thin side, but the pod lifecycle is narrow enough that these three cover the core actions without bloat.
Covers read-one, read-many, and create, but there is no explicit update, delete, or payment-status transition beyond reading status; notable gaps typical of a minimal lifecycle surface.
Available Tools
3 toolsget_podGet a podARead-onlyInspect
Read one pod by its id: its status and the link its owner needs. This is how to check whether a started pod has been paid for, whether it is ready, and for a developer pod, which of its owner's steps to connect it are done.
Inputs: id (required).
list_podsList podsARead-onlyInspect
List the pods on the owner's Everpod account, oldest first. Each has its kind, its status and the link its owner needs: pay_url while it is awaiting payment, url (the pod's page on everpod.ai) once it is paid.
Inputs: no named parameters.
start_podStart a podAIdempotentInspect
Start a new pod for the owner: an OpenClaw pod, or with kind 'developer' a developer pod. This call charges nothing: the pod stays unpaid until its owner opens pay_url in their browser and pays there. While the account has an unpaid pod, calling again returns that same pod, changed to the name and kind now asked for.
Inputs: name (required), kind, size, login, +1 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Related MCP Connectors
Your own cloud computer run by an AI agent: signed-in browser, its own email, files, long jobs.
Cloud desktops for AI agents: create a Linux computer, see its screen, click, type and run tasks.
Hosting for apps you build with AI. Tested with Claude Code and Codex. Private beta: ask for access.
Persistent cloud workspaces for AI agents: run commands, edit files, use git and a browser.
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Glama MCP Gateway
Add one secure layer between your agents and this server.