Remote MCP access to persistent cloud agents, including runs, approvals, schedules, workflows, and account data.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ we're steadily working through the catalog.
๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Plori.
The plori-ai/plori MCP server gives an MCP client access to hosted agents running in plori.ai. Each agent has its own persistent machine, disk, tools, and memory, so state can remain available between sessions. The server is intended for driving those agents from an AI client or another MCP-compatible application rather than operating files on the developer's computer.
Available operations span the agent lifecycle and its work. You can list, inspect, create, and delete agents; start runs and retrieve their replies; inspect or cancel active work; and handle questions that require human input. The service also supports deferred runs and multi-step workflows. Account tools expose credit balances, usage, disk information, and the configured third-party connections without returning tokens or client secrets.
The service is hosted and exposes a streamable HTTP MCP endpoint at https://api.plori.ai/mcp. There is no local MCP process to build or launch for clients that support this transport. Interactive clients authenticate through a browser using OAuth 2.1 on first connection. Headless environments can send a plori API key as a bearer token instead.
Runs can be blocking, allowing the client to wait for a result, or fire-and-forget. Workflow operations include reading definitions, editing drafts with compare-and-swap behavior, starting executions, and checking execution status and per-step data. Creating or running agents consumes credits, while list operations, result retrieval, and balance reads are free according to the supplied documentation.
For the plori-ai/plori MCP server, configure a remote HTTP MCP connection with the endpoint https://api.plori.ai/mcp. Cursor provides an add-server flow and Cline has a dedicated installation guide. Claude Code and VS Code examples are also provided in the project documentation, as is a Codex CLI setup that performs OAuth login.
A client that only supports stdio can use the separately published plori-mcp npm wrapper, which forwards requests to the same remote endpoint. That wrapper is not the MCP service itself. For CI or other noninteractive environments, pass an API key in the Authorization: Bearer ... header. Keys are created from Dashboard โ Settings on a registered plori account.
The documented tool groups include:
The agent platform's router selects a model for each task. Workflow execution is a real, billed operation, while account and historical read operations do not consume credits as described in the README.
This connection does not provide the cloud agent with access to the user's local files. Shell commands, files, and disk data belong to the agent's hosted environment. The service requires a registered account for API-key creation, and agent creation or execution can incur credit usage. OAuth access can be revoked in the MCP client's settings, while API keys can be revoked from the plori dashboard.
The repository is an integration front door for the hosted product; the remote MCP implementation is served by plori.ai rather than run from this repository. Clients must support streamable HTTP directly or use an appropriate bridge for stdio-only environments.
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