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Runlayer Plugin is Runlayer’s unified MCP entrypoint for your organization. Instead of asking users to install and manage many separate MCP servers, Runlayer Plugin gives them one standard connection that can discover and run the tools they already have access to in Runlayer. It is designed for teams that want broad tool access without broad tool sprawl: one install surface for users, one governance layer for admins, and one place for models to discover what they can actually use.

What Runlayer Plugin Includes

Runlayer Plugin is a special Runlayer plugin that is built dynamically for each user. For each request, Runlayer builds that user’s Runlayer Plugin view from:
  • active hosted connectors the user can access through policies
  • accessible Runlayer skills
  • Runlayer platform tools exposed through the Runlayer MCP connector
Runlayer Plugin does not include:
  • local connectors that run on a user’s machine
  • draft or disabled connectors as normal executable tools
  • connectors blocked by policy for that user
  • tools the user could not use through the underlying connector directly
Runlayer Plugin does not grant new permissions. It only exposes capabilities the user already has through Runlayer, and every tool execution still goes through the underlying connector’s normal Runlayer controls.

Why teams use it

Runlayer Plugin is useful when your team wants a default, opinionated way to use MCP safely at scale. Without it, users often end up with fragmented client setup, duplicate connectors, inconsistent tool availability, and unclear security boundaries. Runlayer Plugin fixes that by giving the organization a single approved surface for tool discovery and execution. In practice, that means:
  • admins can standardize the MCP experience across teams
  • users get a simpler setup and fewer things to configure
  • models can search for the right tool when needed instead of carrying every tool definition in context
  • Runlayer policies, approvals, OAuth checks, ToolGuard, and audit logs stay in the loop

How tool discovery works

When a user connects Runlayer Plugin in their AI client, the model sees a compact interface instead of hundreds of raw tools. Runlayer Plugin exposes two main meta-tools:
ToolPurpose
search_toolsSearch across the user’s accessible tools by describing the task.
execute_toolRun a specific tool returned by search_tools.
The normal flow is:
  1. The model calls search_tools with the task it wants to accomplish.
  2. Runlayer Plugin searches across the user’s accessible connectors and returns relevant tool definitions.
  3. The model calls execute_tool with the exact returned tool name and arguments.
  4. Runlayer routes the call to the underlying connector and applies the same policy, security, auth, and audit controls as a direct connector call.

search_tools

Use search_tools before running connector tools. Inputs:
ArgumentRequiredDescription
metaYesWhy the model is searching. Use this for intent/context.
queryNoNatural-language description of the capability needed.
top_kNoNumber of results to return. Default is 5, maximum is 10.
Results include the exact tool name, description, source connector name, and input schema. Example:
{
  "meta": "Need to create a GitHub issue for the deployment bug",
  "query": "create GitHub issue",
  "top_k": 5
}

execute_tool

Use execute_tool after selecting a tool from search_tools results. Inputs:
ArgumentRequiredDescription
metaYesWhy the model is executing this tool.
tool_nameYesExact tool name returned by search_tools.
argumentsYesJSON object matching the returned input schema. Use {} if the tool takes no arguments.
Example:
{
  "meta": "Create the issue the user requested",
  "tool_name": "github_create_issue",
  "arguments": {
    "repo": "acme/app",
    "title": "Deployment bug",
    "body": "Deployment failed after the latest release."
  }
}
If a model guesses a tool name, Runlayer Plugin returns a structured error with suggestions. The model should call search_tools again and use an exact name from the results.

Skills and /runlayer

Runlayer Plugin install packages include a Runlayer skill that teaches supported clients how to use Runlayer Plugin. New install packages expose the MCP server as runlayer-plugin. Older organization installs may still expose the same server as onelayer; those installs continue to work because the proxy URL and plugin identity are unchanged. The skill tells the model to:
  • use Runlayer Plugin as the default entrypoint for tools, plugins, and skills
  • call search_tools before execute_tool
  • avoid guessing tool names
  • route external tool calls through Runlayer so security policies and audit logs remain in effect
In supported clients, this appears as an org-level /runlayer skill so users can ask for Runlayer tools and skills without installing separate prompt packages manually.

Permissions and security

Runlayer Plugin keeps the same security model as direct connector usage.
  • User-scoped results: each user sees only hosted connectors and skills they can access.
  • Policy-aware discovery: policy changes affect what appears in Runlayer Plugin on the next request.
  • Normal execution path: execute_tool delegates to the underlying connector proxy, so PBAC, ToolGuard, OAuth/session checks, and audit logging still apply.
  • OAuth preserved: if a connector needs user authorization, Runlayer Plugin can surface an auth-required tool instead of silently hiding the connector.
  • Partial results: if one connector is slow or temporarily unavailable, Runlayer Plugin can still return tools from other available connectors.

Admin rollout options

Admins have three rollout paths, depending on the target clients.
Rollout pathBest forWhat it configures
Auto SyncLocal desktop/editor clients managed by the Runlayer CLIMCP config entry for Runlayer Plugin
Anthropic org installClaude admin plugin rolloutClaude plugin package with MCP config and skill
OpenAI org installChatGPT organization rolloutChatGPT connector plus uploaded Runlayer skill
Open the Runlayer Plugin page in Runlayer and click Add to Organization. Choose Auto Sync, Anthropic, or OpenAI in the dialog. For Auto Sync setup and MDM deployment, see Automatic Configuration Provisioning.

Anthropic org install

Use this flow when you want to install Runlayer Plugin through Claude admin plugin settings. The downloaded runlayer.zip contains:
  • runlayer/.claude-plugin/plugin.json
  • runlayer/.mcp.json
  • runlayer/skills/runlayer/SKILL.md
1

Download runlayer.zip

In Runlayer, open the Runlayer Plugin page, click Add to Organization, select Anthropic, and click Download zip.
2

Open Claude admin plugin settings

Go to Claude admin plugin settings and click Add plugin.Claude admin plugin settings with the Add plugins button
3

Select Upload a file

Choose Upload a file.Claude add plugins dialog with Upload a file selected
4

Upload the zip

Select runlayer.zip and click Upload.Claude upload plugin dialog with runlayer.zip ready to upload
5

Require access

Change user access to Required.Claude plugin settings with user access set to Required

OpenAI org install

Use this flow when you want Runlayer Plugin available in ChatGPT for your organization. The downloaded runlayer.zip contains:
  • runlayer/SKILL.md
  • runlayer/agents/openai.yaml
  • runlayer/assets/runlayer-logo.svg
1

Open ChatGPT Admin Connectors settings

Go to ChatGPT Admin Connectors, enable developer mode, and click Create in Apps and Connectors.
2

Create the MCP connector

Use the values shown in the Runlayer dialog:
FieldValue
NameRunlayer Plugin
DescriptionUnified MCP server that provides access to all your organization's connectors, skills, plugins, and Runlayer platform tools.
MCP Server URLThe URL shown in the Runlayer dialog
AuthenticationOAuth
Complete the OAuth authorization when prompted.
3

Publish the connector

Publish the connector so it is available to your organization.
4

Review risks and configure confirmation

Review potential risks, mark them as reviewed, and configure action confirmation. For read-only actions, select Allow read actions with no user confirmation, then save.
5

Upload the Runlayer skill

Click Download zip in the Runlayer dialog. Go to chatgpt.com/skills, click + New skill, select Upload from your computer, and upload runlayer.zip.
6

Share the skill

Share the skill and set access to Installed for everyone.

User examples

Once Runlayer Plugin is connected, users can ask their AI client for work in normal language. Examples:
  • “Use Runlayer to find the right GitHub tool and create an issue for this bug.”
  • “Search for Slack tools I can use, then summarize messages about the incident.”
  • “Use /runlayer to list available skills for release management.”
  • “Find the Salesforce tool for reading account details, then look up Acme.”
If the model cannot find a tool, ask it to search Runlayer Plugin first:
Search Runlayer Plugin for the tool you need before executing anything. Use the exact tool name from search results.

Troubleshooting

The user may not have access to any active hosted connectors. Check connector status and policies for that user. Local connectors are not included in Runlayer Plugin.
Runlayer Plugin only includes active hosted connectors visible to the user through policy. Draft, disabled, local, or policy-blocked connectors will not appear as normal executable tools.
The underlying connector requires user OAuth or another user-scoped authorization. Complete the authorization, then search again.
Ask it to call search_tools first. Runlayer Plugin tool names must match the names returned by search results.
Runlayer Plugin is best-effort during discovery. If one connector times out or returns a non-auth error, Runlayer Plugin can still return tools from other available connectors.

Plugins

Learn how dynamic tools work for Runlayer plugins.

Policies

Control which users can access each connector and tool.

Audit Logs

Review Runlayer Plugin and connector activity.