A
licenseB
qualityD
maintenanceAI-native control plane for Kubernetes and GitOps. Provides 30+ tools for service deployment, database provisioning, and log management via natural language.
Last updated
1
1
MIT
Control Plane
by io.cpln
Ownership verified
Server Details
Control Plane (controlplane.com): deploy and operate workloads across AWS, GCP, Azure, and more.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- controlplane-com/ai-plugin
- GitHub Stars
- 9
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.1/5.0
Tool DescriptionsA
Average 4.4/5 across 58 of 58 tools scored. Lowest: 3.3/5.
Server CoherenceA
Disambiguation5/5
Every tool has a clearly distinct purpose. Domain, secret, policy, identity, workload, volumeset, template, and auxiliary tools (metrics, logs, terraform, search) are all well-separated. Even related tools like add_domain_route vs update_domain_route are clearly differentiated by description.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., create_gvc, update_workload, list_resources). However, there are minor deviations like 'expand_volumeset' (expand vs update), 'workload_reveal_secret' (with prefix), and 'convert_to_terraform' (preposition). These are understandable but not perfectly uniform.
Tool Count4/5
With 58 tools, the count is high but justified by the breadth of Control Plane resources and operations. Each tool serves a specific, non-redundant purpose. The number is slightly above the ideal range, but the scope of the platform demands it.
Completeness5/5
The tool surface covers all major lifecycle operations for every resource type: create, read, update, delete, plus specialized actions (expand volumeset, reveal secret, exec into workloads, query metrics/logs, manage templates, export terraform). There are no obvious gaps that would hinder an agent's workflow.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
Nexlayer MCPofficial
Flicense-qualityCmaintenanceAgentic cloud platform with 45+ MCP tools. Deploy any containerized stack, debug live pods (shell, file editing, DB queries), manage custom domains & TLS, push to built-in container registry, scale pods, and manage GPU workloads. The infrastructure layer where AI agents ship software to production.Last updated8- Alicense-qualityBmaintenanceA high-performance control plane for Ollama-based local LLM sessions with background memory consolidation, hybrid cloud planning, and real-time fleet monitoring.Last updated5MIT
- Alicense-qualityCmaintenanceEnables users to deploy a production-grade control plane for agents, providing a spec-compliant MCP server, scoped tool grants, failure-mode detection, and cost governance.Last updatedMIT