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Documentation Index

Fetch the complete documentation index at: https://docs.llmgrid.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

The MCP Servers section allows you to register, configure, and govern MCP servers that expose tools to models and agents. MCP servers enable models to call external tools in a controlled and auditable way. MCP servers can be scoped by team and access group, monitored for health, and optionally published through the AI Hub for discovery.

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MCP Servers Page

At the top of the page, you can perform key management actions:
  • Add New MCP Server – Register a new MCP server
  • Tabs:
    • All Servers – View all configured MCP servers
    • Connect – Reference integration guidance and examples

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Filters & Context

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Current Team

Use the Current Team selector to filter MCP servers by team context. This determines which servers are visible and manageable.

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Access Group

Use the Access Group selector to filter MCP servers based on access group assignment.

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All Servers

The All Servers tab lists all MCP servers visible in the current context.

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Table Columns

Each server entry includes:
  • Server ID
    Unique identifier for the MCP server.
  • Name
    Human-readable server name.
  • Alias
    Optional alias used for routing or reference.
  • URL
    Base endpoint where the MCP server is hosted.
  • Transport
    Communication transport used by the server (for example, HTTP).
  • Auth Type
    Authentication mechanism required to access the server.
  • Health Status
    Indicates whether the server is reachable and healthy.
  • Access Groups
    Access groups allowed to interact with this server.
  • Created At / Updated At
    Timestamps for lifecycle tracking.
  • Actions
    Edit or manage the server configuration.
When no MCP servers are configured, an empty state message is displayed.

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Add New MCP Server

Select Add New MCP Server to register a server.

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Common Configuration Fields

  • Server Name (required)
    Friendly name used throughout the console and AI Hub.
  • Alias (optional)
    Short identifier that can be referenced programmatically.
  • Server URL (required)
    Base URL where the MCP server is hosted.
  • Transport
    Defines how requests are sent to the server.
  • Auth Type
    Specifies how requests are authenticated.
  • Access Groups
    Restrict which users, teams, or keys can access this server.
After saving, the server becomes available for selection in:
  • Playground
  • Agents
  • Model tool configurations

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Health & Monitoring

Each MCP server tracks a Health Status that reflects connectivity and responsiveness. Use health indicators to:
  • Validate new servers after setup
  • Investigate tool execution issues
  • Monitor availability over time

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Connect Tab

The Connect tab provides implementation guidance for using MCP servers in API requests.

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Limiting Tools to MCP Servers

Requests can restrict which MCP servers or server groups are available by passing a dedicated request header. This enables:
  • Fine-grained tool control per request
  • Safer production usage
  • Reduced tool surface area

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Implementation Example

The Implementation Example section provides a complete request example demonstrating how to invoke tools exposed by MCP servers. What the example shows conceptually:
  • Sending a request to the responses endpoint
  • Declaring MCP tools in the request payload
  • Specifying server labels and URLs
  • Providing required headers for server authentication
  • Enforcing tool usage
This example is meant for reference and should be adapted to your application setup.

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MCP Servers in the AI Hub

MCP servers can be made visible in the AI Hub.

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Publishing MCP Servers

From the AI Hub:
  1. Select MCP Hub
  2. Choose Select MCP Servers to Make Public
  3. Select one or more servers
  4. Confirm publication
Publishing a server only makes it discoverable. All authentication, access control, and guardrails remain enforced.

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Common Use Cases

  • Connect models to internal systems or APIs
  • Enable function-like tool execution
  • Support agent-based workflows
  • Standardize external integrations behind a governed interface
  • Provide discoverability through the AI Hub

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Best Practices

  • Use access groups to restrict server visibility
  • Monitor health status regularly
  • Validate servers in Playground before production use
  • Avoid overexposing tools; publish only approved servers
  • Document server capabilities via AI Hub links
  • Agents – Use MCP servers as tools
  • Models – Attach MCP tools to model workflows
  • Playground – Test MCP tool execution
  • AI Hub – Publish MCP servers for discovery
  • Virtual Keys – Enforce access and limits