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Glama

Get model endpoint configuration

get_model_endpoint_config

Get local MCP and model endpoint configuration for sharing with Copilot or another MCP client. Exposes metadata from discovered local runtimes like Ollama, LM Studio, and LocalAI.

Instructions

Return the local MCP and model endpoint configuration to share with Copilot or another MCP client.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. 'Return' implies a read-only, non-mutating operation, and the description adds context about what configuration is returned. But it does not disclose potential sensitivity of the config, whether any network/remote call is made, or any side effects, so it is only minimally transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, front-loaded sentence efficiently states the action, resource, and purpose. There is no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter read-only tool, the description is mostly sufficient Disclaimer: it lacks an output schema and does not explain what the returned configuration contains beyond 'local MCP and model endpoint configuration.' It also doesn't distinguish itself from describe_model_endpoint, so an agent could benefit from more context about the exact content or format of the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters existing, so there is nothing for the description to clarify. The baseline of 4 is appropriate; the description does not need to add parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'the local MCP and model endpoint configuration' and names the intended use case. It is clear about the resource and action, but it does not explicitly distinguish itself from sibling tools like describe_model_endpoint, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'to share with Copilot or another MCP client' gives a clear usage context. However, the description does not mention when not to use this tool or how it differs from list_local_models and describe_model_endpoint, leaving some selection judgment to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.