get_custom_model_capabilitiesReturn supported BYOM adapter modes, credential policy, network restrictions, and notes about localhost access from the public remote MCP.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, non-destructive nature, so the description's added value is limited to specifying what capability categories are returned. It does not disclose any behavioral traits such as network dependency, latency, or failure modes beyond the phrase 'from the public remote MCP', though this is a lightweight read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is dense but efficient, with the main action 'Return' front-loaded and the specific capability categories enumerated afterward. No wasted words or redundant restatement of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only metadata query with annotations covering safety, the description is sufficiently complete. It names all the capability categories returned, and the absence of an output schema is acceptable given the clarity of the listed return content.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema description coverage, so no parameter documentation is needed. The description still adds useful context about the content of the returned capabilities, which is the closest analog to parameter semantics here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states an exact verb ('Return') and a concrete resource ('supported BYOM adapter modes, credential policy, network restrictions, and notes about localhost access'). This is clearly distinguished from sibling tools like run_custom_model or validate_custom_model, which perform actions rather than retrieve capability metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is a pre-flight informational tool for checking custom model capabilities, but it never explicitly says when to call it or when not to use alternatives. There is no mention of preferring this over sibling tools or calling it before run_custom_model/validate_custom_model.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly separated by resource and action, but translate_text/localize_text and prepare_compare/prepare_custom_compare have adjacent purposes that could cause an agent to choose one when the other is intended. Descriptions provide enough distinction for a careful model, so the ambiguity is limited.
All 15 tools follow a consistent snake_case verb_noun pattern with verbs like build, detect, get, localize, open, prepare, run, search, transcribe, translate, and validate. There is no mixing of camelCase, vague imperative fragments, or generic action names.
15 tools is at the upper edge of a well-scoped set, and each functional area has dedicated tools. It is slightly heavy because a few pairs like translate/localize and prepare_compare/prepare_custom_compare are close variations, but the count is still reasonable for the server's broad workspace scope.
Core workflows are covered: template search/get/fill, translation and localization, custom model capabilities/validation/run, compare plan preparation, transcription, and entitlement checks. Gaps include no compare-plan execution tool, no template create/update/delete lifecycle, and no persistence for custom model configurations, but agents can work around these.