Retrieve architecture, quantization, and context length details for a specific LM Studio model by passing its model ID.
Get detailed information about a specific model including architecture, quantization, and context length
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | The ID of the model to get details for |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It partially compensates by disclosing what the call returns (architecture, quantization, context length), which substitutes for the absent output schema. However, it says nothing about behavior on an invalid or missing model_id, read-only safety, or any auth requirement.
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, front-loaded with the verb and resource, with the return contents appended rather than padded out. No filler or redundancy.
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 trivial single-parameter read tool with no annotations and no output schema, the description covers purpose and return content well, which compensates for the missing output schema. It stops short of coverage for edge cases (unknown model_id) and routing versus list_models.
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?
Only one parameter and schema description coverage is 100%, so the schema already defines model_id fully. The description adds no format hints (where the ID comes from, e.g. list_models) beyond the schema, so the baseline 3 applies.
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?
States a specific verb and resource (get detailed information about a specific model) and enumerates the returned content: architecture, quantization, context length. It implicitly distinguishes itself from list_models by targeting 'a specific' model, but never names the siblings (load_model, unload_model, configure_model) that an agent might confuse it with.
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?
No when-to-use guidance and no alternatives named. The description never says to use this when you already have a model_id versus calling list_models first, nor how it relates to load/unload/configure. Usage is inferable from the name alone, which is the definition of minimal guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.