estimate_vramCalculates GPU VRAM needed for a local GGUF model, including weights, KV cache, and overhead, then suggests how many layers to offload to the GPU.
估算模型显存占用（权重+KV+开销），附带建议的 GPU offload 层数 （返回中的 suggested_gpu_layers 字段）。
| Name | Required | Description | Default |
|---|---|---|---|
| ctx_len | No | ||
| model_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It states what is estimated (weights, KV, overhead) but omits whether the tool is read-only, whether a model must be loaded first, and what side effects, if any, exist.
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?
The description is a single, front-loaded sentence with no filler. It states the core purpose immediately and includes only the most relevant extra detail about the returned suggestion field.
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?
An output schema exists, so return values need not be described in depth. However, with no annotations, 0% parameter description coverage, and no usage guidance, the definition is incomplete for a two-parameter estimation tool, especially regarding ctx_len and model_id semantics.
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?
There are two parameters and schema description coverage is 0%. The description does not explain model_id or ctx_len, nor does it clarify how context length affects the estimate, so it fails to compensate for the missing schema documentation.
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 gives a specific verb (估算/estimate) and resource (模型显存占用/model VRAM usage), and it lists the included components (权重+KV+开销). It does not differentiate this tool from the sibling suggest_gpu_layers, which likely covers a similar GPU-layer-suggestion purpose.
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 or alternatives are provided. The description mentions the returned suggested_gpu_layers field but does not explain when an agent should call estimate_vram instead of suggest_gpu_layers or other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.