Run in-memory benchmarks to measure model speed (ms/batch), throughput, and VRAM footprint. Identify performance bottlenecks for inference optimization.
Runs synthetic in-memory benchmark to test model speed (ms/batch), throughput, and VRAM footprint.
| Name | Required | Description | Default |
|---|---|---|---|
| seq_len | No | ||
| batch_size | No | ||
| iterations | No |
Changes observed during successful MCP inspections.
v4.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that a benchmark is run, without revealing whether the operation is read-only, potentially destructive, resource-intensive, or what the output format is. The agent cannot assess safety or side effects.
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 that communicates the core purpose without redundancy. It is appropriately concise and well-structured, with the action verb and key metrics immediately clear.
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?
With no annotations, no output schema, and no parameter documentation, the description is severely incomplete. It does not inform the agent about parameter meanings, return format, required environment, or side effects, making it impossible to call the tool correctly without external knowledge.
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 schema has 0% description coverage, meaning parameter names alone must convey meaning. The description does not explain seq_len, batch_size, or iterations at all, failing to compensate for the lack of schema documentation. An agent would have to guess based on naming conventions alone, which is inadequate.
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 clearly states the tool runs a synthetic in-memory benchmark measuring speed, throughput, and VRAM footprint. The verb 'runs' and specific measurements differentiate it from siblings like system_diagnostics, which are about system health rather than model performance.
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 provides no guidance on when to use this tool versus alternatives such as system_diagnostics or get_atlas_regions. It does not mention any conditions, prerequisites, or scenarios where it is appropriate, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.