$0.01 USDC on Base mainnet: check model/configuration fit against bounded StackBench capacity evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| engine | No | ||
| concurrency | No | ||
| max_evidence | No | ||
| quantization | No | ||
| context_length | Yes | ||
| hardware_system | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safe read-only, idempotent, non-destructive profile. The description adds meaningful operational context by disclosing a $0.01 USDC cost on Base mainnet and noting that the evidence base is 'bounded,' which are traits not captured in the structured annotations.
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 compact sentence with the core operation stated after the cost prefix. It is appropriately sized, though the '$0.01 USDC on Base mainnet' opener slightly delays the actual behavior.
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?
Despite having an output schema and safety annotations, the tool has seven parameters, zero schema descriptions, and no guidance on when to use it relative to its siblings. The description leaves the meaning of 'fit,' the role of max_evidence, and the relationship to concurrent/quantization settings unexplained.
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?
Schema description coverage is 0%, so the description must compensate for seven undocumented parameters, but it only refers generically to 'model/configuration fit.' Parameters such as hardware_system, context_length, concurrency, quantization, and max_evidence are not semantically expanded beyond their names and JSON types.
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 a specific operation—'check model/configuration fit against bounded StackBench capacity evidence'—making the core function clear. It doesn't explicitly name a sibling, but the verb 'check' and the focus on 'fit against evidence' distinguish it from generate, predict, recommend, and search.
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 or when-not-to-use guidance is provided, and no sibling tools are referenced. An agent cannot tell from the description whether to choose this over predict_performance or recommend_deployment.
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.
Each tool has a clear, distinct stage in the deployment advisory workflow: searching evidence, checking fit, predicting performance, recommending purchases, and generating launch configs. There is no meaningful overlap between tool purposes.
All tool names follow a consistent verb_noun pattern: check, generate, predict, recommend, and search paired with clear objects. Naming style is uniform and predictable.
Five tools is well-scoped for the advisory domain, covering the full pipeline from evidence search through config generation. Each tool has a distinct role and none feels redundant or missing.
The tool surface covers the complete advisory lifecycle: finding evidence, assessing fit, predicting performance, forming recommendations, and producing launch templates. There are no obvious dead ends or missing operations for the stated purpose.