$0.00 free search of bounded public StackBench benchmark and failure evidence; use this for evidence-only requests.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior, so the description only needs to add context beyond those flags. It adds useful scope and cost details ('free', 'bounded public StackBench benchmark'), but it does not discuss pagination, result limits, accessibility, or any operational behaviors beyond the simple search.
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 entire description is a single front-loaded sentence containing the key facts: cost, scope, bounded nature, and intended usage. There is no filler, repetition, or irrelevant detail.
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 simple two-parameter, read-only search tool with an existing output schema, the description covers all necessary calling context: what data is searched, that it is free and bounded, and the kind of requests it supports. Nothing critical is missing for an agent to decide whether to use this tool.
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 carries significant responsibility for explaining parameters. It does not explicitly describe 'query' or 'limit' semantics. The search context makes 'query' reasonably inferable, and JSON schema constraints document 'limit', but the description itself adds little direct meaning to the parameters.
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 identifies the specific action ('search'), the resource ('bounded public StackBench benchmark and failure evidence'), and distinguishes its purpose from the sibling tools by framing it for 'evidence-only requests.' This prevents confusion with decision-oriented siblings like recommend_deployment or predict_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 offers a clear positive instruction: 'use this for evidence-only requests.' It does not explicitly list exclusions or name alternative tools, but the use case is clear enough given the sibling tool names and the focused scope described.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.