VHGENGINE

by app.railway.up.engine-production-3bdc

get_estimates

Read-only

The measured/expected wait per generation mode. Free, no auth.

    Size a call before spending. Returns {op, modes:{instant|smart|research:
    {p50_ms, p90_ms, samples, source ("measured" once enough samples, else
    "default"), advice}}}.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`op`	No	Which operation these estimates describe.
`modes`	No	Per mode {p50_ms, p90_ms, samples, source ('measured' once enough samples exist, else 'default'), advice}.

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description adds meaningful context: it's free, requires no auth, and explains the source field's behavior (measured vs default) and advice. This goes beyond the structured annotation without contradicting it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense sentence with the return structure embedded. It front-loads the core purpose ('measured/expected wait per generation mode') and immediately notes free/no-auth. While the nested return format is heavy, every phrase earns its place with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless, read-only endpoint with an output schema, the description delivers everything needed: purpose, usage timing, cost/auth status, and a detailed return-shape. There are no gaps for the intended use case.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the schema provides no semantics. The description compensates by fully describing the return object: op, modes, per-mode p50/p90 timings, sample counts, source, and advice. This gives complete meaning to the tool's output.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: returning measured/expected wait times per generation mode. The phrase 'Size a call before spending' gives a specific, actionable purpose that distinguishes it from cost-related siblings like quote or pricing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool ('before spending') but does not explicitly mention alternatives or exclusions. It implies a pre-call sizing use case, which is sufficient guidance without being exhaustive.

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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TDQS

A4.1/5.0

Disambiguation5/5

Each tool targets a distinct resource and action, e.g., signup vs. delete_account, create_key vs. revoke_key, generate_hooks vs. score_hook. Even similar tools like generate_hooks and generate_hooks_batch are clearly differentiated by single vs. batch operation.

Naming Consistency5/5

All 32 tools use a consistent verb_noun snake_case pattern (e.g., add_credits, create_checkout, revoke_key, list_outcomes) with no mixing of camelCase or other conventions.

Tool Count4/5

32 tools is slightly above the typical 15-tool range, but the domain is broad (account, keys, webhooks, generation, scoring, jobs, outcomes), and each tool has a specific purpose. No tools seem redundant.

Completeness4/5

The tool surface covers most lifecycle operations: CRUD for accounts/keys/webhooks, generation/scoring with batch and async variants, outcomes reporting, and auxiliary tools. Missing explicit delete for hooks (expire automatically) and some update operations, but no critical gaps.

Resources

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