VHGENGINE

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

get_account

Read-only

Return this account's state + remaining rate-limit budget. Free read.

    `rate_limit` carries {limit, remaining, reset_epoch, reset_at, window_seconds} for
    the per-account window, the same budget REST clients read from X-RateLimit-* headers.
    Pace a fleet off `remaining` instead of discovering the ceiling by taking a
    rate_limited mid-run; this read itself consumes one of those calls. No API key is
    ever echoed back. Errors: unauthorized, rate_limited.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`api_key`	No	API key for this call. Omit to fall back to the Authorization: Bearer / X-API-Key request header (streamable-HTTP only), then the VHGENGINE_API_KEY env var (the stdio default). No key resolvable -> unauthorized.

Output Schema

TableJSON Schema

Name	Required	Description
`name`	No	Display name given at signup.
`tier`	No	owner (unlimited) or standard (ten product requests).
`email`	No	Contact address if one was given at signup.
`credits`	No	Current balance.
`unlimited`	No	true only for the owner tier.
`account_id`	No	Your account id.
`created_at`	No	Account creation time, ISO-8601 UTC.
`rate_limit`	No	{limit, remaining, reset_epoch, reset_at, window_seconds} for the per-account window. Pace off `remaining`; limit 0 means limiting is disabled and remaining/reset are null.
`request_limit`	No	10 for standard; null for owner.
`requests_used`	No	Product requests admitted so far.
`api_key_prefix`	No	Prefix of the key that authenticated this call. Never the key itself.
`requests_remaining`	No	Remaining product requests; null for owner.

TDQS

A4.5/5.0

Behavior5/5

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

The description adds substantial behavioral context beyond the readOnlyHint annotation: it notes that rate_limit mirrors the X-RateLimit-* headers, that the call consumes one unit, that no API key is echoed back, and it lists possible errors. This gives the agent a clear picture of side effects and security behavior.

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

Conciseness5/5

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

The description is compact and front-loaded with the purpose. Each sentence adds value: the budget structure, the pacing guidance, the cost warning, the security note, and the error list. The code block for rate_limit is a concise structured explanation.

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?

Given the output schema exists, the description need not enumerate return fields. It covers errors, rate-limit behavior, and security. The tool is simple with one optional parameter, and the description is complete enough for an agent to invoke correctly without surprises. No further context is needed.

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

Parameters3/5

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

The input schema fully describes the optional api_key parameter with its fallback behavior. The description adds only a note that the API key is never echoed back, which is a behavioral guarantee rather than parameter semantics. With 100% schema coverage, the baseline of 3 is appropriate.

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 states a specific verb and resource: 'Return this account's state + remaining rate-limit budget.' It clearly distinguishes this from sibling read tools like get_usage or get_activity by focusing on the account's own state and rate-limit budget. The first sentence is direct and unambiguous.

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 this tool: to check the rate-limit budget before running a fleet, and it warns that this read itself consumes a rate-limit call. It doesn't explicitly name alternative tools for other purposes, but the guidance is sufficient to understand its typical use case.

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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