VHGENGINE

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

list_jobs

Read-only

List this account's jobs, newest first (summaries without the result blob).

    Args: limit (1-200, default 50), offset (>=0), api_key. Returns {jobs:[{job_id,
    status, stage, progress_pct, created_at, started_at, finished_at}], limit,
    offset, total}. Errors: unauthorized, rate_limited.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max jobs to return, 1-200. Above the ceiling is an invalid_request, never a silent truncation.
`offset`	No	Number of rows to skip for paging, 0-9223372036854775807. Page with offset += the limit you actually requested; `total` in the response is the unpaged count. The ceiling is SQLite's largest bindable integer: above it the read could only ever have been a 500, so it is a typed invalid_request instead.
`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
`jobs`	No	Summaries: {job_id, status, stage, progress_pct, created_at, started_at, finished_at}. Fetch a result with get_job.
`limit`	No	Page size actually applied.
`total`	No	Jobs matching ignoring paging.
`offset`	No	Offset this page started at.

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true. The description adds meaningful behavior beyond that: ordering (newest first), response fields (job_id, status, progress_pct, etc.), pagination semantics, and error types (unauthorized, rate_limited). This enriches the agent's understanding without contradiction.

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 well-structured with labeled Args, Returns, and Errors sections. Every sentence is informative, no fluff, and the main purpose is front-loaded in the first sentence.

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 presence of output schema and fully detailed parameter descriptions, the description covers all essential aspects: purpose, response format, error cases, and parameter constraints. It is complete enough for an agent to use the tool correctly.

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?

Schema coverage is 100%, with each parameter having detailed descriptions (e.g., limit ceiling behavior, offset paging). The description merely restates parameter names and basic bounds, adding no new meaning beyond what the schema already provides.

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 uses the specific verb 'List' with the resource 'this account's jobs' and adds 'newest first' and 'summaries without the result blob'. This clearly distinguishes it from sibling tools like get_job, which likely retrieves full job details.

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 gives clear context by stating it returns summaries without the result blob, implying use for lightweight listing rather than full job retrieval. It does not explicitly name alternatives or say when not to use, but the context is clear enough.

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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