VHGENGINE

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

list_creator_profiles

Read-only

List current creator-profile versions, newest changed first. Free; read scope.

    Returns {profiles, limit, offset, total}. Each profile carries its immutable
    version, declarations, current secondary-use decisions, consent receipts, and
    unverified-attestation warning. Errors: unauthorized, forbidden, invalid_request,
    rate_limited.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Profile page size, 1-100.
`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
`limit`	No	Page size actually applied.
`total`	No	Profiles owned by this account before paging.
`offset`	No	Offset this page started at.
`profiles`	No	Current profile objects in this page.

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses the free cost, the exact return shape ({profiles, limit, offset, total}), per-profile contents (immutable version, declarations, consent receipts), and possible error types. This adds substantial value over annotations alone.

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 main purpose in the first sentence. Subsequent sentences add distinct value—return structure and errors—without redundancy or 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?

With rich parameter schemas, an output schema, and a readOnly annotation, the description adds sufficient context about ordering, response contents, and error cases. It is fully adequate for an agent to invoke 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 a detailed description. The description only echoes limit/offset in the return shape, adding no new parameter semantics beyond the schema. Baseline 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 clearly states the verb ('List'), the resource ('current creator-profile versions'), and a distinguishing trait ('newest changed first'). This effectively differentiates it from sibling tools like create_creator_profile or get_creator_profile.

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 use ('Free; read scope', listing versions) but does not explicitly name alternatives or exclusions. The appropriate use case is strongly implied rather than explicitly contrasted with siblings, so it misses a 5.

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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