Skip to main content
Glama

sculpt_pass_gate

Read-onlyIdempotent

Evaluate a sculpting pass against reference and capture images to decide whether to advance, retry, or stop. Uses semantic review and deterministic scores, blocking ambiguous comparisons.

Instructions

Return advance, retry, or stop. Semantic review controls advancement; deterministic scores supply corrections, while an explicitly ambiguous comparison blocks advancement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
passIdYes
attemptNo
thresholdNo
maxAttemptsNo
compareResultYes
semanticReviewNo
minimumConfidenceNo
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, so the tool's safety profile is known. The description adds insight into the decision mechanism—semantic review has authority, deterministic scores correct, and ambiguity blocks—which is beyond the annotations. Yet it stops short of explaining conditions for retry vs stop or the effect of thresholds and maxAttempts.

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 well-structured sentence that front-loads the primary output ('Return advance, retry, or stop'). It packs meaningful logic without unnecessary verbiage, though its density may reduce immediate comprehensibility.

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

Completeness2/5

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

For a tool with seven parameters, nested objects, and no output schema, this description is far too sparse. It neither describes the inputs' semantics beyond two concepts nor explains return value structure or usage scenarios, leaving significant gaps for an agent.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it only hints at semanticReview and compareResult.ambiguous. Critical parameters such as passId, threshold, maxAttempts, and minimumConfidence are not explained in the description, leaving the agent with little context for how to set them.

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

Purpose4/5

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

The description clearly states the tool returns a decision among advance, retry, or stop, which is a specific action. It further explains the decision logic by referencing semantic review, deterministic scores, and ambiguous comparisons, giving it distinct behavioral framing. However, it does not explicitly differentiate itself from sibling gate tools like sculpt_spec_gate.

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

Usage Guidelines2/5

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

The description gives no explicit guidance on when to use this tool versus alternatives. It implies a gating step in a sculpt pipeline but does not mention sibling tools, prerequisites, or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jonit-dev/threenative-sculpt-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server