Skip to main content
Glama

chieflab_review_visual_asset

P104 — approve or reject a single visual asset (LinkedIn graphic / X image / landing hero / Product Hunt gallery / carousel slide). USE WHEN a reviewer hits 'Approve' or 'Reject' on a launch image in the Launch Room. Reads the run's stored visualAssets list, updates the matching asset's status (approved | rejected | pending_approval), persists the change to run metadata so future loads + the channel-media render reflect the decision. Companion to chieflab_regenerate_visual_asset (which produces an updated asset shape without persisting).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
runIdYesLaunch run id that produced the asset.
reasonNoOptional. Why rejected (or why approved). Stored in metadata for audit + future brain memory.
assetIdYesThe visual asset id from launchPack.channels[channel].media[*].assetId.
decisionYesapprove | reject. Updates the asset's status field.
workspaceIdNoOptional workspace id.

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that the tool reads the run's visualAssets list, updates the matching asset's status, persists to run metadata, and affects future loads and channel-media render. However, it lacks details on permissions, idempotency, or potential side effects like notifications.

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 a single, well-structured paragraph. It front-loads the core action and context, then explains mechanics and companion tool. Every sentence adds essential information with no redundancy or wasted words.

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

Completeness3/5

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

The tool has 5 parameters with full schema coverage, but no output schema. The description explains the persistence effect but does not describe the tool's return value or response format. For a complete understanding, an agent would benefit from knowing what the tool returns (e.g., success message, updated asset object).

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?

Schema coverage is 100%, so baseline is 3. The description adds value by specifying that assetId comes from launchPack.channels[channel].media[*].assetId, that decision updates the status field, and that reason is stored for audit and brain memory. This context enriches the schema descriptions.

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 action (approve or reject a visual asset), specifies the types of assets (LinkedIn graphic, X image, etc.), and explicitly distinguishes it from the sibling tool chieflab_regenerate_visual_asset by noting the companion writes the decision while regeneration produces an updated shape without persisting.

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 explicit context: 'USE WHEN a reviewer hits Approve or Reject on a launch image in the Launch Room.' It also mentions the companion tool for regeneration. However, it does not explicitly state when not to use this tool or list alternative tools for other actions.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes, e.g., approve_action vs execute_approved_action vs publish_approved_post. However, alias overloading (e.g., chieflab_launch_product and chieflab_get_users_after_build pointing to the same handler) introduces some ambiguity. The detailed descriptions mostly mitigate confusion, but an agent might still struggle to choose between near-identical aliases.

Naming Consistency4/5

Tools predominantly follow a 'chieflab_verb_noun' pattern (e.g., chieflab_approve_action, chieflab_connect_provider). A few exceptions exist (chieflab_help, chieflab_inbox, chieflab_boot) that are single nouns, but these are clearly distinct and the overall consistency is high.

Tool Count3/5

32 tools is on the high side for an MCP server, but the domain of a growth/marketing launch platform naturally requires many operations (launch, approve, execute, measure, iterate, connect providers, etc.). The count is borderline but still manageable; it doesn't reach the 50+ extreme.

Completeness4/5

The tool set covers the full launch lifecycle: create, approve, execute, measure, and iterate. It includes provider connections, manual fallback, brain summary, and work requests. Minor gaps exist (e.g., no explicit tool for deleting a launch or revoking approval), but core workflows are fully supported.

Resources