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GarphenGate

moltline-mcp

Preview Product

preview_product
Read-onlyIdempotent

Preview a product's metadata, persona teaser, and paid-skill teasers by providing its slug. Get a free, read-only snapshot with retry-safe error handling.

Instructions

Preview one product: metadata, persona teaser, and paid-skill teasers. FREE.

Typical input {"slug": "inbox-zero-assistant"} returns {"slug": ..., "name": ..., "tagline": ..., "persona_preview": ..., "skills": [{"name": ..., "preview": ...}], "free_skill": ..., "note": ...}. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "unknown slug ''"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesProduct slug exactly as returned by search_catalog or recommend_products, e.g. "inbox-zero-assistant".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context: it never raises protocol errors and instead returns a structured error object, shows an example response format, and explains the free nature of the tool. This goes well beyond the structured annotations.

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 front-loaded with the main purpose, followed by a detailed example and error-handling notes. Each sentence contributes value: the example is informative, the error behavior is crucial, and the safety note is practical. Though slightly dense, it remains well-structured and not wasteful.

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?

For a single-parameter tool with an output schema, the description covers the purpose, input source, output example, error behavior, and idempotency/retry guidance. This is sufficient for an agent to select and invoke the tool correctly without ambiguity.

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 schema provides 100% coverage with a descriptive explanation of the 'slug' parameter, including an example. The tool description also includes an example input ('inbox-zero-assistant'), but this is redundant with the schema and adds no new semantic meaning. The schema already carries the full weight.

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 'Preview one product: metadata, persona teaser, and paid-skill teasers' with a specific verb and scope. It distinguishes from siblings by emphasizing the teaser/preview nature and includes an example output that clarifies the tool's purpose.

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 by mentioning that typical input comes from search_catalog or recommend_products, and notes that calls are read-only and idempotent. However, it does not explicitly name alternatives like get_full_product or state when not to use this tool, so guidance is clear but not fully explicit.

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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