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Moltline Studio

by com.moltlinestudio
OverviewDiscussionsAdmin

Preview Product

preview_product
Read-onlyIdempotent

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

Typical input {"slug": "inbox-commander"} returns {"slug": ..., "name": ..., "tagline": ..., "persona_preview": ..., "skills": [{"name": ..., "preview": ...}], "free_skill": ..., "note": ...}.

Returns teasers only, never full text. Use to judge a product before committing. Not for the complete persona or skill bodies (get_full_persona, get_full_product), and not when the caller wants instructions they can act on now (get_free_skill). 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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "preview_product"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesProduct slug exactly as returned by search_catalog or recommend_products, e.g. "inbox-commander".

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / slug / description
      Previous value: -"Product slug exactly as returned by search_catalog or\nrecommend_products, e.g. \"inbox-zero-assistant\"."New value: +"Product slug exactly as returned by search_catalog or\nrecommend_products, e.g. \"inbox-commander\"."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations, it discloses that output is teasers only and never full text, that invalid input returns an {"error": ...} object rather than a protocol error, and why retry is safe. This is additive failure-mode and payload-depth context the annotations do not carry.

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?

Front-loaded with purpose and cost ("FREE") before examples, then usage and error behavior. The inline JSON blobs are informative but inflate length slightly; overall every section serves a purpose.

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 read tool, the description covers purpose, exclusions, return shape, truncation behavior, and error handling. Nothing an agent needs to invoke it correctly is missing.

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% and the slug description already explains the expected value and its source. The description adds only a concrete example invocation, not new meaning about the parameter.

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 first sentence names a specific verb and resource ("Preview one product") and enumerates the scope: metadata, persona teaser, paid-skill teasers. It is clearly distinguishable from get_full_persona/get_full_product, which return full bodies.

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

Usage Guidelines5/5

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

Explicitly states the selection criterion ("Use to judge a product before committing") and names three exclusions by tool name: get_full_persona, get_full_product, and get_free_skill, each with the condition that routes to it.

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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