Moltline Research Desk

Get Full Product

get_full_product

Read-onlyIdempotent

Load one product in full: its persona plus every paid skill. PREMIUM (license).

Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "name": ..., "persona": "", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}.

Returns persona plus every skill for one product. Use when the caller wants the whole product. Not for a single skill (get_full_skill) and not for a free look, which list_products and get_free_skill provide with no plan. 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

Name	Required	Description	Default
`slug`	Yes	Product slug exactly as returned by list_products, e.g. "thesis-advisor".

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds 'Every call is read-only and idempotent' and details error handling: 'never raises a protocol error — it returns an error object'. This fully equips the agent with behavioral expectations.

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?

Well-structured and front-loaded: first sentence states purpose, then example, usage guidance, error handling, and safety. Some redundancy exists (purpose repeated in later sentence), but overall efficient. A slightly tighter version could earn 5.

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?

Given a single parameter, rich annotations, an output schema, and multiple sibling tools, the description covers all needed context: scope, boundaries, error model, safety guarantees, and differentiation. No gaps.

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

Parameters5/5

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

Schema coverage is 100% with the slug parameter described. The description adds value with a typical input example, clarifies slug source ('exactly as returned by list_products'), and explains error behavior for invalid slugs, going well beyond the schema.

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 'Load one product in full: its persona plus every paid skill' with a clear verb and resource. It immediately distinguishes itself from siblings like get_full_skill and list_products, making its unique purpose unambiguous.

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 says when to use ('caller wants the whole product') and when not to, naming alternatives ('get_full_skill for a single skill', 'list_products and get_free_skill for a free look'). Also explains error retry behavior, providing complete guidance.

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

A4.7/5.0

Disambiguation5/5

Each tool targets a distinct function: statistics tools for descriptive, inferential, and planning; product tools for listing, free skill, paid skill, and full product; and citation formatting. No overlap or ambiguity.

Naming Consistency4/5

Most tools follow a verb_noun or noun_verb pattern, but there is a mix: some are noun phrases (confidence_interval, sample_size, stats_describe) while others are verb phrases (format_citation, list_products). The naming is clear but not perfectly uniform.

Tool Count5/5

With 8 tools, the server covers two functional domains (statistics and product browsing) without being bloated. Each tool serves a clear purpose, and the count feels well-scoped.

Completeness4/5

The product domain is covered thoroughly (list, free skill, paid skill, full product). The statistics domain is missing hypothesis tests and more advanced analyses, but the included tools cover basic descriptive, confidence intervals, and sample size planning, which is reasonable for a 'research desk' scope.

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