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Get Full Skill

get_full_skill
Read-onlyIdempotent

Load one paid skill's complete instructions from a product. PREMIUM (license).

Typical input {"slug": "thesis-advisor", "skill_name": "Outline Builder"} returns {"slug": ..., "skill": ..., "instructions": ""}.

Returns one named skill, selected by skill_name. Use when the caller wants one specific paid skill. Not for the free gateway skill, which get_free_skill returns with no plan, and not for every skill at once (get_full_product). 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 list_products.
skill_nameYesExact skill name as listed in that product's "skills" array from list_products.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

The description adds error handling behavior beyond annotations: 'this tool never raises a protocol error — it returns {"error": ...}'. It also confirms idempotency and read-only nature, which aligns with annotations. No contradiction.

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 concise—three short paragraphs covering purpose, example, usage guidelines, and error handling. Every sentence is necessary and well-structured, with no fluff.

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?

The description covers purpose, usage, parameters, error handling, and retry safety. The output schema likely handles return structure details, so the description is complete for the tool's complexity. An agent has all necessary information to select and invoke the tool correctly.

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?

The input schema already provides 100% coverage with clear descriptions for both parameters. The description adds value with a typical input example and clarifies that skill_name selects one named skill. This justifies moving above the baseline of 3.

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 loads one paid skill's complete instructions from a product. It provides a concrete example and explicitly distinguishes from sibling tools get_free_skill and get_full_product, making the 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?

The description explicitly states when to use: 'Use when the caller wants one specific paid skill.' It also tells when not to use: 'Not for the free gateway skill... and not for every skill at once.' This provides clear guidance on alternatives.

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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