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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": "curriculum-architect-hs", "skill_name": "unit-planner"} 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, e.g. "curriculum-architect-hs"; must be non-empty.
skill_nameYesExact skill name as listed in that product's "skills" array from list_products, e.g. "unit-planner"; must be non-empty.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (which already note readOnly, idempotent, non-destructive), the description adds critical behavioral details: error handling (returns error JSON, never a protocol error), retry safety, and a concrete example of the return shape. No contradictions with annotations.

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 front-loaded with purpose, provides a concrete example immediately, and every sentence earns its place. Spans 7 sentences but each adds distinct value—purpose, example, selection, exclusions, error handling, safety.

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?

With an output schema present, the description need not detail return values, and it still covers purpose, usage guidance, error behavior, and idempotency. The description is complete for a focused lookup tool with robust annotations and schema.

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% with strong descriptions for both parameters, so baseline is 3. The description adds value by explaining the typical input/output JSON format and connecting the parameter values to list_products output, elevating it above the baseline.

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 verb ('Load'), resource ('one paid skill's complete instructions from a product'), and scope, explicitly distinguishing from siblings like 'get_free_skill' and 'get_full_product'.

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 says when to use ('when the caller wants one specific paid skill'), when not to use ('Not for the free gateway skill... not for every skill at once'), and names alternative tools ('get_free_skill', 'get_full_product').

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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