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Glama

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

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint. The description adds valuable behavioral context: it never raises protocol errors, returns error objects with fix instructions, and is safe to retry. This goes beyond annotations and helps the agent understand error handling and idempotency. No contradiction 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose and includes several pieces of information (example, usage guidance, error handling, safety). It is moderately concise; each sentence adds value. Minor redundancy could be trimmed, but overall it is well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters, no enums, and an output schema, the description is fairly complete. It explains the output shape (slug, skill, instructions) and error handling. It mentions licensing and distinguishes from siblings. It could mention authentication briefly, but it still provides sufficient context for correct invocation.

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%, so the baseline is 3. The description provides a typical input example and explains the output shape, but it does not add new semantic information about the parameters beyond what the schema descriptions already provide. The example is helpful but not sufficient to raise the score.

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 specifies the verb 'Load' and the resource 'one paid skill's complete instructions'. It also distinguishes from sibling tools by explicitly naming get_free_skill and get_full_product, making the purpose specific and 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 provides explicit guidance: 'Use when the caller wants one specific paid skill.' It also states when not to use it: 'Not for the free gateway skill, which get_free_skill returns with no plan, and not for every skill at once (get_full_product).' This clearly differentiates usage from siblings.

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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Glama MCP Gateway

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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: statistical planning (sample_size), description (stats_describe), interval estimation (confidence_interval), citation formatting (format_citation), and product/skill retrieval (list_products, get_free_skill, get_full_skill, get_full_product). No two tools overlap in function, and the descriptions explicitly clarify boundaries.

Naming Consistency3/5

Naming is a mix of verb_noun (list_products, get_free_skill, format_citation) and descriptive noun phrases (confidence_interval, sample_size, stats_describe). While all are readable and use snake_case, the lack of a consistent pattern (e.g., all verbs or all nouns) makes it harder to predict tool names.

Tool Count4/5

At 8 tools, the count is appropriate for the server's scope, which covers statistics, citation formatting, and product retrieval. It is not overburdened, and each tool seems justified. The number is slightly above the minimal threshold but well within a reasonable range.

Completeness2/5

The server's name 'research' suggests broader coverage, but the tool surface has notable gaps. Basic statistical tools like hypothesis tests (t-test, ANOVA), correlation, or proportion analysis are missing. The citation tool is limited to three styles. The product retrieval tools are tied to a specific product line, leaving a weak general research focus.

Resources