Skip to main content
Glama

agy_read_skill

Fetch the complete SKILL.md content of any skill, including its name, instructions, and metadata, to review or debug it.

Instructions

Read a skill's full SKILL.md content.

Return Format

{"name": str, "content": str, "meta": dict}

Examples

agy_read_skill("python-expert")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ctxNo
skill_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
Behavior3/5

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

With empty annotations, the description carries the behavioral disclosure burden. The verb 'Read' implies a read-only, non-destructive operation, and the return format provides useful output expectations. However, it does not disclose error behavior for missing skills, whether it operates on installed skills only, or any side effects. It adds some value but leaves important behavior unstated.

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 compact and well-organized: a one-line purpose statement, a clear return format block, and a concrete example. Every element earns its place and the structure is scannable. No redundant or filler content.

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

Completeness3/5

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

The tool is simple, and the description provides purpose, return format, and an example—adequate for a basic call. But it omits edge-case behavior such as what happens when the skill does not exist, whether skills must already be installed, or how content is encoded. Given the absence of annotations and schema descriptions, these gaps make the definition only minimally complete.

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 description coverage is 0%, so the description must clarify parameters. The example agy_read_skill("python-expert") makes it clear that skill_name is a positional string identifying a skill. However, the optional 'ctx' parameter is completely unexplained, and the description only partially compensates for the lack of schema descriptions.

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 a specific verb and resource: 'Read a skill's full SKILL.md content.' This clearly distinguishes the tool from siblings like agy_list_skills (which lists names), agy_search_skills, and mutation tools like install/delete. An agent can immediately understand the tool's unique function.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives. It does not state conditions such as 'use when you need the full content rather than just the list of skills' or mention exclusions/prerequisites like requiring the skill to be installed. The example only shows how to call it, not when to choose it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.